1. Introduction

1.1. Context: The Role of Quorum Sensing in Synthetic Biology

Quorum sensing (QS) is a microbial communication mechanism that allows bacteria to coordinate gene expression in response to population density. QS operates through the production and detection of autoinducers, which accumulate as microbial populations grow. Once a critical threshold is reached, these autoinducers trigger a collective change in gene expression across the microbial community. QS regulates key microbial behaviors, such as biofilm formation, virulence, and metabolic activity, making it an essential component in both natural environments and synthetic biology [1,2,3,4,5] (Figure 1). In the context of synthetic biology, QS has been engineered to program complex behaviors in microbial systems, enabling precise control over population dynamics, synchronized gene expression, and metabolic processes. This makes QS an invaluable tool for applications such as metabolic engineering, bioproduction, and environmental remediation. [6,7,8,9,10,11,12]

1.1.1. Biofilm Formation

Natural Environment: In natural environments, QS-mediated biofilm formation plays a critical role in microbial survival. Biofilms are structured microbial communities that adhere to surfaces and are protected by a self-produced matrix. This biofilm provides protection against environmental stressors, such as antibiotics or harsh conditions. For instance, in Pseudomonas aeruginosa, the LasI/LasR QS system regulates biofilm development by controlling the production of extracellular matrix components. This allows bacterial populations to coordinate biofilm formation as a defense mechanism or to capture resources efficiently (Figure 1: Natural Environment). [13,14,15,16,17,18,19,20]

Synthetic Systems: In synthetic biology, QS has been harnessed to control biofilm formation in engineered systems. By modifying QS circuits, engineers can regulate biofilm development for industrial applications. In water treatment systems, for example, biofilms are leveraged to break down pollutants, providing a natural solution for environmental remediation. By controlling QS pathways, biofilm formation can be either promoted or inhibited, depending on the needs of the system (Figure 1: Synthetic Systems). For example, in bioengineering applications, synthetic QS circuits are used to regulate the formation of biofilms in bioreactors, where biofilms can enhance stability and productivity in microbial consortia. [21,22,23,24,25,26,27,28]

1.1.2. Gene Regulation

Synchronized Gene Expression: QS systems regulate synchronized gene expression across microbial populations, ensuring that all cells activate or repress genes at the same time based on population density. This coordination is vital in applications where uniform gene expression is necessary for efficient production [29,30,31]. In synthetic biology, synchronized gene expression allows engineers to precisely control when certain genes are turned on, making QS a powerful tool for biomanufacturing and production processes [29,32,33,34,35,36,37,38,39,40]. For example, the LuxI/LuxR system in Vibrio fischeri is used to trigger gene expression, including bioluminescence when the population reaches a critical size (Figure 1: Synchronized Gene Expression) [41,42,43]. In industrial microbial fermentation, QS is employed to synchronize the production of enzymes, ensuring that all cells contribute equally to the production process. This synchronized expression optimizes resource use and increases the yield of desired products [44,45,46,47].

Temporal Control: In addition to synchronizing gene expression, QS systems enable temporal control of gene activation. Synthetic QS systems can be designed to delay the expression of certain genes until the population has reached the optimal size, ensuring that metabolic processes are initiated at the right time. This is particularly valuable in industrial bioprocessing, where the timing of enzyme production can impact overall efficiency and output (Figure 1: Temporal Control). For example, in engineered microbial fermentation systems, QS circuits control the timing of enzyme production to ensure that metabolic processes are activated only when the population density is sufficient for efficient resource use. [38,48,49,50,51,52]

1.1.3. Metabolic Pathway Control

Dynamic Regulation: QS enables dynamic regulation of metabolic pathways by linking gene expression to population density. This means that microbial communities can adjust their metabolic activity in real time, ensuring that resources are allocated efficiently during different growth phases. In synthetic biology, QS is employed to regulate metabolic flux, ensuring that production processes operate smoothly and without waste. For example, in Escherichia coli, QS is used to modulate metabolic pathways based on cell density, improving bio-production outcomes (Figure 1: Dynamic Regulation) [6,46,53,54,55,56]. In synthetic biology applications such as biofuel production, QS-controlled pathways allow microbial populations to adjust their metabolism to maximize the conversion of raw materials into biofuels, improving both efficiency and yield [6,46,49,57,58,59,60].

Optimizing Bio-Production: In engineered systems, QS plays a crucial role in optimizing bio-production by regulating the flow of metabolites through synthetic pathways. By linking QS to the production of valuable compounds such as pharmaceuticals, biofuels, or chemicals, engineers can fine-tune microbial metabolism to minimize waste and maximize yield (Figure 1: Optimizing Bio-Production). For example, in bio-reactors, QS-driven regulation ensures that the production of biofuels is optimized by controlling when and how metabolic pathways are activated, reducing inefficiencies and increasing overall output. [6,44,46,54,61]

1.1.4. Synthetic Consortia

Inter-Species Communication: QS not only coordinates behaviors within a single species but also facilitates inter-species communication in microbial communities. This ability is especially useful in synthetic consortia, where multiple engineered species work together to perform complementary tasks. For example, in environmental remediation, different species within a microbial community can use QS to regulate complementary metabolic activities, enabling more efficient breakdown of pollutants (Figure 1: Inter-Species Communication). QS systems are engineered in multi-species consortia to facilitate coordinated efforts in environmental cleanup projects, where one species degrades a toxic compound, and another species transforms the byproduct into a less harmful substance. [62,63,64,65,66,67,68]

Orchestration of Complex Behaviors: In synthetic biology, QS systems enable the orchestration of complex behaviors across microbial communities, such as dividing labor for resource breakdown or product synthesis. By using QS to regulate the timing and intensity of each species’ contribution, engineers can design highly efficient multi-step processes in biomanufacturing or bioremediation. This division of labor maximizes resource use and ensures that each species specializes in a specific task, contributing to the overall success of the system (Figure 1: Orchestration of Complex Behaviors). For example, in the production of bioplastics or biofuels, QS-based communication systems allow engineered microbial consortia to divide labor, with each species performing a different step in the production pathway. This coordination ensures that all parts of the process are optimized for maximum efficiency. [69,70,71,72,73,74]

Figure 1. Applications of Quorum Sensing in Synthetic Biology.

Figure 1. Applications of Quorum Sensing in Synthetic Biology.

1.2. The Potential of Machine Learning in Optimizing Quorum Sensing Systems

Machine learning (ML) has rapidly emerged as a transformative tool in synthetic biology, particularly in the optimization of quorum sensing (QS) systems. By processing vast amounts of biological data, ML algorithms can identify patterns, predict outcomes, and make data-driven decisions in ways that were previously impossible. The ability of ML to handle complex datasets, detect subtle biological signals, and dynamically adjust to environmental fluctuations makes it a crucial component in advancing QS-based systems. Deep learning, reinforcement learning, and multi-omics integration are some of the advanced ML techniques that are now being used to optimize QS circuits for a variety of applications in bio-production, metabolic engineering, and precision medicine. [75,76,77,78,79,80,81]

ML algorithms such as deep neural networks are capable of processing multi-dimensional biological data, allowing them to recognize QS signal patterns from autoinducer molecules such as AHLs and AI-2. This is particularly valuable in complex environments, such as mixed-species consortia, where QS signaling can be difficult to interpret due to overlapping signals. By using pattern recognition algorithms, ML can differentiate meaningful QS signals from background noise, ensuring accurate and reliable communication between microbes. In addition, reinforcement learning (RL) is increasingly being applied to develop self-optimizing QS circuits that learn from environmental feedback, continuously refining gene regulation and metabolic activities based on system performance. [75,80,82,83,84,85,86,87,88]

The Need for Adaptive Systems: Traditional QS systems, while effective in controlled environments, are inherently static. They rely on pre-programmed thresholds for gene expression, which limits their ability to adapt to real-time environmental changes. For example, a QS circuit designed to activate gene expression when the microbial population reaches a certain density may not perform optimally if external factors such as pH, temperature, or nutrient levels shift unpredictably. This lack of adaptability can result in suboptimal performance in complex, fluctuating environments such as industrial bioreactors or clinical settings. [89,90,91,92,93,94,95]

Integrating ML into QS systems addresses these limitations by creating adaptive systems that can adjust to environmental signals in real time. ML algorithms continuously monitor external conditions and use this data to modify gene circuits dynamically. This feedback-driven adaptability allows QS systems to optimize metabolic processes and gene expression without human intervention [96,97,98,99,100,101,102,103,104]. For instance, in industrial applications, ML can enable QS-based microbial consortia to adjust their behavior in response to fluctuations in nutrient availability, maintaining high productivity and efficiency. Similarly, in therapeutic settings, ML-enhanced QS systems can tailor the production of therapeutic compounds based on the changing conditions in a patient’s body, improving treatment precision and minimizing side effects. [96,97,98,99,100,101,102,103,104]

In bio-manufacturing, ML-based QS systems offer significant improvements in process efficiency by allowing microbes to self-regulate their metabolic pathways in response to external stressors. By predicting environmental shifts, ML-driven QS systems can make preemptive adjustments to gene expression, ensuring that microbes are optimally configured to maintain high productivity even under adverse conditions. This ability to forecast and respond to environmental changes is a major advancement over traditional QS circuits, which are limited by static feedback loops [67,105,106,107,108,109,110,111]. Figure 2 illustrates how ML enables real-time pattern detection, predictive modeling, and adaptive control in QS systems, showcasing its potential to transform how we design and operate synthetic biological systems.

Pattern Recognition: One of the most significant contributions of ML to QS systems is its ability to recognize patterns in QS signals and environmental data. QS systems rely on autoinducer molecules such as AHLs and AI-2 to communicate population density and regulate gene expression accordingly. However, these signals can be difficult to interpret in complex environments where multiple species are present, or where external noise may interfere with signal detection. ML excels in filtering out noise and identifying the specific QS signals that matter, allowing for more precise control of microbial behavior. [82,112,113,114,115,116,117]

• Recognizing QS Signals: ML algorithms can detect subtle variations in autoinducer signals within microbial populations, allowing for more accurate regulation of behaviors like biofilm formation or metabolic control. For example, in Pseudomonas aeruginosa, ML can identify changes in AHL concentration, enabling dynamic control of biofilm growth. [31,118,119]
• Signal-to-Noise Filtering: In multi-species microbial consortia, where overlapping QS signals can cause interference, ML algorithms are capable of distinguishing relevant signals from background noise. This improves the reliability of QS circuits in mixed microbial environments, such as those used in environmental remediation or industrial production. [66,82,120,121,122,123]
• Real-Time Pattern Detection: ML allows QS systems to detect and respond to real-time changes in the microbial environment. For instance, in bio-reactors, ML algorithms can continuously monitor QS signals and make dynamic adjustments to gene regulation or enzyme production as cell density changes. [124,125,126,127,128,129,130,131]

Predictive Modeling: Another powerful application of ML in QS systems is predictive modeling. By analyzing historical and real-time data, ML models can predict the optimal timing and conditions for gene expression, enabling QS circuits to preemptively adjust to changing environmental factors. This capability is particularly useful in industrial and therapeutic settings, where optimizing the timing of gene expression can improve both productivity and precision. [132,133,134,135,136,137,138]

• Predicting Optimal Gene Expression: ML models can forecast the best moments to trigger gene expression in QS systems based on past data and environmental conditions. This ensures that key genes are activated when the population density is optimal for enzyme production or biofuel synthesis, increasing overall efficiency. [139,140,141,142,143,144,145]
• Data-Driven Predictions: By integrating data from various biological layers—such as genomics, proteomics, and metabolomics—ML can provide holistic predictions of how microbial populations will behave in response to environmental changes. This is particularly valuable in bio-manufacturing, where ML-driven models can predict how cells will respond to nutrient fluctuations or temperature shifts, allowing for real-time adjustments to QS circuits. [146,147,148,149,150,151,152]
• Forecasting Environmental Changes: ML systems can also forecast environmental changes, such as shifts in pH or temperature, and adjust QS-regulated pathways in advance. For example, ML algorithms can predict an upcoming temperature fluctuation in a bio-reactor and modify metabolic activity in microbes to maintain stability and optimize production. [153,154,155,156,157,158,159]

Adaptive Control: The integration of ML into QS systems facilitates adaptive control, allowing for continuous, real-time adjustments to gene circuits in response to environmental signals. This creates a self-regulating system that can maintain optimal performance even in fluctuating conditions. The ability of ML to create dynamic feedback loops ensures that QS systems can adjust their behavior autonomously, without requiring external intervention. [160,161,162,163]

• Real-Time System Adjustments: ML-driven QS systems can make real-time adjustments to gene expression, metabolic activity, or biofilm formation based on environmental feedback. In bio-reactors, for instance, ML models can continuously optimize enzyme production based on real-time data, ensuring that production remains efficient even as conditions change. [49,106,164,165,166]
• Dynamic Feedback Loops: ML enables the creation of dynamic feedback loops where changes in QS signals trigger modifications in gene expression, which are then monitored and further refined by the ML system. This ensures that QS-regulated behaviors, such as metabolic pathway control, remain efficient under varying conditions. [78,167,168,169,170,171,172]
• Self-Regulation: One of the most promising aspects of integrating ML with QS systems is the potential for self-regulation. ML-driven synthetic organisms can autonomously adjust their metabolic pathways in response to environmental fluctuations, optimizing processes like biofuel production without the need for human intervention. This is particularly valuable in large-scale industrial applications where maintaining optimal performance requires continuous monitoring and adjustment. [46,60,106,173,174,175]

This review aims to explore the intersection of QS and ML, emphasizing their hybrid potential to develop self-regulating synthetic systems. The integration of ML into QS circuits transforms static networks into dynamic, adaptive systems capable of real-time, autonomous responses to environmental changes. This approach offers unprecedented control over microbial behavior, gene expression, and metabolic processes, which holds great promise for precision medicine—where engineered microbes could adjust therapeutic outputs based on patient-specific conditions—and industrial biotechnology, where adaptive systems could enhance production efficiency under fluctuating conditions. ML enables advanced pattern recognition, predictive modeling, and adaptive control, significantly improving the efficiency, precision, and scalability of QS-based systems. Furthermore, this review critically examines the technical and ethical challenges of deploying these technologies, including data complexity, safety concerns, and regulatory oversight. By providing a comprehensive analysis, this review highlights the transformative potential of ML-enhanced QS systems while addressing the hurdles that must be overcome to realize more autonomous, self-regulating synthetic organisms.

Figure 2. Machine Learning in Synthetic Biology.

Figure 2. Machine Learning in Synthetic Biology.

2. Mechanisms of Quorum Sensing in Synthetic Biology

2.1. Biological Basis of Quorum Sensing

Molecular Mechanisms: At the core of quorum sensing (QS) are autoinducers—small signaling molecules produced by microbial cells and released into their environment. As the population density increases, the concentration of these autoinducers accumulates. Once the concentration crosses a threshold, these molecules bind to specific receptors, triggering a signaling cascade that leads to coordinated gene expression across the microbial community. The two most studied types of QS systems involve N-acyl homoserine lactones (AHLs) in Gram-negative bacteria and autoinducing peptides (AIPs) in Gram-positive bacteria. Additionally, QS systems, such as those utilizing AI-2, are found across multi-species microbial communities, facilitating interspecies communication [3,4,5,10,28,176,177,178,179,180]. Table 1 summarizes the key QS systems, the species that employ them, the autoinducer molecules involved, and their corresponding processes and applications in synthetic biology.

AHL-based QS in Gram-negative bacteria: One of the most studied QS systems is the LuxI/LuxR system in Vibrio fischeri (Table 1), where the autoinducer N-acyl homoserine lactone (AHL) is synthesized by LuxI and diffuses freely across the cell membrane. When AHL reaches a critical concentration, it binds to the receptor LuxR, which activates the transcription of target genes, such as those controlling bioluminescence. In synthetic biology, this system has been repurposed for synchronized gene expression and bioluminescence signaling in engineered microbial populations [3,181,182,183,184,185,186,187].

Another well-known QS system is the LasI/LasR system in Pseudomonas aeruginosa (Table 1), where the autoinducer N-3-oxo-dodecanoyl homoserine lactone regulates biofilm formation, toxin production, and motility. This system is frequently studied for its role in virulence and has been adapted for anti-biofilm strategies in medical and industrial settings, where biofilm disruption is critical. In synthetic biology, biofilm engineering and coordination in microbial consortia are typical applications of this system. [188,189,190,191,192,193,194]

In Gram-positive bacteria, the AgrC/AgrA system in Staphylococcus aureus uses autoinducing peptides (AIPs) to regulate the production of virulence factors, including toxins and biofilm development (Table 1). This system is vital for controlling pathogenicity in medical contexts, and its manipulation has been explored for QS-based control of virulence and disruption of biofilms in medical devices. [195,196,197,198,199,200,201,202,203]

In Bacillus subtilis, the ComX/ComP system utilizes competence-stimulating peptides (CSP) to regulate sporulation and competence development—processes critical for survival under harsh environmental conditions (Table 1). In synthetic biology, this system is used to manage synchronized behaviors like sporulation in microbial factories, enabling improved resource management and nutrient regulation in engineered systems. [204,205,206,207,208,209,210,211,212]

The AI-2 system is unique in that it operates across multiple species, enabling inter-species communication (Table 1). This furanosyl borate diester-based system is involved in regulating virulence and facilitating cross-species communication in microbial consortia. In synthetic biology, AI-2 is leveraged for creating inter-species gene circuits in microbial factories, where different species coordinate tasks like metabolic production and resource management. [213,214,215,216,217,218,219,220,221]

Table 1 provides an overview of these QS systems, their respective microbial species, autoinducers, controlled processes, and applications in synthetic biology, illustrating the versatility of QS as a tool for engineering population-level behaviors in microbes.

Table 1. Key Quorum Sensing Systems in Microbial Species.

Table 1. Key Quorum Sensing Systems in Microbial Species.

QS System	Microbial Species	Autoinducer Molecule	Controlled Processes	Synthetic Applications
LuxI/LuxR	Vibrio fischeri	N-acyl homoserine lactone (AHL)	Bioluminescence, Virulence	Bioluminescence signaling, synchronized gene expression in engineered systems
LasI/LasR	Pseudomonas aeruginosa	N-3-oxo-dodecanoyl homoserine lactone	Biofilm formation, Toxin production, Motility	Anti-biofilm strategies, biofilm engineering, coordination in microbial consortia
AgrC/AgrA	Staphylococcus aureus	Autoinducing peptide (AIP)	Toxin production, Virulence, Biofilm development	QS-based control of virulence in pathogens, disruption of biofilms in medical devices
ComX/ComP	Bacillus subtilis	Competence-stimulating peptide (CSP)	Sporulation, Competence development	Synchronized sporulation in microbial factories, nutrient regulation in engineered systems
AI-2	Multi-species (e.g., Escherichia coli)	Furanosyl borate diester	Inter-species communication, Virulence regulation	Cross-species synthetic consortia, inter-species gene circuits for microbial factories

Role in Synthetic Biology: Quorum sensing has become an essential mechanism in synthetic biology for controlling population-level behaviors. By leveraging QS systems, synthetic biologists can engineer microbial communities to perform complex, synchronized tasks. For instance, the LuxI/LuxR system is often used for synchronized gene expression in engineered systems, where bioluminescence signaling serves as a visual cue for successful coordination across microbial populations. Similarly, the LasI/LasR system has been applied to develop anti-biofilm strategies in environments prone to microbial contamination, such as medical devices and industrial pipelines. [2,6,7,44,72,179,222,223,224]

Moreover, AI-2, with its ability to mediate inter-species communication, is being explored in the design of synthetic consortia where multiple species work together in a coordinated fashion. These consortia are used in bioproduction systems, where each species contributes to a different part of the production process, allowing for increased efficiency and division of labor. The potential to control complex behaviors such as sporulation and competence development in engineered systems, as seen with the ComX/ComP system in Bacillus subtilis, further illustrates the versatility of QS for regulating both simple and complex cellular processes in synthetic environments. [65,74,219,225,226,227,228,229,230]

2.2. Challenges in Traditional Quorum Sensing Systems

Inflexibility in Dynamic Environments: Traditional quorum sensing (QS) systems, despite their effectiveness in controlled environments, face significant limitations when applied to dynamic and unpredictable environments. These systems are designed to operate based on pre-programmed thresholds—once the concentration of autoinducers surpasses a critical level, QS circuits trigger specific gene expression pathways. However, this rigid threshold-based activation makes traditional QS systems inflexible, as they cannot adjust to rapid or unexpected changes in environmental conditions. For instance, in a bioreactor where temperature, pH, or nutrient availability might change unpredictably, a pre-programmed QS system will continue to execute its gene expression program regardless of whether the new conditions support optimal function. This leads to a loss of control over gene expression, resulting in process inefficiency or system failure in industrial or clinical applications. [25,93,231,232,233,234]

The primary challenge arises from the inability of traditional QS systems to integrate real-time environmental feedback. Once activated, these systems follow a set pathway without the ability to halt or modify the response based on changing conditions. For example, in the production of biofuels, if a QS system triggers enzyme production when the nutrient supply is insufficient, the system wastes energy and resources, reducing overall efficiency. This inflexibility can be especially problematic in industrial applications where external variables such as oxygen levels, nutrient availability, or the presence of toxins can fluctuate rapidly, demanding real-time system adjustments [235,236,237,238,239,240]. The Table 2 provides an overview of these challenges and potential solutions, emphasizing the need for real-time environmental integration to enhance system performance.

Table 2. Challenges in Traditional Quorum Sensing Systems.

Table 2. Challenges in Traditional Quorum Sensing Systems.

Challenge	Description	Impact on Synthetic Systems	Potential Solutions
Environmental Variability	Traditional QS systems cannot adapt to rapid or unpredictable environmental changes	Leads to loss of control over gene expression and process inefficiency	Integration of real-time environmental sensors with QS systems using ML
Static Feedback Loops	QS circuits often rely on pre-programmed responses that don’t adjust dynamically	Limits flexibility, causing suboptimal responses in fluctuating conditions	Dynamic feedback loops with machine learning algorithms
Signal Crosstalk	Overlap of QS signals in mixed microbial populations leading to interference	Unintended activation of gene circuits, reducing system reliability	Design of orthogonal QS systems to prevent signal interference
Scalability	Difficulty scaling QS systems in large, diverse microbial consortia	Impairs ability to regulate gene expression in complex environments	ML-driven modeling to manage scalability in diverse consortia
Lack of Predictive Capacity	QS systems respond reactively rather than preemptively	Inefficiency in changing environments, delayed system response	Predictive modeling with machine learning for anticipatory adjustments

Dependence on Static Feedback Loops: Another significant limitation of traditional QS systems is their reliance on static feedback loops, where the relationship between input (autoinducer concentration) and output (gene expression) remains fixed and unresponsive to fluctuations. Once a quorum is sensed, the system commits to a predefined response, with no capacity for dynamic adjustment. This rigidity becomes a major drawback in environments where conditions fluctuate, such as microbial populations involved in industrial bioprocessing or therapeutic applications. Static feedback mechanisms ensure that the system behaves predictably under stable conditions, but they become suboptimal in complex or fluctuating environments, where a more nuanced response is required. [6,25,97,224,241,242,243,244]

For example, in a multi-phase bioreactor, QS-controlled gene expression might be optimized for a particular phase of production. However, if external variables such as nutrient levels or temperature shift, the system's inability to modify its gene expression output in response to these changes results in suboptimal performance. This lack of flexibility and responsiveness in static systems means that they are often unsuitable for real-world applications where constant environmental fluctuations are the norm. [6,35,45,49,54,245,246,247]

In synthetic biology, this is particularly problematic when coordinating tasks across multi-species consortia. If the QS circuits of different species are pre-programmed with static feedback loops, any unforeseen environmental change can disrupt the coordination, leading to reduced efficiency or even failure of the overall system. As outlined in Table 2, potential solutions include the development of dynamic feedback loops that allow for real-time adjustments, driven by machine learning algorithms capable of predicting optimal responses based on environmental data inputs. [65,248,249,250,251,252,253]

Signal Crosstalk: In natural environments, particularly in mixed microbial populations, traditional QS systems often suffer from signal crosstalk—where overlapping QS signals interfere with one another. This problem arises because many microbial species use similar or identical autoinducer molecules, leading to unintended activation of gene circuits. In engineered microbial consortia, such interference reduces the reliability of QS systems, as signals from one species can trigger responses in another, leading to uncoordinated behavior and system inefficiencies. [63,66,254,255,256,257,258,259,260]

For instance, in synthetic microbial consortia designed for bioremediation or bioproduction, signal crosstalk could result in the premature or unregulated activation of metabolic pathways, wasting resources or causing undesirable interactions between species. Preventing signal crosstalk is essential for designing orthogonal QS systems—QS circuits that are insulated from interference by other signals. These orthogonal systems use engineered autoinducers or novel receptors that minimize the risk of signal overlap, ensuring that each species in a consortium operates independently. As highlighted in Table 2, the design of orthogonal QS systems is a crucial area of ongoing research in synthetic biology. [6,65,67,175,249,253,258]

Scalability: The scalability of QS systems presents another challenge, particularly when attempting to regulate gene expression across large, diverse microbial consortia. In small, homogeneous populations, QS systems can effectively control population-wide behaviors. However, as the system scales—both in terms of population size and species diversity—the complexity of signal transmission and interpretation increases. Traditional QS systems struggle to maintain signal fidelity and response efficiency in large-scale applications, leading to poor coordination in consortia designed for industrial-scale processes. [6,30,65,66,67,252,261]

For example, in large-scale bioproduction, where different microbial species are tasked with distinct roles, inefficiencies arise when traditional QS systems cannot propagate signals effectively across the entire population. This results in delayed responses, uneven gene expression, and ultimately reduced output. To address scalability, machine learning-driven modeling can help manage the complexities of QS systems in large, diverse consortia. ML can predict how signals will propagate in a scaled-up system and suggest modifications to QS circuits to maintain signal integrity across larger populations (Table 2). [78,106,111,151,262,263,264,265]

Lack of Predictive Capacity: Traditional QS systems are reactive, responding to environmental stimuli only after a quorum has been sensed. This reactive nature limits their predictive capacity, meaning they cannot preemptively adjust to environmental shifts before they occur. In dynamic environments, this results in inefficiency because the system only reacts after conditions have changed, often leading to a delayed response that can reduce system effectiveness. [67,224,244,266]

For instance, in a therapeutic context, engineered bacteria that rely on QS might produce an antimicrobial compound only after a certain pathogen density is reached. However, by the time the quorum is sensed, the infection may have already progressed to a harmful level. To overcome this, integrating machine learning can allow QS systems to become anticipatory, where they predict environmental changes and adjust gene expression preemptively based on predictive models. This ability to anticipate changes would vastly improve the efficiency of QS systems in fluctuating environments, as shown in Table 2. [67,97,111,241,267,268,269,270,271,272]

In summary, the challenges faced by traditional quorum sensing systems—such as environmental variability, static feedback loops, signal crosstalk, scalability issues, and lack of predictive capacity—highlight the need for more sophisticated approaches. Table 2 outlines the impact of these challenges on synthetic biology applications and presents potential solutions, particularly through the integration of machine learning to enable real-time adaptability, predictive capacity, and enhanced scalability in complex environments. By overcoming these challenges, future QS systems could achieve greater reliability and efficiency in both industrial and medical applications. [4,6,67,97,179,273,274,275]

3. Machine Learning in Synthetic Biology: A New Paradigm for Optimizing QS Systems

3.1. Overview of Machine Learning for Biological Optimization

Machine learning (ML) is transforming the landscape of synthetic biology, especially in the optimization of quorum sensing (QS) systems. With the ability to process vast amounts of biological data and learn from both labeled and unlabeled datasets, ML algorithms allow for the creation of systems that can predict, optimize, and adapt in real time. By applying ML to QS systems, researchers can harness predictive and adaptive capabilities that were previously unavailable [77,147,276,277,278,279]. This section explores key ML techniques and their applications in QS-driven systems, showcasing how each algorithm optimizes cellular behavior and metabolic processes in synthetic biology. Table 3 highlights these ML algorithms, their features, applications, and specific examples of their use in QS-ML systems.

Supervised Learning: Supervised learning is a foundational ML technique in which models learn from labeled data to make predictions about new, unseen data. In QS systems, supervised learning is used to analyze large datasets generated by microbial behavior to predict optimal gene expression states and control population behaviors in real time. By training models on known data, such as the timing of autoinducer accumulation or gene expression events, supervised learning helps optimize the timing and coordination of cellular processes, making it particularly useful in large-scale bioproduction or therapeutic applications. For example, supervised learning models are employed to predict the gene regulation patterns in synthetic microbial consortia, allowing for precise control over metabolic outputs such as enzyme production or toxin neutralization in industrial applications. As highlighted in Table 3, supervised learning is key to analyzing QS signaling data, predicting the most effective moments to trigger gene expression or regulate metabolic pathways, ensuring optimized system performance. [280,281,282,283,284,285,286]

Reinforcement Learning (RL): Reinforcement learning (RL) is an advanced approach where a model learns to optimize its actions through trial and error by interacting with its environment. RL is especially well-suited to dynamic environments like QS systems, where real-time feedback from environmental factors (e.g., nutrient levels, temperature changes) can inform better decision-making. RL can continuously optimize metabolic processes by enabling QS systems to make real-time adjustments to gene expression, ensuring that microbial populations remain efficient under variable conditions. This adaptability makes RL ideal for systems such as bio-reactors that require continuous monitoring and fine-tuning of metabolic outputs. For example, in bio-reactors, RL-driven systems can adjust enzyme production or alter metabolic pathways based on real-time feedback about nutrient concentrations or microbial population density, ensuring optimal bio-production with minimal waste. As shown in Table 3, RL’s application in continuous optimization allows QS-driven systems to remain efficient and adaptive to fluctuating environmental conditions, making it indispensable in industrial-scale applications. [287,288,289,290,291,292,293,294]

Unsupervised Learning: Unsupervised learning identifies hidden patterns within unlabeled data, making it particularly useful for exploratory analyses in QS systems where the full range of microbial behaviors may not yet be known. By clustering QS signaling data, unsupervised learning can detect emerging patterns or novel QS pathways that would otherwise remain undiscovered. This approach is useful in synthetic biology for identifying novel microbial communication strategies or for understanding how different microbial species in synthetic consortia coordinate behaviors. For example, unsupervised learning has been applied to cluster QS signaling data in synthetic microbial systems, uncovering novel pathways and interactions that facilitate more efficient control of gene expression and metabolic regulation across microbial species. As highlighted in Table 3, unsupervised learning helps identify new patterns in QS signaling, allowing researchers to detect novel pathways that improve system design and performance. [295,296,297,298,299,300,301,302,303]

Deep Learning: Deep learning builds on neural networks to process complex, high-dimensional biological datasets, such as those generated by multi-omics approaches. In QS systems, deep learning is especially powerful for recognizing complex relationships between QS signals and environmental conditions. By analyzing these interactions, deep learning models can predict how changes in environmental factors (e.g., pH, nutrient levels) will impact gene expression and metabolic processes, providing a higher level of control over QS-driven systems. For example, deep learning algorithms are applied in bio-reactors to analyze how environmental fluctuations, such as nutrient depletion or temperature changes, affect the efficiency of metabolic pathways. This real-time data processing allows for the immediate adjustment of QS-regulated gene expression, maximizing bio-production outputs. As outlined in Table 3, deep learning’s ability to predict complex relationships makes it essential for optimizing metabolic regulation and improving overall system efficiency in real time. [304,305,306,307,308]

Transfer Learning: Transfer learning enables models trained in one domain to apply their knowledge to new, but related, domains. This is particularly useful in synthetic biology, where a QS system optimized for one microbial species can be adapted for use in another species with minimal retraining. Transfer learning facilitates cross-species generalization, making it easier to design and deploy QS systems across a variety of microbial species and environments. For example, transfer learning has been used to generalize QS models trained on E. coli for use in Pseudomonas aeruginosa, allowing the rapid adaptation of optimized gene circuits for different species without the need for extensive retraining. In Table 3, transfer learning is shown to accelerate the generalization of QS systems, allowing synthetic biologists to efficiently design adaptive systems that work across multiple microbial environments. [309,310,311,312,313]

Evolutionary Algorithms: Evolutionary algorithms are inspired by the principles of natural selection, where the most efficient solutions evolve over time through iterative optimization. In synthetic biology, these algorithms are used to optimize synthetic gene circuits, allowing QS systems to evolve towards greater efficiency and adaptability. Evolutionary algorithms are particularly valuable for optimizing QS responses in changing environments, ensuring that gene circuits continue to perform well as external conditions fluctuate. For example, evolutionary algorithms have been employed to evolve QS circuits that regulate metabolic pathways in bio-reactors, optimizing for signal sensitivity and stability under fluctuating conditions. As described in Table 3, evolutionary algorithms enable the continuous improvement of QS-regulated gene circuits, leading to more robust and adaptive biological systems capable of thriving in dynamic environments. [314,315,316,317,318,319,320,321,322,323]

In summary, the range of machine learning algorithms discussed in this section—supervised learning, reinforcement learning (RL), unsupervised learning, deep learning, transfer learning, and evolutionary algorithms—each brings unique advantages to the optimization of quorum sensing (QS) systems. Through these techniques, synthetic biologists can design adaptive, self-regulating systems that optimize metabolic processes in real time and adjust gene expression dynamically in response to environmental changes. As demonstrated in Table 3, each algorithm offers distinct capabilities for improving QS systems, enabling real-time data processing, predictive modeling, and evolutionary optimization. By leveraging these ML algorithms, synthetic biology can achieve unprecedented levels of precision and efficiency in both industrial biotechnology and precision medicine. These techniques represent a new paradigm for biological optimization, one in which ML-driven QS systems can autonomously regulate microbial behaviors, respond to environmental feedback, and continuously improve over time, ensuring high-performance outputs across diverse applications. [77,275,324,325,326,327]

Table 3. Machine Learning Algorithms and Their Applications in Synthetic Biology.

Table 3. Machine Learning Algorithms and Their Applications in Synthetic Biology.

Algorithm	Key Features	Applications in QS-ML Systems	Examples of Use
Supervised Learning	Learns from labeled data to make predictions	Analyzing QS signaling data to predict optimal gene expression states	Predicting gene regulation patterns in microbial consortia
Reinforcement Learning	Trial-and-error approach to learning optimal actions	Continuous optimization of metabolic processes based on feedback from QS signals	Real-time adjustment of metabolic pathways in response to environmental changes
Unsupervised Learning	Identifies patterns in unlabeled data	Clustering QS signaling data to detect emerging patterns or novel behaviors	Detecting novel QS pathways in synthetic microbial systems
Deep Learning	Uses neural networks for complex pattern recognition	Predicting complex relationships between QS signals and environmental conditions	Analyzing high-dimensional QS data for metabolic regulation in real-time
Transfer Learning	Applies knowledge from one domain to another	Applying models trained on one set of microbial species to another	Generalizing QS systems across different microbial species and environments
Evolutionary Algorithms	Optimization techniques inspired by natural evolution	Optimizing synthetic gene circuits to evolve more efficient QS responses	Designing more efficient QS circuits through iterative simulation

3.2. Integrating ML and QS for Hybrid Adaptive Systems

The integration of machine learning (ML) into quorum sensing (QS) systems has given rise to hybrid adaptive systems that leverage real-time data processing, predictive modeling, and dynamic control to optimize microbial behavior. These hybrid systems offer the capability to adjust gene expression in real-time based on both QS signals and environmental inputs, allowing for dynamic modulation of gene circuits and improved adaptability in fluctuating environments. Figure 3 illustrates the structure of these hybrid QS-ML systems, highlighting the key components such as real-time feedback loops, adaptive gene regulation, and environmental sensing. [3,25,67,97,265,271]

Real-Time Feedback Loops: Real-time feedback loops are essential to hybrid QS-ML systems as they enable continuous monitoring of QS signals and environmental conditions. ML processes data from QS signaling molecules (e.g., autoinducers like AHL, AI-2) and external environmental factors (e.g., temperature, pH, nutrient levels) in real-time, allowing the system to dynamically adjust its responses based on evolving conditions. This real-time adjustment ensures that microbial populations are continuously optimized for productivity and adaptability. [328,329,330,331,332,333,334,335]

• Signal Processing: ML models decode QS signals from microbial communities in real-time, identifying the concentration and timing of autoinducer molecules such as AHL, AI-2, and peptides. This signal processing allows the system to predict changes in population density and adjust gene expression accordingly. For example, an ML-driven system processes AHL levels to predict upcoming increases in population density, allowing for preemptive adjustments in gene expression to optimize biofilm formation or suppression based on the environmental context. [64,66,67,179,336,337]
• Environmental Data Input: ML continuously integrates data from environmental sensors, monitoring factors such as temperature, pH, and nutrient availability. This allows the QS system to consider external factors that may affect microbial behavior. For example, a reinforcement learning model predicts the impact of temperature changes on microbial communication and adjusts QS responses accordingly, ensuring that gene circuits remain active only under optimal conditions. [334,338,339,340,341]
• Feedback Control: ML algorithms provide feedback control by predicting the optimal state of gene expression based on QS inputs and environmental data, dynamically adjusting microbial behavior. For example, ML adjusts the production of biofilm-forming genes in microbial populations based on real-time assessments of nutrient availability, ensuring that resources are efficiently used to maintain or suppress biofilm growth. The combination of signal processing, environmental data input, and feedback control ensures that QS-ML systems are capable of making real-time adjustments that optimize both microbial behavior and system outputs (Figure 3). [237,252,342,343,344,345]

Adaptive Gene Regulation: In hybrid QS-ML systems, adaptive gene regulation allows for the dynamic control of gene circuits based on real-time environmental cues and metabolic needs. By integrating ML-driven models, QS systems can fine-tune gene expression in response to constantly shifting conditions, enabling microbial populations to behave adaptively in complex environments. This ensures that gene circuits can be modulated to maintain productivity while conserving resources. [6,78,89,93,106,346,347,348]

• Dynamic Circuit Modulation: ML algorithms dynamically modulate synthetic gene circuits in response to QS inputs, allowing for rapid adjustments in gene expression. This dynamic modulation ensures that gene circuits can adapt to changing metabolic demands, optimizing the system for bio-production or other industrial applications. For example, in a bio-production system, ML dynamically adjusts gene expression levels of synthetic pathways responsible for producing antibiotics or enzymes, ensuring that the metabolic output matches current resource availability and system demands. [8,47,55,349,350,351]
• Context-Aware Learning: ML models learn from past system performance to improve efficiency over time, adapting gene regulation strategies based on historical data. This context-aware learning enables the system to refine its responses based on previous metabolic cycles, making future gene expression more efficient. For example, a neural network model learns from the outcomes of past metabolic cycles to adjust the expression of key genes, increasing the yield of a desired product (e.g., enzymes or biofuels) in subsequent cycles by optimizing resource allocation and metabolic pathway activation. [352,353,354,355,356,357,358]
• Predictive Gene Expression: ML models predict future gene expression needs based on real-time environmental signals and resource availability. By forecasting future metabolic demands, the system can adjust gene expression proactively rather than reactively, ensuring a more efficient and stable microbial system. For example, ML predicts when microbial populations in synthetic consortia will need to upregulate genes responsible for producing virulence factors or enzymes, allowing the system to optimize output based on the anticipated demand. [111,139,141,145,174,359,360]

Through dynamic circuit modulation, context-aware learning, and predictive gene expression, QS-ML hybrid systems achieve higher levels of precision and adaptability, making them ideal for applications in bio-manufacturing, pharmaceutical production, and environmental remediation (Figure 3). [361,362,363,364,365,366]

Environmental Sensing: Environmental sensing is a critical component of hybrid QS-ML systems, enhancing their ability to sense and react to environmental stressors. By integrating multi-source data from various environmental inputs, ML models can dynamically adjust QS-regulated pathways to maintain system stability under fluctuating conditions. This allows microbial populations to respond effectively to stress factors such as oxidative stress, osmotic pressure, or nutrient depletion. [12,233,367,368,369,370,371]

• Multi-Source Data Integration: ML integrates data from multiple environmental sensors (e.g., temperature, pH, osmotic pressure) to make comprehensive decisions about gene regulation and metabolic activity. This multi-source data integration enables the system to react holistically to changing environmental conditions. For example, by integrating data from both pH and temperature sensors, an ML-driven QS system can predict the optimal moment for QS-mediated gene activation in an industrial-scale bioprocess, ensuring that metabolic processes occur under ideal conditions. [152,367,372,373,374,375,376,377,378]
• Stress Response Mechanisms: ML enhances QS systems’ ability to respond to environmental stressors by regulating protective genes that help microbial populations resist stress. This capability is essential for maintaining microbial health and productivity in challenging environments. For example, when nutrient levels suddenly drop, ML modulates QS pathways to initiate stress response mechanisms, upregulating protective genes that help the microbial population survive until conditions improve. [379,380,381,382,383,384,385,386,387]
• Real-Time Adaptation: ML enables real-time adaptation of QS systems to sudden changes in environmental conditions, ensuring that microbial populations remain stable and productive even in the face of unexpected stressors. By dynamically adjusting gene expression and metabolic pathways, the system can maintain balance and optimize its outputs under variable conditions. For example, ML detects real-time changes in nutrient availability or toxin levels and dynamically adjusts microbial activity, shifting from metabolic production to stress response when necessary to ensure long-term system stability. [348,388,389,390,391,392,393]

By integrating environmental sensing with ML-driven QS systems, researchers can create self-regulating systems that remain resilient and efficient under fluctuating environmental conditions (Figure 3). [334,368,374,389,394,395,396,397]

The integration of machine learning (ML) with quorum sensing (QS) systems creates powerful hybrid adaptive systems that offer unprecedented control over microbial behavior and gene regulation. By combining real-time feedback loops, adaptive gene regulation, and environmental sensing, these systems are capable of making dynamic adjustments in response to environmental inputs, optimizing metabolic pathways, and maintaining stability in unpredictable conditions. Figure 3 provides a comprehensive overview of how these hybrid QS-ML systems operate, showcasing their ability to process QS signals, integrate environmental data, and dynamically regulate gene expression to meet changing system demands. [25,67,174,271,275,398,399,400]

As synthetic biology continues to advance, the development of QS-ML hybrid systems will play a key role in improving the efficiency, scalability, and robustness of applications in bio-production, precision medicine, and environmental management. By leveraging the power of ML, these systems can autonomously adapt to complex environments, providing new avenues for optimizing microbial systems in ways that were previously unimaginable. [6,66,67,97,224,401,402,403,404]

Figure 3. Hybrid QS-ML Systems.

Figure 3. Hybrid QS-ML Systems.

4. Case Studies: Applications of QS-ML Hybrid Systems in Disease Control

4.1. Combatting Pathogenic Bacteria with QS-ML Systems

Machine Learning (ML) in Disrupting Pathogenic QS Networks: The integration of machine learning (ML) into quorum sensing (QS) systems offers an advanced and targeted approach to pathogen control by disrupting bacterial communication networks responsible for virulence and biofilm formation. Pathogenic bacteria, such as Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli, rely on QS to coordinate harmful behaviors like biofilm development, toxin production, and antibiotic resistance. By employing ML algorithms to decode and analyze QS signaling pathways, researchers can predict the optimal moments to disrupt QS signals, thus halting infection progression and enhancing treatment outcomes. Table 4 outlines key case studies where QS-ML hybrid systems have been applied to combat pathogenic bacteria, demonstrating their effectiveness in real-world clinical and practical settings. [267,270,405,406,407,408]

ML's Role in Disrupting Biofilm Formation: For pathogens like Pseudomonas aeruginosa, which uses the LasI/LasR QS system to regulate biofilm formation and virulence, biofilms pose a significant challenge in treating infections due to their resistance to antibiotics. ML has been leveraged to predict biofilm development by analyzing the accumulation of autoinducers like N-acyl homoserine lactone (AHL) in real-time. By forecasting the critical points at which biofilm formation becomes clinically problematic, QS-ML hybrid systems can trigger anti-biofilm interventions, such as QS inhibitors or biofilm-disrupting enzymes, before the biofilm fully matures. [192,194,406,409,410,411,412,413]

• Outcome: ML-driven QS disruption strategies have shown to reduce biofilm density in Pseudomonas aeruginosa, significantly improving the efficacy of antimicrobial treatments in cystic fibrosis patients and those with chronic wound infections. [414,415,416]
• Clinical Application: The use of QS-ML systems in cystic fibrosis treatment has enhanced the management of chronic lung infections by enabling more targeted use of antibiotics, reducing the incidence of drug resistance (Table 4). [417,418,419,420]

ML in Suppressing Virulence in Staphylococcus aureus:Staphylococcus aureus, particularly methicillin-resistant Staphylococcus aureus (MRSA), uses the AgrC/AgrA QS system to regulate the production of toxins that cause tissue damage and immune system evasion. ML algorithms can monitor QS signal fluctuations to predict when virulence genes will be upregulated, allowing for preemptive suppression of QS-regulated toxin production. By inhibiting QS at critical junctures, ML-driven systems can significantly reduce the expression of virulence factors. [267,421,422,423,424]

• Outcome: In experimental models, ML-enhanced QS suppression has led to a marked reduction in virulence factor expression, improving the treatment outcomes in hospital-acquired infections, including those caused by MRSA strains. [197,425]
• Clinical Application: QS-ML systems have the potential to lower the virulence of Staphylococcus aureus in clinical settings, reducing the severity of infections and the risk of complications, particularly in hospitals where nosocomial infections are a major concern (Table 4). [422,426]

Real-Time QS Monitoring for Virulence Control in Escherichia coli (EHEC): In food safety, controlling the spread of foodborne pathogens like Escherichia coli (EHEC) is critical to preventing outbreaks of foodborne illness. The AI-2 QS system in E. coli plays a central role in coordinating the activation of virulence factors, such as toxin production. ML algorithms can be used to monitor QS signals in real time, allowing for the early detection of virulence activation before the pathogen reaches dangerous levels. [130,427,428,429,430,431,432,433]

• Outcome: The implementation of real-time QS monitoring via ML has led to preemptive mitigation of toxin production, significantly reducing the severity of E. coli outbreaks in food processing environments. [130,431,434]
• Practical Application: QS-ML systems can be integrated into food safety protocols to provide early warnings of virulence activation, allowing for swift intervention in foodborne outbreaks and improved prevention strategies (Table 4). [130,435,436]

QS-ML Systems in Cholera Control: The LuxO QS system in Vibrio cholerae is a critical regulator of cholera toxin production, which drives the symptoms of cholera infections. By leveraging ML to analyze QS signals in V. cholerae, researchers can predict and disrupt the quorum sensing pathways that lead to toxin production. This approach allows for timely intervention to prevent the release of cholera toxins, which are responsible for severe diarrheal disease and dehydration. [437,438,439,440]

• Outcome: QS-ML interventions have shown a significant reduction in cholera toxin levels, providing a promising approach for controlling cholera outbreaks in regions prone to epidemics. [441,442,443,444]
• Public Health Application: In epidemic-prone areas, QS-ML hybrid systems could be deployed to monitor environmental and population-level QS signals, triggering preemptive responses that help mitigate the spread of cholera and reduce the need for large-scale medical interventions (Table 4). [338,442,445]

By integrating machine learning with quorum sensing systems, QS-ML hybrid systems represent a groundbreaking advancement in the fight against pathogenic bacteria. Through the continuous monitoring of QS signals, ML can predict the development of biofilms, the activation of virulence factors, and the onset of toxin production in pathogens like Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli. The case studies highlighted in Table 4 demonstrate the effectiveness of these systems in reducing the virulence of pathogens, improving the efficacy of antimicrobial treatments, and preventing the spread of foodborne and hospital-acquired infections. [3,5,180,269,406,411,446,447]

As researchers continue to refine QS-ML systems, their predictive capabilities and ability to adapt in real time will enhance the management of antimicrobial resistance, improve infection control strategies, and provide innovative tools for controlling epidemics. These developments hold the potential to revolutionize clinical approaches to infectious diseases and public health management, offering precise, adaptable, and highly effective interventions against some of the most challenging bacterial pathogens. [423,448,449,450,451,452,453,454,455]

Table 4. QS-ML Hybrid Systems in Pathogen Control.

Table 4. QS-ML Hybrid Systems in Pathogen Control.

Pathogen	QS System	ML Intervention	Outcome	Clinical/Practical Application
Pseudomonas aeruginosa	LasI/LasR	Machine learning to predict and disrupt biofilm formation	Reduced biofilm density, improved antimicrobial efficacy	Enhanced treatment of cystic fibrosis and wound infections
Staphylococcus aureus	AgrC/AgrA	ML-driven suppression of QS signals regulating toxin production	Reduced virulence factor expression, improved patient outcomes	Lower virulence in hospital-acquired infections (e.g., MRSA)
Escherichia coli(EHEC)	AI-2	Real-time QS monitoring using ML for early detection of virulence activation	Preemptive mitigation of toxin production	Food safety: preventing foodborne illness outbreaks
Vibrio cholerae	LuxO	ML-enhanced QS disruption to prevent cholera toxin production	Significant reduction in cholera toxin levels	Cholera control strategies in epidemic-prone areas

4.2. Precision Therapies and Gene Regulation

ML-Driven QS for Precision Medicine: The convergence of quorum sensing (QS) and machine learning (ML) is revolutionizing precision therapies by enabling real-time control and optimization of gene regulation in synthetic organisms. QS-ML hybrid systems allow for the development of highly adaptive, patient-specific therapeutic approaches, where gene expression and cellular responses can be dynamically adjusted based on real-time patient data. This adaptability not only improves the efficacy of therapies but also reduces side effects by ensuring that treatments are delivered precisely when and where they are needed. Table 5 highlights several key applications of QS-ML systems in precision therapies, illustrating how ML-driven optimization enhances treatment outcomes across diverse therapeutic modalities, such as gene therapy, cancer immunotherapy, drug delivery systems, stem cell therapy, and synthetic probiotic treatments. [456,457,458,459,460,461,462,463,464]

Gene Therapy Applications: In gene therapy, QS-ML systems can be employed to provide real-time control over the expression of therapeutic genes. By integrating ML algorithms into QS-regulated gene circuits, therapeutic genes can be activated or silenced in response to patient-specific factors, such as metabolic state, disease progression, or physiological changes. This precise regulation allows for tailored gene delivery, significantly improving treatment efficacy while minimizing potential side effects. [465,466,467,468,469,470]

• ML Optimization: ML models continuously process patient data to predict the optimal timing and dosage for gene expression, adjusting the output of therapeutic genes in real time to match the patient's condition. [471,472,473,474,475]
• Outcome: This approach leads to improved efficacy of gene delivery and reduced side effects, as the therapy is personalized to the patient’s specific needs. [476,477,478,479]
• Example Application: In the context of inherited genetic diseases, such as Duchenne muscular dystrophy (DMD), QS-ML systems have been used to regulate the expression of corrective genes, ensuring that gene delivery is synchronized with the patient’s metabolic cycles for better therapeutic outcomes (Table 5). [480,481,482,483,484,485,486,487]

Cancer Immunotherapy: QS-ML hybrid systems are also being applied in cancer immunotherapy, where immune cell activation is carefully controlled to maximize tumor targeting while minimizing damage to healthy tissues. ML plays a critical role in optimizing the timing and intensity of immune responses, ensuring that immune cells are activated at the most opportune moments for tumor destruction. [488,489,490,491]

• ML Optimization: By analyzing tumor microenvironment data—such as oxygen levels, tumor-associated antigens, and immune checkpoint signals—ML models can optimize immune cell activation in QS-controlled systems, adjusting the production of cytokines or other immunomodulatory molecules in response to real-time feedback. [491,492,493,494,495,496,497]
• Outcome: This precise modulation results in enhanced tumor targeting and minimized collateral damage to healthy tissues. [498,499,500,501,502,503]
• Example Application: In trials involving adaptive immune system modulation, QS-ML systems have been shown to improve the efficacy of T-cell-based immunotherapies, allowing engineered immune cells to dynamically adjust their activity based on tumor conditions, reducing the risk of excessive immune responses or off-target effects (Table 5). [504,505,506,507,508]

Drug Delivery Systems: Drug delivery systems that rely on QS-regulated mechanisms are enhanced by ML-driven predictions to optimize the timing and dosage of drug release. These systems are capable of precisely targeting diseased cells while minimizing systemic toxicity, making them especially useful in chemotherapy and other treatments where precision is critical. [509,510,511,512,513,514,515]

• ML Optimization: ML models can analyze patient-specific data, such as tumor growth rates, metabolic profiles, and blood circulation patterns, to predict the optimal timing for drug release and adjust the dosage dynamically. [475,516,517,518,519,520]
• Outcome: This results in precise drug targeting to diseased tissues, significantly reducing systemic toxicity and enhancing the overall safety profile of treatments. [511,513,514,515,521,522,523,524]
• Example Application: In chemotherapy, QS-ML hybrid systems have been applied to deliver drugs directly to tumor cells, using ML to predict when the tumor is most vulnerable and adjust the release of chemotherapy agents accordingly, improving treatment outcomes and reducing side effects (Table 5). [425,525,526,527]

Stem Cell Therapy: In stem cell therapy, QS-ML systems are used to guide the differentiation of stem cells into specific cell types, ensuring that the cells integrate properly and promote tissue regeneration. By using ML to predict optimal differentiation pathways, these systems can adjust QS signals in response to environmental cues, enhancing the therapeutic potential of stem cell-based treatments. [528,529,530,531,532,533]

• ML Optimization: ML algorithms analyze various factors such as local growth signals, immune responses, and tissue conditions to predict the most effective pathways for stem cell differentiation. [533,534,535,536,537,538]
• Outcome: This results in improved stem cell integration and more efficient tissue regeneration, which is critical for successful outcomes in regenerative medicine. [529,530,539,540]
• Example Application: In regenerative therapies for cartilage repair or neural tissue regeneration, QS-ML systems have been used to guide stem cell differentiation, ensuring that cells develop into the appropriate tissue type and integrate seamlessly with surrounding tissues (Table 5). [541,542,543,544,545,546]

Synthetic Probiotic Therapies: Synthetic probiotic therapies utilize engineered microbes that can respond to signals from the gut environment to maintain or restore balance within the microbiome. By integrating ML with QS, these probiotics can be programmed to adjust their behavior in response to changing conditions in the gut, such as pH, nutrient availability, or microbial composition. This approach can be highly effective in treating gastrointestinal disorders and regulating inflammatory responses. [275,547,548,549,550,551]

• ML Optimization: ML models analyze data from the gut environment, predicting the optimal behaviors for synthetic probiotics to restore balance or mitigate inflammation. [547,552,553,554,555,556,557]
• Outcome: The result is enhanced gut health and reduced inflammation, providing a novel approach to treating gastrointestinal disorders and maintaining microbiome homeostasis. [551,558]
• Example Application: In clinical trials, QS-controlled synthetic probiotics have been used to modulate the gut environment in patients with irritable bowel syndrome (IBS) or Crohn's disease, improving symptoms by dynamically adjusting their activity in response to ML predictions about the gut's changing conditions (Table 5). [556,559,560]

Table 5. Applications of QS-ML Systems in Precision Therapies.

Table 5. Applications of QS-ML Systems in Precision Therapies.

Therapy Type	QS Component	ML Optimization	Outcome	Example Application
Gene Therapy	QS-regulated gene circuits	Real-time gene expression adjustment based on patient data	Improved efficacy of gene delivery, reduced side effects	Personalized gene therapy for inherited genetic diseases
Cancer Immunotherapy	QS-controlled immune cell activation	ML to optimize timing and intensity of immune response	Enhanced tumor targeting, minimized damage to healthy tissues	Adaptive immune system modulation in cancer treatment
Drug Delivery Systems	QS-triggered release mechanisms	ML-driven prediction of drug release timing and dosage	Precise targeting of diseased cells, reduced systemic toxicity	Controlled release systems for chemotherapy drugs
Stem Cell Therapy	QS-guided differentiation	ML to predict optimal differentiation pathways	Improved stem cell integration and tissue regeneration	Regenerative medicine: cartilage or neural tissue repair
Synthetic Probiotic Therapies	QS-controlled synthetic probiotics	ML to optimize probiotic behavior in response to gut environment	Enhanced gut health, reduced inflammation	Treatment of gastrointestinal disorders, microbiome modulation

The integration of machine learning (ML) with quorum sensing (QS) systems provides an advanced platform for precision therapies that can dynamically adjust to patient-specific needs and real-time environmental conditions. The examples outlined in Table 5 demonstrate the transformative potential of QS-ML hybrid systems in enhancing therapeutic outcomes across a wide range of medical fields, from gene therapy and cancer immunotherapy to drug delivery systems and synthetic probiotic therapies. By utilizing ML-driven optimization, these systems offer unprecedented control over gene expression, immune modulation, and cellular differentiation, paving the way for more effective, personalized treatments with fewer side effects. [473,518,561,562,563]

These developments in precision medicine mark a significant step forward in the design and implementation of adaptive therapies, ensuring that treatments are delivered at the optimal time and place, tailored to the unique conditions of each patient. As QS-ML systems continue to evolve, they will play an increasingly important role in advancing personalized healthcare and improving the efficacy and safety of therapeutic interventions. [457,564,565,566,567]

5. Discussion

5.1. Technical Challenges in Hybrid QS-ML Systems

Implementing hybrid QS-ML systems presents several technical challenges that must be addressed to fully unlock their potential in synthetic biology and precision medicine. These challenges range from data integration and computational complexity to the availability of training data, scalability, and system robustness. Each of these challenges has a direct impact on the performance and reliability of QS-ML systems [568,569,570,571,572,573,574,575,576]. Table 6 summarizes the major technical obstacles and suggests potential solutions to overcome them, offering a pathway for future advancements.

Table 6. Challenges in Implementing QS-ML Systems.

Table 6. Challenges in Implementing QS-ML Systems.

Challenge	Description	Impact	Potential Solutions
Data Integration	Difficulty in merging biological, genomic, and QS signaling data	Leads to incomplete or inconsistent models	Development of unified data integration platforms
Computational Complexity	High computational costs of real-time processing of QS-ML systems	Slows down system performance, limits scalability	Use of cloud computing and edge computing for real-time processing
Training Data Availability	Lack of large, labeled datasets for training ML models	Hinders model accuracy and generalization	Generating synthetic datasets and using transfer learning
Scalability	Difficulty in scaling systems for large, complex microbial consortia	Reduces effectiveness in multi-species synthetic biology setups	Designing modular, hierarchical QS-ML systems
System Robustness	Vulnerability to environmental disturbances or unexpected inputs	Causes system breakdowns or unintended outcomes	Building robust safety mechanisms and fail-safe designs

Data Collection and Integration: One of the most significant technical barriers to implementing QS-ML systems is the challenge of data integration. Biological data is highly heterogeneous, encompassing genomic data, QS signaling molecules, metabolic information, and environmental factors. Merging these diverse datasets into a unified platform for machine learning (ML) models is complex and prone to errors, leading to incomplete or inconsistent models. Moreover, the real-time nature of QS-ML systems demands that this data be processed and integrated at high speed, making it difficult to maintain accuracy across all inputs. [373,577,578,579,580,581,582]

• Impact: Inconsistent or incomplete data integration can lead to misinformed decisions by the ML models, reducing the overall effectiveness of the system and limiting its real-world application, particularly in clinical or industrial settings where precision is crucial. [583,584,585]
• Potential Solutions: To address this issue, there is a need for the development of unified data integration platforms that can seamlessly merge genomic, proteomic, and QS signaling data into a consistent format for ML models. This would require innovations in bioinformatics tools and data standardization protocols that ensure high data fidelity across different sources (Table 6). [578,582,586,587]

Computational Complexity: The computational demands of processing real-time data in QS-ML systems are substantial. These systems must handle a continuous influx of biological signals while simultaneously running predictive models that adjust gene expression and metabolic activities based on environmental feedback. This requires significant computational power, particularly when scaling systems to larger microbial consortia or multi-species setups. The high computational cost associated with real-time processing can slow down system performance and limit its scalability. [148,402,588,589,590,591,592,593]

• Impact: The computational complexity involved can severely reduce system performance, creating delays in decision-making and leading to suboptimal outcomes, particularly in fast-changing environments like industrial bioreactors or patient-specific medical applications. [594,595,596,597]
• Potential Solutions: One potential solution is to leverage cloud computing or edge computing platforms, which can provide the necessary computational resources to handle large-scale real-time data processing. Edge computing is especially useful for decentralized processing, allowing real-time adjustments to be made closer to where the data is generated, thus reducing latency and improving system responsiveness (Table 6). [598,599,600,601]

Training Data Availability: Another major challenge is the availability of large, labeled datasets for training ML models in QS-ML systems. Biological datasets, particularly those involving complex microbial communities, are often scarce, making it difficult to train ML models that can generalize well to new environments or species. This lack of training data can lead to model overfitting, where the system performs well on known data but struggles when faced with new or diverse conditions. [602,603,604,605,606]

• Impact: Without sufficient training data, ML models may lack the accuracy needed to reliably predict gene expression outcomes or metabolic responses in novel environments, reducing the system's overall effectiveness in practical applications.
• Potential Solutions: To mitigate this challenge, researchers can generate synthetic datasets using in silico simulations of microbial behaviors, which can help train models even in the absence of real-world data. Additionally, transfer learning—where models trained on one dataset are adapted for use in a different but related context—can be employed to improve model generalization in environments with limited training data (Table 6).

Scalability: Scaling QS-ML systems from simple microbial populations to large, complex consortia introduces new challenges in terms of signal processing, cross-species communication, and maintaining system coherence. The more complex the microbial environment, the greater the chance that QS signals will interfere with one another, creating noise that reduces the system's overall efficacy.

• Impact: The difficulty in scaling these systems leads to reduced effectiveness in multi-species synthetic biology setups, limiting the potential for applying QS-ML systems to large industrial processes or complex therapeutic applications, such as those involving human microbiomes. [603,604,605,607,608,609]
• Potential Solutions: One promising solution is to design modular, hierarchical QS-ML systems that can function independently at smaller scales but are capable of interacting in a coordinated manner when combined. These modular systems allow for better management of complexity, as each unit can be optimized for a specific task or environment, while the overall system remains adaptable to larger setups (Table 6). [603,604,610,611,612]

System Robustness: QS-ML systems are highly sensitive to environmental inputs, making them vulnerable to disturbances or unexpected changes. For instance, fluctuations in temperature, pH, or nutrient availability can lead to system breakdowns or cause unintended gene expression. Ensuring that these systems remain robust in the face of unpredictable environmental conditions is critical for their reliability, particularly in industrial or medical applications where stability is paramount. [613,614,615,616]

• Impact: Environmental disturbances can result in unintended outcomes, such as the activation of harmful gene circuits or the overproduction of metabolic byproducts, posing risks to both system efficacy and safety. [617,618,619]
• Potential Solutions: To enhance system robustness, multi-layered safety mechanisms should be integrated into QS-ML systems. These include fail-safe designs that automatically revert the system to a safe state if anomalies are detected, and robust feedback loops that allow the system to dynamically adjust its behavior in response to environmental disturbances. Additionally, redundancy in key circuits can provide backup functions in case of failure, ensuring that critical operations continue even when parts of the system malfunction (Table 6). [620,621,622,623]

The successful implementation of QS-ML hybrid systems faces several technical challenges, including data integration, computational complexity, training data availability, scalability, and system robustness. These obstacles must be overcome to enable the widespread deployment of QS-ML systems in synthetic biology and precision medicine. Table 6 outlines key challenges and potential solutions, providing a roadmap for future innovations that will allow QS-ML systems to reach their full potential. By addressing these challenges through advanced data integration platforms, computational optimizations, and robust safety mechanisms, researchers can enhance the performance, scalability, and reliability of QS-ML systems, paving the way for their use in complex real-world applications. [425,459,569,571,573,575,624,625,626]

5.4. Opportunities for Future Research and Development

The future of hybrid QS-ML systems holds immense potential, but advancing this field will require addressing key challenges related to multi-omics data integration, scalability, and cross-disciplinary collaboration. The technical issues discussed in section 5.1, such as data integration and scalability, along with the control-flexibility balance from section 5.2 and the ethical considerations in section 5.3, all point to specific areas where innovation and collaboration are needed. Figure 4 illustrates the core areas of future research that will be critical for driving the development of QS-ML systems, highlighting the importance of integrating multi-omics data, improving scalability, and fostering cross-disciplinary collaboration to build more adaptable, reliable, and scalable systems. [146,701,702,703,704,705,706,707]

Multi-Omics Data Integration: The integration of multi-omics data (genomics, proteomics, metabolomics) into QS-ML systems is a key area for future research, as it can provide a more comprehensive understanding of microbial behavior and enhance the precision of ML-driven predictions. Currently, the challenge of integrating such diverse datasets—discussed in section 5.1—hampers the ability of QS-ML systems to make accurate, real-time decisions in complex environments. [587,708,709]

• Genomics Integration: Incorporating genomic data into QS-ML systems can help improve gene regulation by allowing the system to predict gene expression changes in response to environmental signals. By integrating genomic sequencing data, ML models can forecast how different QS pathways will affect microbial behavior at a molecular level. For example, predicting gene expression changes in microbial consortia based on genomic data can improve the control of synthetic biological systems, such as those used in bio-production. [147,710]
• Proteomics Integration: Proteomics data—which maps the interactions between proteins—can enhance the ability of QS-ML systems to adjust cellular behaviors more precisely. By integrating protein expression profiles, ML can optimize metabolic pathways based on real-time analysis of protein networks. For example, ML-driven QS systems could analyze protein interaction networks to adjust metabolic pathways in real time, improving the efficiency of biosensor applications or other synthetic biology processes. [711,712]
• Metabolomics Integration: Metabolomics data tracks metabolic activities, providing insight into cellular energy use, nutrient uptake, and metabolic fluxes. This data is critical for bio-production applications where the timely adjustment of gene expression is necessary to optimize production yields and efficiency. For example, an ML-driven QS system that adjusts gene expression based on real-time metabolomic data could enhance the productivity of bio-reactors or optimize microbial behavior in biosensors. [709,710]
• Cross-Omics Correlation: Future advancements will involve building models that correlate data across genomics, proteomics, and metabolomics layers to fine-tune predictions and improve the accuracy of cellular responses. This cross-omics approach will help QS-ML systems become more adaptive and predictive in diverse biological environments. For example, developing ML models that integrate cross-omics data to optimize microbial bio-production processes, ensuring that the system reacts dynamically to environmental changes and improves output predictability (Figure 4). [713,714]

Figure 4. Future Directions for QS-ML Systems.

Figure 4. Future Directions for QS-ML Systems.

Scalability: Scaling QS-ML systems to larger, more complex applications is one of the biggest hurdles, as discussed in 5.1. Addressing issues related to computational complexity and system robustness is crucial for making these systems practical in industrial, medical, and environmental settings. To achieve this, research will need to focus on improving computational efficiency, developing cloud-based solutions, and designing modular QS-ML systems that can scale up or down based on the application. [67,596,715,716,717]

• Computational Efficiency: Future QS-ML systems must be capable of processing large datasets in real time without overloading computational resources. The development of scalable ML algorithms that can handle real-time data from large microbial populations will be essential for expanding the scope of QS-ML applications. For example, scalable ML models that process data in multi-species bioreactors could allow for real-time monitoring and adjustments of microbial behavior in large-scale industrial processes, improving efficiency and reducing waste. [716,717]
• Cloud-Based Solutions: Leveraging cloud computing can help overcome the limitations of local processing power by enabling remote monitoring and control of QS-ML systems. Cloud-based platforms would allow for continuous monitoring of biological processes in real-time, with the ability to make adjustments based on the analysis of large, complex datasets. For example, cloud-based monitoring of bioreactors in industrial applications could enable QS-ML systems to make real-time adjustments to microbial behavior from remote locations, enhancing the scalability of bio-production processes (Figure 4). [715,718]
• Modular Design for QS-ML Systems: Modular designs would allow for the flexible scaling of QS-ML systems, from lab-scale experiments to large industrial bioprocesses. By creating self-contained modules that can function independently or as part of a larger system, QS-ML systems could be easily adapted to different applications. For example, modular systems that control different microbial populations in large bioreactors could allow for independent regulation of each population, improving system performance in complex industrial setups (Figure 4). [719,720,721]

Cross-Disciplinary Collaboration: Cross-disciplinary collaboration will play a pivotal role in advancing QS-ML systems, bringing together expertise from fields such as synthetic biology, AI, bioinformatics, and systems biology. These collaborations will drive innovation by combining biological insights with advanced computational tools, leading to more sophisticated models that can predict and regulate biological behaviors with greater accuracy. [722,723,724]

• Synthetic Biologists & AI Researchers: Collaboration between synthetic biologists and AI researchers will be crucial for optimizing the design and control of QS-ML systems. AI researchers can develop novel ML algorithms tailored specifically to biological datasets, while synthetic biologists provide the biological context necessary for these systems to function effectively. For example, AI researchers developing new ML algorithms optimized for biological data could work with synthetic biologists to refine QS-ML systems that manage microbial behavior in real-time bioprocesses. [707,725]
• Bioinformatics & Systems Biology: Collaboration between bioinformatics and systems biology experts will help create more advanced models for predicting biological behaviors. By leveraging bioinformatics tools to generate detailed datasets, systems biologists can test and validate these models in experimental settings, ensuring that the predictions hold up in real-world applications. For example, bioinformatics researchers providing detailed datasets for ML models that optimize QS interactions, with systems biologists conducting experiments to validate predictions in real-time applications (Figure 4). [146,723]
• Shared Data Platforms: Establishing shared data platforms will be critical for fostering collaboration between biological and AI researchers. Open-source databases that contain multi-omics data could accelerate the development of QS-ML systems by providing a common resource for researchers to train ML models and optimize synthetic biology applications. For example, open-source databases that house genomic, proteomic, and metabolomic data could be used to train ML models for more accurate control of QS systems in industrial or medical applications, allowing for faster innovation and wider adoption (Figure 4). [146,707]

The future development of QS-ML systems will require significant advancements in multi-omics data integration, scalability, and cross-disciplinary collaboration. By focusing on integrating data from genomics, proteomics, and metabolomics, researchers can build more comprehensive models that improve the accuracy and adaptability of QS-ML systems in real-world environments. Scalability remains a key challenge, but innovations in modular system design, cloud computing, and computational efficiency offer promising solutions. Furthermore, fostering collaboration between synthetic biologists, AI researchers, and bioinformaticians will be essential for creating robust and scalable QS-ML systems capable of revolutionizing synthetic biology, precision medicine, and industrial biotechnology. [146,147,463,588,704,705,713,726,727,728]

6. Conclusion

References

1. Konda, M. , et al., Quorum Sensing: A New Target for Anti-infective Drug Therapy. 2023.
2. Li, Y. , et al., An Engineered Escherichia coli Community for Studying Quorum Sensing. SynBio 2023, 1, 144–157. [Google Scholar] [CrossRef]
3. Snehashis Koley, A.B. , Mandira Mukherjee, Quorum Sensing in Gram-Negative Bacteria, in Natural Products. 2023, CRC Press. p. 37.
4. Swetha, B.M., M. Saravanan, and P. Piruthivraj, Emerging trends in the inhibition of bacterial molecular communication: An overview. Microbial Pathogenesis, 2023: p. 106495.
5. Taylor-Robinson, Z.S.a.A.W. , Quorum Sensing in Biofilm, in Recent Advances in Bacterial Biofilm Studies - Formation, Regulation, and Eradication in Human Infections. 2023, IntechOpen.
6. Ge, C. , et al., Redesigning regulatory components of quorum-sensing system for diverse metabolic control. Nature communications 2022, 13, 2182. [Google Scholar] [CrossRef] [PubMed]
7. Gu, F. , et al., A synthetic population-level oscillator in non-microfluidic environments. Communications Biology 2023, 6, 515. [Google Scholar] [CrossRef] [PubMed]
8. Liu, Y. , et al., Dynamic cell programming with quorum sensing-controlled CRISPRi circuit. ACS Synthetic Biology 2020, 9, 1284–1291. [Google Scholar] [CrossRef] [PubMed]
9. Moon, S.Y. , et al., Designing microbial cell factories for programmable control of cellular metabolism. Current Opinion in Systems Biology, 2023: p. 100493.
10. Saha, R. , et al., Microbial quorum sensing systems: new and emerging trends of biotechnology in bioremediation. Microbes and Microbial Biotechnology for Green Remediation, 2022: p. 795-811.
11. Soma, Y. , et al., Design of synthetic quorum sensing achieving induction timing-independent signal stabilization for dynamic metabolic engineering of E. coli. ACS Synthetic Biology 2021, 10, 1384–1393. [Google Scholar] [CrossRef]
12. Wan, X. , et al., Programming living sensors for environment, health and biomanufacturing. Microbial biotechnology 2021, 14, 2334–2342. [Google Scholar] [CrossRef]
13. =, *!!! REPLACE !!!*. Al-Tayawi, T.S., E.M. Adel, and F.H. Omer, =An Overview of Biofilm as a Virulence Factor for Bacteria to Survive in the Harsh Environment. =International Journal of Medical Science and Clinical Research Studies 2023, 3, 1188–1197. [Google Scholar]
14. Aransiola, S.A., B. Selvaraj, and N.R. Maddela, Bacterial biofilm formation and anti-biofilm strategies. Research in Microbiology 2024, 175, 104172.
15. Bello, O.O. , et al., Occurrence and Role of Bacterial Biofilms in Different Systems. Acta microbiologica Bulgarica, 2023.
16. Gaurav G Khandalkar, P.K.U. , Rahul G Ingle, Natural Products Could be a Promising Remedy against Biofilm-Forming Bacteria. International Journal of Pharmaceutical Quality Assurance 2024, 15, 1035–1039. [Google Scholar] [CrossRef]
17. Handa, V. , et al., Quorum Sensing Mechanisms, Biofilm Growth, and Microbial Corrosion Effects of Bacterial Species, in Machine Learning in 2D Materials Science. 2023, CRC Press. p. 133-146.
18. Harpke, M. and E. Kothe, Biofilm formation in Gram-positives as an answer to combined salt and metal stress. Journal of Basic Microbiology 2023, 63, 888–896. [Google Scholar] [CrossRef]
19. Sivakumar Krishnan, S.A.P., Y. V. Nancharaiah, Environmental microbial biofilms: formation, characteristics, and biotechnological applications, in Material-Microbes Interactions. 2023: Academic Press. p. 3-45.
20. Xu, F. , et al., Protective effects of antibiotic resistant bacteria on susceptibles in biofilm: Influential factors, mechanism, and modeling. Science of The Total Environment 2024, 930, 172668. [Google Scholar] [CrossRef] [PubMed]
21. Chattopadhyay, I., T.M. Usman, and S. Varjani, Exploring the role of microbial biofilm for industrial effluents treatment. Bioengineered 2022, 13, 6420–6440. [CrossRef] [PubMed]
22. Jaiswal, S. and P. Shukla, Alternative strategies for microbial remediation of pollutants via synthetic biology. Frontiers in microbiology 2020, 11, 808. [Google Scholar] [CrossRef] [PubMed]
23. Nguyen, P.Q. , Synthetic biology engineering of biofilms as nanomaterials factories. Biochemical Society Transactions 2017, 45, 585–597. [Google Scholar] [CrossRef]
24. Sahreen, S. , et al., Exploring the function of quorum sensing regulated biofilms in biological wastewater treatment: A review. International Journal of Molecular Sciences 2022, 23, 9751. [Google Scholar] [CrossRef]
25. Wang, S. , et al., Advances in the Application of Quorum Sensing to Regulate Electrode Biofilms in Bioelectrochemical Systems. Fermentation 2023, 9, 625. [Google Scholar] [CrossRef]
26. Xue, Y.-M. , et al., Engineering a Pseudomonas putida as living quorum quencher for biofilm formation inhibition, benzenes degradation, and environmental risk evaluation. Water Research 2023, 246, 120690. [Google Scholar] [CrossRef]
27. Yaashikaa, P., M. K. Devi, and P.S. Kumar, Engineering microbes for enhancing the degradation of environmental pollutants: A detailed review on synthetic biology. Environmental Research 2022, 214, 113868. [Google Scholar] [CrossRef]
28. Zhou, L. , et al., Regulatory mechanisms and promising applications of quorum sensing-inhibiting agents in control of bacterial biofilm formation. Frontiers in microbiology 2020, 11, 589640. [Google Scholar] [CrossRef]
29. Kraikivski, P. , Synchronization of Oscillatory Gene Networks. Case Studies in Systems Biology, 2021: p. 123-136.
30. Martinelli, V., et al., Multicellular PI control for gene regulation in microbial consortia. IEEE Control Systems Letters 2022, 6, 3373–3378. [CrossRef]
31. Rattray, J.B. , et al., Bacterial quorum sensing allows graded and bimodal cellular responses to variations in population density. MBio 2022, 13, e00745–22. [Google Scholar] [CrossRef] [PubMed]
32. Agrawal, D.K. , et al., Mathematical modeling of RNA-based architectures for closed loop control of gene expression. ACS synthetic biology 2018, 7, 1219–1228. [Google Scholar] [CrossRef] [PubMed]
33. Cannarsa, M.C. , et al., Light-driven synchronization of optogenetic clocks. bioRxiv, 2023: p. 2023.10. 24.563722.
34. Chang, M., et al., Gene expression modulation tools for bacterial synthetic biology. Biotechnology for Sustainable Materials 2024, 1, 6.
35. Glasscock, C.J. , et al., Dynamic control of gene expression with riboregulated switchable feedback promoters. ACS synthetic biology 2021, 10, 1199–1213. [Google Scholar] [CrossRef]
36. Jang, S. , et al., Synthetic regulatory tools to engineer microbial cell factories for chemical production, in Current Developments in Biotechnology and Bioengineering. 2019, Elsevier. p. 115-141.
37. Liu, H. , et al., Synthetic gene circuits enable Escherichia coli to use endogenous H2S as a signaling molecule for quorum sensing. ACS Synthetic Biology 2019, 8, 2113–2120. [Google Scholar] [CrossRef]
38. Moore, J.C., I. Ramos, and S. Van Dien, Practical genetic control strategies for industrial bioprocesses. Journal of Industrial Microbiology and Biotechnology 2022, 49, kuab088.
39. Simmons, T.R. , et al., Rewiring native post-transcriptional carbon regulators to build multi-layered genetic circuits and optimize engineered microbes for bioproduction. bioRxiv, 2023: p. 2023.10. 04.560922.
40. Tang, C. , et al., On-demand biomanufacturing through synthetic biology approach. Materials Today Bio 2023, 18, 100518. [Google Scholar] [CrossRef]
41. Bramhachari, P.V. and G. Mohana Sheela, Vibrio fischeri symbiotically synchronizes bioluminescence in marine animals via quorum sensing mechanism. Implication of Quorum Sensing System in Biofilm Formation and Virulence, 2018: p. 207-219.
42. Miyashiro, T. and E.G. Ruby, Shedding light on bioluminescence regulation in Vibrio fischeri. Molecular microbiology 2012, 84, 795–806. [CrossRef]
43. Venturi, V. and B.M. Ahmer, LuxR solos are becoming major players in cell–cell communication in bacteria. 2015, Frontiers Media SA. p. 89.
44. Cao, Z., Z. Liu, and X. Mao, Application of quorum sensing in metabolic engineering. Journal of Agricultural and Food Chemistry 2023, 71, 5062–5074. [CrossRef]
45. Min, B.E., et al., Optimization of industrial microorganisms: recent advances in synthetic dynamic regulators. Journal of Industrial Microbiology and Biotechnology 2017, 44, 89–98. [CrossRef]
46. Song, J. , et al., Self-regulated efficient production of L-threonine via an artificial quorum sensing system in engineered Escherichia coli. Microbiological Research 2024, 284, 127720. [Google Scholar] [CrossRef]
47. Wu, J. , et al., Developing a pathway-independent and full-autonomous global resource allocation strategy to dynamically switching phenotypic states. Nature Communications 2020, 11, 5521. [Google Scholar] [CrossRef] [PubMed]
48. Bäcker, L.E. , et al., Tuning and functionalization of logic gates for time resolved population programming. 2024.
49. Li, X., Q. Qi, and Q. Liang, Construction of cascade circuits for dynamic temporal regulation and its application to PHB production. Biotechnology for Biofuels and Bioproducts 2023, 16, 158. [Google Scholar] [CrossRef] [PubMed]
50. Qin, Z. , et al., Programming dissipation systems by DNA timer for temporally regulating enzyme catalysis and nanostructure assembly. ACS nano 2022, 16, 14274–14283. [Google Scholar] [CrossRef] [PubMed]
51. Siebert, D., J. Altenbuchner, and B. Blombach, A timed off-switch for dynamic control of gene expression in Corynebacterium glutamicum. Frontiers in Bioengineering and Biotechnology 2021, 9, 704681.
52. Yu, W. , et al., A pathway independent multi-modular ordered control system based on thermosensors and CRISPRi improves bioproduction in Bacillus subtilis. Nucleic Acids Research 2022, 50, 6587–6600. [Google Scholar] [CrossRef]
53. Cui, S. , et al., Multilayer genetic circuits for dynamic regulation of metabolic pathways. ACS Synthetic Biology 2021, 10, 1587–1597. [Google Scholar] [CrossRef]
54. Dinh, C.V. and K.L. Prather, Development of an autonomous and bifunctional quorum-sensing circuit for metabolic flux control in engineered Escherichia coli. Proceedings of the National Academy of Sciences 2019, 116, 25562–25568. [Google Scholar] [CrossRef]
55. Tian, J. , et al., Developing an endogenous quorum-sensing based CRISPRi circuit for autonomous and tunable dynamic regulation of multiple targets in Streptomyces. Nucleic Acids Research 2020, 48, 8188–8202. [Google Scholar] [CrossRef]
56. Wu, Y. , et al., Design of a programmable biosensor-CRISPRi genetic circuits for dynamic and autonomous dual-control of metabolic flux in Bacillus subtilis. Nucleic acids research 2020, 48, 996–1009. [Google Scholar] [CrossRef]
57. Lopez-Garcia, C.L. , Synthetic biology approaches for the construction of improved microbial cell factories. 2021, Iowa State University.
58. Lv, X., et al., New synthetic biology tools for metabolic control. Current Opinion in Biotechnology 2022, 76, 102724.
59. Sarath, R. , et al., Metabolic Engineering Approaches for Bioenergy Production, in Applied Biotechnology for Emerging Pollutants Remediation and Energy Conversion. 2023, Springer. p. 305-332.
60. Zou, Y. , et al., A self-regulated network for dynamically balancing multiple precursors in complex biosynthetic pathways. Metabolic Engineering 2024, 82, 69–78. [Google Scholar] [CrossRef] [PubMed]
61. Leitão, A.L. and F.J. Enguita, Synthetic biology approaches for secondary metabolism engineering, in Microbial Cell Factories Engineering for Production of Biomolecules. 2021, Elsevier. p. 51-64.
62. Czárán, T. , et al., Cue-driven microbial cooperation and communication: evolving quorum sensing with honest signaling. BMC biology 2024, 22, 73. [Google Scholar] [CrossRef] [PubMed]
63. Fan, Q. , et al., Structure and signal regulation mechanism of interspecies and interkingdom quorum sensing system receptors. Journal of agricultural and food chemistry 2022, 70, 429–445. [Google Scholar] [CrossRef] [PubMed]
64. Jin, Y. , et al., Constructions of quorum sensing signaling network for activated sludge microbial community. ISME communications 2024, 4, ycae018. [Google Scholar] [CrossRef] [PubMed]
65. Marken, J.P. and R.M. Murray, Addressable and adaptable intercellular communication via DNA messaging. Nature Communications 2023, 14, 2358. [CrossRef]
66. Wu, S. , et al., Deciphering and Constructing the Quorum Sensing Language “Interpreter” Ecosystem for Microbial Community. 2024.
67. Wu, S. , et al., Design and analysis of quorum sensing language “Interpreter” ecosystem for microbial community. Chemical Engineering Journal 2024, 496, 153148. [Google Scholar] [CrossRef]
68. Wu, X. , et al., Communication mediated interaction between bacteria and microalgae advances photogranulation. Science of The Total Environment 2024, 914, 169975. [Google Scholar] [CrossRef]
69. Gorochowski, T.E., et al., Toward engineering biosystems with emergent collective functions. Frontiers in bioengineering and biotechnology 2020, 8, 705.
70. Karkaria, B.D., et al., From microbial communities to distributed computing systems. Frontiers in Bioengineering and Biotechnology 2020, 8, 834.
71. Kylilis, N. , et al., Tools for engineering coordinated system behaviour in synthetic microbial consortia. Nature communications 2018, 9, 2677. [Google Scholar] [CrossRef]
72. Li, S., et al., Synthetic microbial consortia with programmable ecological interactions. Methods in Ecology and Evolution 2022, 13, 1608–1621. [CrossRef]
73. Rodríguez Amor, D. and M. Dal Bello, Bottom-up approaches to synthetic cooperation in microbial communities. Life 2019, 9, 22. [Google Scholar] [CrossRef] [PubMed]
74. Zhang, J., et al., Engineering microbial consortia through synthetic biology approach. Sheng wu Gong Cheng xue bao= Chinese Journal of Biotechnology 2023, 39, 2517–2545.
75. Bonidia, R.P. and A.C.P. de Leon Ferreira. BioAutoML: Democratizing Machine Learning in Life Sciences. in Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS). 2024. SBC.
76. Ding, N. , et al., AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors. Molecules, 2024. 29(15).
77. Goshisht, M.K. , Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges. ACS omega 2024, 9, 9921–9945. [Google Scholar] [CrossRef] [PubMed]
78. Merzbacher, C. and D.A. Oyarzún, Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochemical Society Transactions 2023, 51, 1871–1879. [CrossRef]
79. Murala, P. and K. Sivaraman. Data science Analysis Using Deep Learning Techniques in Mining Biological Data. in 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). 2024. IEEE.
80. Soni, M. , et al., A comprehensive analysis of the convergence between deep learning technologies and bioinformatics, catalyzing groundbreaking innovations in biological data interpretation, in Explainable Artificial Intelligence for Biomedical and Healthcare Applications. 2024, CRC Press. p. 211-229.
81. Valeri, J.A. , et al., BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences. Cell Systems 2023, 14, 525–542. [Google Scholar] [CrossRef]
82. Li, X. , et al., Synthetic neural-like computing in microbial consortia for pattern recognition. Nature communications 2021, 12, 3139. [Google Scholar] [CrossRef]
83. Mahmud, M., et al., Deep learning in mining biological data. Cognitive computation 2021, 13, 1–33. [CrossRef]
84. Min, S., B. Lee, and S. Yoon, Deep learning in bioinformatics. Briefings in bioinformatics 2017, 18, 851–869.
85. Sameer, A.A.S.a.R. , Artificial Intelligence Techniques in Bioinformatics: Unravelling Complex Biological Systems. International Journal of Advanced Research in Science, Communication and Technology 2023. 3(1): p. 269-275.
86. Tawseef Ahmed Teli, F.S.M. , Zubair Masoodi, Application of ML and DL on Biological Data, in Applications of Machine Learning and Deep Learning on Biological Data. 2023, Auerbach Publications eBooks. p. 159-180.
87. Vakilipoor, F., et al. Hybrid deep learning-based feature-augmented detection for molecular communication systems. in Proceedings of the 9th ACM International Conference on Nanoscale Computing and Communication. 2022.
88. Weiss, R. , et al., Applications of neural networks in biomedical data analysis. Biomedicines 2022, 10, 1469. [Google Scholar] [CrossRef]
89. Bartoli, V., M. di Bernardo, and T.E. Gorochowski, Self-adaptive biosystems through tunable genetic parts and circuits. Current Opinion in Systems Biology 2020, 24, 78–85. [CrossRef]
90. Bhattacharya, P., K. Raman, and A.K. Tangirala, A generic systems-theoretic approach to identify biological networks capable of adaptation. bioRxiv, 2021: p. 2021.05. 27.445914.
91. Bhattacharya, P., K. Raman, and A.K. Tangirala, On biological networks capable of robust adaptation in the presence of uncertainties: A linear systems-theoretic approach. Mathematical Biosciences 2023, 358, 108984. [Google Scholar] [CrossRef] [PubMed]
92. Dasauni, K., D. Singh, and T.K. Nailwal, Understanding the molecular and genetics determinants of microbial adaptation in changing environment, in Microbiome Under Changing Climate. 2022, Elsevier. p. 333-352.
93. Prakash, S. , et al., Engineered sensor bacteria evolve master-level gameplay through accelerated adaptation. bioRxiv, 2022: p. 2022.04. 22.489191.
94. Rattray, J.B. , et al., The dynamic response of quorum-sensing to density is robust to signal supplementation and signal synthase knockouts. bioRxiv, 2022: p. 2022.09. 12.507654.
95. Preiser, R. , Complex adaptive systems, in Elgar Encyclopedia of Interdisciplinarity and Transdisciplinarity, F. Darbellay, Editor. 2024. p. 86–90.
96. Alamnie, G. and B. Andualem, Biosafety Issues of Unintended Horizontal Transfer of Recombinant DNA, in Genetic Transformation in Crops. 2020, IntechOpen.
97. Cimolato, C. , et al., Exploring alternative quorum sensing model structures and quorum quenching strategies. bioRxiv, 2023: p. 2023.07. 07.548074.
98. Haudiquet, M. , et al., Selfish, promiscuous and sometimes useful: how mobile genetic elements drive horizontal gene transfer in microbial populations. Philosophical Transactions of the Royal Society B 2022, 377, 20210234. [Google Scholar] [CrossRef] [PubMed]
99. Ma, Y., A. Manna, and T.S. Moon, Advances in engineering genetic circuits for microbial biocontainment. Current Opinion in Systems Biology, 2023: p. 100483.
100. Qiu, X. , et al., Regulation of quorum sensing for the manipulation of conjugative transfer of antibiotic resistance genes in wastewater treatment system. Water Research 2024, 253, 121222. [Google Scholar] [CrossRef] [PubMed]
101. Rycroft, T. , et al., A quantitative risk assessment method for synthetic biology products in the environment. Science of the Total Environment 2019, 696, 133940. [Google Scholar] [CrossRef] [PubMed]
102. Sarangi, R., et al., Horizontal gene transfer: Implications in human health and diseases. Journal of Integrative Medicine and Research 2023, 1, 13–18. [CrossRef]
103. Vidiella, B. and R. Solé, Ecological firewalls for synthetic biology. Iscience, 2022. 25(7).
104. Zhu, X. , et al., Innovative microbial disease biocontrol strategies mediated by quorum quenching and their multifaceted applications: A review. Frontiers in Plant Science 2023, 13, 1063393. [Google Scholar] [CrossRef]
105. Ding, Q. and L. Liu, Reprogramming cellular metabolism to increase the efficiency of microbial cell factories. Critical Reviews in Biotechnology 2024, 44, 892–909. [Google Scholar] [CrossRef]
106. Kugler, A. and K. Stensjö, Machine learning predicts system-wide metabolic flux control in cyanobacteria. Metabolic engineering 2024, 82, 171–182. [Google Scholar] [CrossRef]
107. Lu, H. , et al., Cell factory design with advanced metabolic modelling empowered by artificial intelligence. Metabolic Engineering, 2024.
108. Parker, B. , How can we design robust manufacturing metabolisms and reconsider the bioreactor? Research Directions: Biotechnology Design 2023, 1, e3. [Google Scholar] [CrossRef]
109. Phaneuf, P. , et al., Data-Driven Strain Design Towards Mitigating Biomanufacturing Stresses. bioRxiv, 2023: p. 2023.09. 17.558093.
110. Ryu, G., et al., Deep learning for metabolic pathway design. Metabolic Engineering 2023, 80, 130–141. [CrossRef] [PubMed]
111. Srikanth, M. and M. Bhanurangarao, Deep learning approaches for predictive modeling and optimization of metabolic fluxes in engineered microorganism. International Journal of Research in Science &Amp, 2023: p. 1-11.
112. Adugna, T., A. Ramu, and A. Haldorai, A review of pattern recognition and machine learning. J. Machine Comput 2024, 4, 210–220.
113. de Ridder, D., J. De Ridder, and M.J. Reinders, Pattern recognition in bioinformatics. Briefings in bioinformatics 2013, 14, 633–647. [CrossRef] [PubMed]
114. Hense, B., C. Kuttler, and J. Müller, Functionality of Autoinducer Systems in Complex Environments, in The Physical Basis of Bacterial Quorum Communication. 2014, Springer. p. 83-103.
115. Li, X., et al. Application Methods of Artificial Neural Network in Pattern Recognition. in 2020 13th International Conference on Intelligent Computation Technology and Automation (ICICTA). 2020. IEEE.
116. Singh, C. , Machine Learning in Pattern Recognition. European Journal of Engineering and Technology Research, 2023. 8(2).
117. Nidhi Pateriya, N.T. , Gulafsha Anjum, A Review Paper on Machine Learning in Pattern Recognition. International Journal of Innovative Research in Computer and Communication Engineering 2024, 12, 86–90. [Google Scholar] [CrossRef]
118. Wu, S. , et al., Quorum sensing for population-level control of bacteria and potential therapeutic applications. Cellular and Molecular Life Sciences 2020, 77, 1319–1343. [Google Scholar] [CrossRef]
119. Xiong, F. , et al., Enhanced AHL-mediated quorum sensing accelerates the start-up of biofilm reactors by elevating the fitness of fast-growing bacteria in sludge and biofilm communities. Water Research 2024, 257, 121697. [Google Scholar] [CrossRef]
120. Ford, G., et al. Unknown signal detection in interference and noise using hidden Markov models. in 2021 IEEE Statistical Signal Processing Workshop (SSP). 2021. IEEE.
121. Silva, K.P.T. and J.Q. Boedicker, A neural network model predicts community-level signaling states in a diverse microbial community. PLoS Computational Biology 2019, 15, e1007166. [Google Scholar] [CrossRef]
122. Wang, M. and Q. Tu, Effective data filtering is prerequisite for robust microbial association network construction. Frontiers in Microbiology 2022, 13, 1016947. [Google Scholar]
123. Ankita, D. Jain, C.M.H., Noise reduction using machine learning, B. Corporation, Editor. 2019.
124. Bravo-Frank, N. , et al., Realtime bacteria detection and analysis in sterile liquid products using deep learning holographic imaging. npj Biosensing 2024, 1, 8. [Google Scholar] [CrossRef]
125. Butcher, R.J. and J.J. Tabor, Real-time detection of response regulator phosphorylation dynamics in live bacteria. Proceedings of the National Academy of Sciences 2022, 119, e2201204119. [CrossRef]
126. Chen, N. , et al., Real-Time Monitoring of Dynamic Chemical Processes in Microbial Metabolism with Optical Sensors. Chinese Journal of Chemistry 2023, 41, 1836–1840. [Google Scholar] [CrossRef]
127. Jiang, W. and J. Shan. Design of Real-Time Detection System of Bacteria Concentration Changes in Biological Fermentation. in Advanced Hybrid Information Processing: Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21–22, 2019, Proceedings, Part II. 2019. Springer.
128. Mukherjee, S. and B.L. Bassler, Bacterial quorum sensing in complex and dynamically changing environments. Nature Reviews Microbiology 2019, 17, 371–382. [CrossRef] [PubMed]
129. Remya, S. and T. Anjali, An intelligent and optimal deep learning approach in sensor based networks for detecting microbes. IEEE Sensors Journal, 2023.
130. Sharma, S. and L. Tharani, Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning. Science Progress 2024, 107, 00368504231223029. [Google Scholar] [CrossRef] [PubMed]
131. Sun Li, C.L. , Real-time signal sequence detection method based on deep learning. 2020.
132. Abdul Subhahan Shaik, N.S., Dr. C. Krishna Priya, Predictive Modeling in Remote Sensing Using Machine Learning Algorithms. International Journal of Current Science Research and Review 2024, 7, 4116–4123.
133. Baron, M. , et al., Use of a systems-biology informed machine learning model to predict drug response using clinically available NGS data. 2023, American Society of Clinical Oncology.
134. Chango, D.A.C., A.D.P. Anchatipán, and F.R.R. Bedón, Análisis del Uso de Machine Learning para Sistema de control predictivo a nivel industrial. Polo del Conocimiento 2024, 9, 1023–1040. [CrossRef]
135. Karanam, V.S.S.L. and B. Ramamurthy. Improving Transfer Time Prediction of ML Models via Auto-correcting Dynamical Systems Modeling. in 2024 IEEE 10th International Conference on Network Softwarization (NetSoft). 2024. IEEE.
136. King, R. , A machine learning approach to predict gene expression levels based on stochastic simulation. 2024.
137. Toma, M. and O.C. Wei, Predictive modeling in medicine. Encyclopedia 2023, 3, 590–601. [CrossRef]
138. Zaidi, A.R. , et al., Developing MLP based prediction system for anticancer drug response using hybrid features of genomics and cheminformatics. 2024.
139. Al Taweraqi, N. and R.D. King, Improved prediction of gene expression through integrating cell signalling models with machine learning. BMC bioinformatics 2022, 23, 323. [Google Scholar] [CrossRef]
140. Huiqing Wang, C.L. , Chunlin Dong, Gene expression prediction method based on LSTM loop neural network. 2018.
141. Kamal, I.M., N.A. Wahid, and H. Bae. Gene expression prediction using stacked temporal convolutional network. in 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). 2020. IEEE.
142. Kumar, N.P. , et al., Design of optimal Elman Recurrent Neural Network based prediction approach for biofuel production. Scientific Reports 2023, 13, 8565. [Google Scholar]
143. Lugagne, J.-B., C.M. Blassick, and M.J. Dunlop, Deep model predictive control of gene expression in thousands of single cells. Nature Communications 2024, 15, 2148. [CrossRef]
144. Mateescu, C., et al. Artificial intelligence approach in predicting biomass-to-biofuels conversion performances. in 2022 23rd International Carpathian Control Conference (ICCC). 2022. IEEE.
145. Wang, H., et al., A new LSTM-based gene expression prediction model: L-GEPM. Journal of Bioinformatics and Computational Biology 2019, 17, 1950022.
146. Agarwal, N. , et al., Artificial Intelligence and Machine Learning for Analysis of Multi-omics, in Multi-Omics Analysis of the Human Microbiome: From Technology to Clinical Applications. 2024, Springer. p. 339-354.
147. Firdous, A. , et al., Machine learning approaches for multiomics data analysis, in Biological Insights of Multi-Omics Technologies in Human Diseases. 2024, Elsevier. p. 311-338.
148. McElhinney, J.M. , et al., Interfacing machine learning and microbial omics: a promising means to address environmental challenges. Frontiers in Microbiology 2022, 13, 851450. [Google Scholar] [CrossRef] [PubMed]
149. Rao, B.S., et al. A Novel Machine Learning Approach of Multi-omics Data Prediction. in 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). 2022. IEEE.
150. Shi, Z. , et al., Data-driven synthetic cell factories development for industrial biomanufacturing. BioDesign Research, 2022. 2022.
151. Wu, L. , et al., Data-driven prediction of colonization outcomes for complex microbial communities. Nature Communications 2024, 15, 2406. [Google Scholar] [CrossRef] [PubMed]
152. Zitnik, M. , et al., Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities. Information Fusion 2019, 50, 71–91. [Google Scholar] [CrossRef] [PubMed]
153. Aboagyea, E.A. , et al., Machine Learning Methods for the Forecasting of Environmental Impacts in Early-stage Process Design. 2024.
154. Amato, F. , et al., A novel framework for spatio-temporal prediction of environmental data using deep learning. Scientific reports 2020, 10, 22243. [Google Scholar] [CrossRef]
155. Deb, S. , et al., Machine learning methods trained on simple models can predict critical transitions in complex natural systems. Royal Society Open Science 2022, 9, 211475. [Google Scholar] [CrossRef]
156. Kolesnikova, K., et al. Use the neural networks in prediction of environmental processes. in 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST). 2024. IEEE.
157. Mitchell, A. , et al., Adaptive prediction of environmental changes by microorganisms. Nature 2009, 460, 220–224. [Google Scholar] [CrossRef]
158. Volkova, E., S. Muratchaev, and A. Volkov. Development of a Automated Environmental Monitoring System with Forecasting. in Futuristic Trends in Network and Communication Technologies: Third International Conference, FTNCT 2020, Taganrog, Russia, October 14–16, 2020, Revised Selected Papers, Part I 3. 2021. Springer.
159. Yang, J., et al. Prediction of temperature change with multi-dimensional environmental characteristic based on CNN-LSTM-ATTENTION model. in 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 2022. IEEE.
160. Caldas, R.D., et al. A hybrid approach combining control theory and AI for engineering self-adaptive systems. in Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. 2020.
161. Harris, A.W., et al., Designing genetic feedback controllers. IEEE Transactions on Biomedical Circuits and Systems 2015, 9, 475–484. [CrossRef]
162. Zile, M. , Intelligent and Adaptive Control, in Microgrid Architectures, Control and Protection Methods. 2020, Springer, Cham. p. 423-446.
163. Zhou, Z. , Design and analysis of adaptive control systems: Adaptive control algorithms using machine learning. Applied and Computational Engineering 2024, 39, 51–56. [Google Scholar] [CrossRef]
164. de Avila Ferreira, T., et al. Real-time optimization of uncertain process systems via modifier adaptation and gaussian processes. in 2018 European Control Conference (ECC). 2018. IEEE.
165. Morais, M.S. and A. Araujo, Real-time optimization strategy of the alcoholic fermentation process using metamodeling. 2023.
166. Williams, T. , et al., Machine learning and metabolic modelling assisted implementation of a novel process analytical technology in cell and gene therapy manufacturing. Scientific Reports 2023, 13, 834. [Google Scholar] [CrossRef]
167. Bauer, K. , et al., Feedback loops in machine learning: A study on the interplay of continuous updating and human discrimination. Journal of the Association for Information Systems 2024, 25, 804–866. [Google Scholar] [CrossRef]
168. Dietrich, J. , A Methodology for Vertical Translation Between Molecular and Organismal Level in Biological Feedback Loops. bioRxiv, 2021: p. 2021.09. 20.461028.
169. Gesmundo, A., A continual development methodology for large-scale multitask dynamic ML systems. arXiv preprint arXiv:2209.07326, 2022.
170. Pithan, L. , et al., Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments. Journal of synchrotron radiation, 2023. 30(6).
171. Wang, T. , et al., ML-NPI: predicting interactions between noncoding RNA and protein based on meta-learning in a large-scale dynamic graph. Journal of Chemical Information and Modeling 2023, 64, 2912–2920. [Google Scholar] [CrossRef] [PubMed]
172. Zhou, J., R. Ramanathan, and W.-F. Wong, Synthesis of the Dynamical Properties of Feedback Loops in Bio-Pathways. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2019, 18, 1217–1226.
173. Borri, A., M. d'Angelo, and P. Palumbo, Self-regulation in a stochastic model of chemical self-replication. International Journal of Robust and Nonlinear Control 2023, 33, 4908–4922. [CrossRef]
174. Espinel-Ríos, S., et al., Machine learning-supported cybergenetic modeling, optimization and control for synthetic microbial communities, in Computer Aided Chemical Engineering. 2023, Elsevier. p. 2601-2606.
175. Ji, Y., T. Chakraborty, and S.V. Wegner, Self-regulated and bidirectional communication in synthetic cell communities. ACS nano 2023, 17, 8992–9002. [CrossRef]
176. Archisman Bhunia, K.N. , Abhilasha Singh, Asmeeta Sircar, Nivedita Chatterjee, Quorum Sensing, in Omics for Environmental Engineering and Microbiology Systems. 2022, CRC Press.
177. Escobar-Muciño, E., M.M. Arenas-Hernández, and M.L. Luna-Guevara, Mechanisms of Inhibition of Quorum Sensing as an Alternative for the Control of E. coli and Salmonella. Microorganisms 2022, 10, 884. [CrossRef]
178. Huang, S. , et al., Insights into adaptive mechanisms of extreme acidophiles based on quorum sensing/quenching-related proteins. Msystems 2022, 7, e01491–21. [Google Scholar] [CrossRef]
179. Moreno-Gámez, S., M.E. Hochberg, and G. Van Doorn, Quorum sensing as a mechanism to harness the wisdom of the crowds. Nature communications 2023, 14, 3415. [CrossRef]
180. Shen, Y. , et al., New antibacterial targets: Regulation of quorum sensing and secretory systems in zoonotic bacteria. Microbiological Research 2023, 274, 127436. [Google Scholar] [CrossRef]
181. Ampomah-Wireko, M. , et al., Chemical probe of AHL modulators on quorum sensing in Gram-Negative Bacteria and as antiproliferative agents: A review. European Journal of Medicinal Chemistry 2021, 226, 113864. [Google Scholar] [CrossRef]
182. Bazhenov, S. , et al., Influence of the luxR regulatory gene dosage and expression level on the sensitivity of the whole-cell biosensor to acyl-homoserine lactone. Biosensors 2021, 11, 166. [Google Scholar] [CrossRef]
183. Dominelli, N. , et al., Interkingdom Signaling of the Insect Pathogen Photorhabdus luminescens with Plants Via the LuxR solo SdiA. Microorganisms 2023, 11, 890. [Google Scholar] [CrossRef] [PubMed]
184. Gonzales, M. , et al., AHL-Based Quorum Sensing Regulates the Biosynthesis of a Variety of Bioactive Molecules in Bacteria. Journal of Natural Products 2024, 87, 1268–1284. [Google Scholar] [CrossRef] [PubMed]
185. Li, S. , et al., Characterization of differentiated autoregulation of LuxI/LuxR-type quorum sensing system in Pseudoalteromonas. Biochemical and Biophysical Research Communications 2022, 590, 177–183. [Google Scholar] [CrossRef]
186. Liu, L. , et al., AHL-mediated quorum sensing to regulate bacterial substance and energy metabolism: A review. Microbiological Research 2022, 262, 127102. [Google Scholar] [CrossRef]
187. Zeng, M. , et al., Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators. ACS Synthetic Biology 2023, 13, 282–299. [Google Scholar] [CrossRef] [PubMed]
188. Ábrahám, Á. , et al., Single-cell level LasR-mediated quorum sensing response of Pseudomonas aeruginosa to pulses of signal molecules. Scientific Reports 2024, 14, 16181. [Google Scholar] [CrossRef] [PubMed]
189. Ballante, F. , et al., Modified N-acyl-L-homoserine lactone compounds abrogate Las-dependent quorum-sensing response in human pathogen Pseudomonas aeruginosa. Frontiers in Molecular Biosciences 2023, 10, 1264773. [Google Scholar] [CrossRef]
190. de Oliveira Pereira, T., M. -C. Groleau, and E. Déziel, Surface growth of Pseudomonas aeruginosa reveals a regulatory effect of 3-oxo-C12-homoserine lactone in the absence of its cognate receptor, LasR. Mbio 2023, 14, e00922–23. [Google Scholar] [CrossRef]
191. Manson, D.E. , et al., Abiotic small molecule inhibitors and activators of the LasR quorum sensing receptor in Pseudomonas aeruginosa with potencies comparable or surpassing N-acyl homoserine lactones. ACS infectious diseases 2024, 10, 1212–1221. [Google Scholar] [CrossRef]
192. Mohan, M.S. , et al., Attenuation of Las/Rhl quorum sensing regulated virulence and biofilm formation in Pseudomonas aeruginosa PAO1 by Artocarpesin. Microbial Pathogenesis 2024, 189, 106609. [Google Scholar] [CrossRef]
193. Rex, D.A.B. , et al., Pleotropic potential of quorum sensing mediated N-acyl homoserine lactones (AHLs) at the LasR and RhlR receptors of Pseudomonas aeruginosa. Structural Chemistry 2023, 34, 1327–1339. [Google Scholar] [CrossRef]
194. Singothu, S. and V. Bhandari, Computational assessment of marine natural products as LasR inhibitors for attenuating quorum sensing in Pseudomonas aeruginosa. Journal of Biomolecular Structure and Dynamics, 2024: p. 1-15.
195. Eisenbraun, E.L. , et al., Synthetic Peptides Capable of Potent Multigroup Staphylococcal Quorum Sensing Activation and Inhibition in Both Cultures and Biofilm Communities. Journal of the American Chemical Society, 2024.
196. Fang, L., C. Cosgriff, and F. Alonzo III, Determinants of maturation of the Staphylococcus aureus autoinducing peptide. Journal of Bacteriology 2024, 206, e00195–24.
197. Inagaki, R. , et al., Eliminating extracellular autoinducing peptide signals inhibits the Staphylococcus aureus quorum sensing agr system. Biochemical and Biophysical Research Communications 2024, 711, 149912. [Google Scholar] [CrossRef] [PubMed]
198. Liu, X. , et al., AgrA directly binds to the promoter of vraSR and downregulates its expression in Staphylococcus aureus. Antimicrobial Agents and Chemotherapy 2024, 68, e00893–23. [Google Scholar] [CrossRef] [PubMed]
199. Milly, T.A. and Y. Tal-Gan, Targeting peptide-based quorum sensing systems for the treatment of gram-positive bacterial infections. Peptide Science 2023, 115, e24298. [Google Scholar] [CrossRef]
200. Stock, M.R. , et al., Characterization of MroQ-dependent maturation and export of the Staphylococcus aureus accessory gene regulatory system autoinducing peptide. Infection and Immunity 2022, 90, e00263–22. [Google Scholar] [CrossRef]
201. Williams, P. , et al., Quorum-sensing, intra-and inter-species competition in the staphylococci. Microbiology 2023, 169, 001381. [Google Scholar] [CrossRef]
202. Wittekind, M.A. , et al., The Small Protein ScrA Influences Staphylococcus aureus Virulence-Related Processes via the SaeRS System. Microbiology Spectrum 2023, 11, e05255–22. [Google Scholar] [CrossRef]
203. Zhou, J. , et al., The Functional Study of Response Regulator ArlR Mutants in Staphylococcus Aureus. Applied Biochemistry and Biotechnology, 2024: p. 1-16.
204. Anju, S. , et al., Novel insights on the bacillus quorum sensing mechanism: Its role in competence, virulence, sporulation and biofilm formation. Implication of quorum sensing system in biofilm formation and virulence, 2018: p. 313-327.
205. Auchtung, J.M. and A.D. Grossman, Extracellular Peptide Signaling and Quorum Responses in Development, Self-Recognition, and Horizontal Gene Transfer in Bacillus subtilis. Chemical communication among bacteria, 2008: p. 13-30.
206. Dandach, S.H. and M. Khammash. Stochastic strategies for survival: bacterial competence in Bacillus Subtilis. in Proceedings of the 2011 American Control Conference. 2011. IEEE.
207. Gupta, R. , et al., Competence and Sporulation in Bacillus subtilis. Fundamentals of Bacterial Physiology and Metabolism, 2021: p. 653-670.
208. Kumar, A. and T.R. Singh, A quantitative study of gene regulatory pathways in Bacillus subtilis for virulence and competence phenotype by quorum sensing. Systems and synthetic biology 2013, 7, 33–39. [Google Scholar] [CrossRef]
209. Muers, M. , Racing to decide. Nature Reviews Microbiology 2012, 10, 84–84. [Google Scholar] [CrossRef]
210. Rai, N., R. Rai, and K. Venkatesh, Quorum sensing in competence and sporulation, in Quorum sensing vs quorum quenching: a battle with no end in sight. 2014, Springer. p. 61-64.
211. Schultz, D., et al., Deciding fate in adverse times: sporulation and competence in Bacillus subtilis. Proceedings of the National Academy of Sciences 2009, 106, 21027–21034. [CrossRef] [PubMed]
212. Xi, H., L. Duan, and M. Turcotte, Point-cycle bistability and stochasticity in a regulatory circuit for Bacillus subtilis competence. Mathematical Biosciences 2013, 244, 135–147. [Google Scholar] [CrossRef] [PubMed]
213. Combarnous, Y. and T.M.D. Nguyen, Cell communications among microorganisms, plants, and animals: origin, evolution, and interplays. International Journal of Molecular Sciences 2020, 21, 8052. [CrossRef] [PubMed]
214. Gamby, S. , et al., Altering the communication networks of multispecies microbial systems using a diverse toolbox of AI-2 analogues. ACS chemical biology 2012, 7, 1023–1030. [Google Scholar] [CrossRef]
215. Guo, L., X. He, and W. Shi, Intercellular communications in multispecies oral microbial communities. Frontiers in microbiology 2014, 5, 328.
216. Mohana Sheela, G. , et al., Intra and inter-species communication in microbes: living with complex and sociable neighbors. Implication of quorum sensing system in biofilm formation and virulence, 2018: p. 7-16.
217. Pereira, C.S., J.A. Thompson, and K.B. Xavier, AI-2-mediated signalling in bacteria. FEMS microbiology reviews 2013, 37, 156–181. [CrossRef]
218. Pierson III, L.S., R. M. Maier, and I.L. Pepper, Microbial communication: bacteria/bacteria and bacteria/host, in Environmental Microbiology. 2015, Elsevier. p. 461-481.
219. Rodrigues, M.V. , et al., Synthesis and Potential of Autoinducer-2 and Analogs to Manipulate Inter-Species Quorum Sensing. Israel Journal of Chemistry, 2023. 63(5-6): p. e202200091.
220. Scott, S.R. and J. Hasty, Quorum sensing communication modules for microbial consortia. ACS synthetic biology 2016, 5, 969–977. [CrossRef]
221. Sedlmayer, F. , et al., Designer cells programming quorum-sensing interference with microbes. Nature communications 2018, 9, 1822. [Google Scholar] [CrossRef]
222. Li, X. , et al., Quantifying the optimal strategy of population control of quorum sensing network in Escherichia coli. Npj Systems Biology and Applications 2021, 7, 35. [Google Scholar] [CrossRef]
223. Walton, S.J., S. E. Clamons, and R.M. Murray, Analysis of Circuits for Dosage Control in Microbial Populations. bioRxiv, 2020: p. 2020.12. 18.423556.
224. XU, M. XU, M., et al., 合成生物学应用于微生物群体感应的研究进展. SCIENTIA SINICA Vitae 2023, 53, 64–81. [Google Scholar] [CrossRef]
225. Duncker, K.E., Z.A. Holmes, and L. You, Engineered microbial consortia: strategies and applications. Microbial Cell Factories 2021, 20, 1–13.
226. Liu, Y. , et al., Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform. Biotechnology Advances, 2024: p. 108348.
227. Matuszyńska, A. , et al., A new era of Synthetic Biology-microbial community design. Synthetic Biology, 2024: p. ysae011.
228. Mittermeier, F., et al., Artificial microbial consortia for bioproduction processes. Engineering in life sciences 2023, 23, e2100152.
229. Stephens, K. , et al., Bacterial co-culture with cell signaling translator and growth controller modules for autonomously regulated culture composition. Nature communications 2019, 10, 4129. [Google Scholar] [CrossRef] [PubMed]
230. Wang, L., et al., Engineering consortia by polymeric microbial swarmbots. Nature Communications 2022, 13, 3879. [CrossRef] [PubMed]
231. Blöbaum, L. , et al., Quantifying microbial robustness in dynamic environments using microfluidic single-cell cultivation. Microbial Cell Factories 2024, 23, 44. [Google Scholar] [CrossRef]
232. Kashyap, A., W. Wang, and B.A. Camley, Trade-offs in concentration sensing in dynamic environments. Biophysical Journal 2024, 123, 1184–1194. [CrossRef]
233. Lynch, M.R. , Environmental Sensing, in Evolutionary Cell Biology: The Origins of Cellular Architecture. 2024, Oxford University Press. p. 570–600.
234. Mahdavi, S. , et al., Flexibility and sensitivity in gene regulation out of equilibrium. bioRxiv, 2023: p. 2023.04. 11.536490.
235. Cavraro, G. , Advocating Feedback Control for Human-Earth System Applications. IEEE Control Systems Letters, 2024.
236. Hu, G. and F. You, AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory. Applied Energy 2024, 356, 122334. [Google Scholar] [CrossRef]
237. M. Zand, A. M. Zand, A., et al., Multi-Layer Autocatalytic Feedback Enables Integral Control Amidst Resource Competition and Across Scales. bioRxiv, 2024: p. 2024.08. 22.609155.
238. Rino, T., F. Giannino, and D. Fiore. Feedback control of plant-soil autotoxicity via pulse-width modulation. in 2024 European Control Conference (ECC). 2024. IEEE.
239. Wang, H. , et al., A feedback control method for plant factory environment based on photosynthetic rate prediction model. Computers and Electronics in Agriculture 2023, 211, 108007. [Google Scholar] [CrossRef]
240. Wang, W., et al., Online Feedback Optimization over Networks: A Distributed Model-free Approach. arXiv preprint arXiv:2403.19834, 2024.
241. Babaniyi, G.G., U. J. Akor, and B.R. Babaniyi, Anti-quorum Sensing Therapies: Issues and Limitations. 2023.
242. Banerjee, A. , et al., Quorum Sensing and Environmental Sustainability. Microbial Biotechnology: Role in Ecological Sustainability and Research, 2022: p. 75-88.
243. Dubern, J.-F. , et al., Growth rate and nutrient limitation as key drivers of extracellular quorum sensing signal molecule accumulation in Pseudomonas aeruginosa. Microbiology 2023, 169, 001316. [Google Scholar] [CrossRef]
244. Tripathi, S. , et al., Quorum sensing-a promising tool for degradation of industrial waste containing persistent organic pollutants. Environmental Pollution 2022, 292, 118342. [Google Scholar] [CrossRef]
245. Ceroni, F., et al., Burden-driven feedback control of gene expression. Nature methods 2018, 15, 387–393. [CrossRef] [PubMed]
246. Nikita, S., et al., Advances in bioreactor control for production of biotherapeutic products. Biotechnology and Bioengineering 2023, 120, 1189–1214. [CrossRef] [PubMed]
247. Pang, W.L. , Quantitative analysis of genetic expression responses to dynamic microenvironmental perturbation. 2007, UC San Diego.
248. Boo, A. , et al., Host-aware RNA-based control of synthetic microbial consortia. bioRxiv, 2023: p. 2023.05. 15.540816.
249. Daniel, R., V. Kravchik, and N. Geva-Zatorsky, Utilizing Incoherent Feedforward Loops for Enhanced Response Stability and Coordination of Microbial Consortia-Based Biosensors. 2023.
250. Kang, C.W. , et al., Circuit-guided population acclimation of a synthetic microbial consortium for improved biochemical production. Nature communications 2022, 13, 6506. [Google Scholar] [CrossRef] [PubMed]
251. Lee, T.A. , et al., Cybernetic control of a natural microbial co-culture. bioRxiv, 2024: p. 2024.07. 04.602068.
252. Martinelli, V., et al., Multicellular pd control in microbial consortia. IEEE Control Systems Letters 2023, 7, 2641–2646. [CrossRef]
253. VanArsdale, E. , et al., Electrogenetic signaling and information propagation for controlling microbial consortia via programmed lysis. Biotechnology and Bioengineering 2023, 120, 1366–1381. [Google Scholar] [CrossRef]
254. Hoekstra, N.D. , Characterization of a novel quorum quenching enzyme and determination of autoinducer signal receptor specificity. 2021, University of Minnesota.
255. Müller, I.E., et al., Gene networks that compensate for crosstalk with crosstalk. Nature Communications 2019, 10, 4028. [CrossRef]
256. Munir, S. , et al., Quorum sensing interfering strategies and their implications in the management of biofilm-associated bacterial infections. Brazilian Archives of Biology and Technology 2020, 63, e20190555. [Google Scholar] [CrossRef]
257. Sanders, J. , et al., Jump-start and push-start: Mutual activation through crosstalk in coupled quorum sensing pathways. bioRxiv, 2023: p. 2023.05. 08.539885.
258. Sanders, J.G. , et al., Crosstalk enables mutual activation of coupled quorum sensing pathways through “jump-start” and “push-start” mechanisms. Scientific Reports 2023, 13, 19230. [Google Scholar] [CrossRef]
259. Wang, S., G.F. Payne, and W.E. Bentley, Quorum sensing communication: molecularly connecting cells, their neighbors, and even devices. Annual review of chemical and biomolecular engineering 2020, 11, 447–468. [CrossRef]
260. Wellington, S. and E.P. Greenberg, Quorum sensing signal selectivity and the potential for interspecies cross talk. MBio 2019, 10, 10–1128.
261. Liu, B. , et al., A portable regulatory RNA array design enables tunable and complex regulation across diverse bacteria. Nature Communications 2023, 14, 5268. [Google Scholar] [CrossRef] [PubMed]
262. Borchert, A.J., et al., Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440. Msystems 2024, 9, e00942–23.
263. Merzbacher, C., O. Mac Aodha, and D.A. Oyarzun, Modelling dynamic host-pathway interactions at the genome scale. bioRxiv, 2024: p. 2024.04. 09.588720.
264. R. Moimenta, A. R. Moimenta, A., et al., A continuous dynamic genome-scale model explains batch fermentations led by species of the Saccharomyces genus. bioRxiv, 2024: p. 2024.05. 03.592398.
265. Santos, A.P.A. , Exploration of non-coding RNA in complex microbial communities with machine learning. 2024, Universidade de São Paulo.
266. Wu, S. , et al., Potential of orthogonal and cross-talk quorum sensing for dynamic regulation in cocultivation. Chemical Engineering Journal 2022, 445, 136720. [Google Scholar] [CrossRef]
267. Alsiyabi, A., S. A. Shahid, and A. Al-Harrasi, An Automated Machine Learning Framework for Antimicrobial Resistance Prediction Through Transcriptomics. bioRxiv, 2024: p. 2024.06. 22.600223.
268. Koblitz, J. , et al., Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. bioRxiv, 2024: p. 2024.08. 12.607695.
269. Naga, N.G. , et al., It is the time for quorum sensing inhibition as alternative strategy of antimicrobial therapy. Cell Communication and Signaling 2023, 21, 1–14. [Google Scholar] [CrossRef] [PubMed]
270. Panjla, A. , et al., Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance. Chemistry–An Asian Journal, 2024: p. e202400102.
271. Thompson, J.C., V. M. Zavala, and O.S. Venturelli, Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions. PLOS Computational Biology 2023, 19, e1011436. [Google Scholar] [CrossRef]
272. Zhang, H. , et al., Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides. Journal of Advanced Research, 2024.
273. Fang, Y., et al., Molecular Communication for Quorum Sensing Inspired Cooperative Drug Delivery. IEEE Transactions on Molecular, Biological and Multi-Scale Communications 2023, 9, 100–105. [CrossRef]
274. Ilyas, H. , et al., Commands of Synthetic Biology to Modernize and Re-design the Biological Systems. Advancements in Life Sciences 2024, 11, 549–557. [Google Scholar] [CrossRef]
275. Wu, S. , et al., Machine learning aided construction of the quorum sensing communication network for human gut microbiota. Nature Communications 2022, 13, 3079. [Google Scholar] [CrossRef]
276. Kohyama, S. , et al., Machine learning-aided design and screening of an emergent protein function in synthetic cells. Nature Communications 2024, 15, 2010. [Google Scholar] [CrossRef]
277. Mafat, I.H. , et al., Exploring Machine Learning Applications in Chemical Production through Valorization of Biomass, Plastics, and Petroleum Resources: A Comprehensive Review. Journal of Analytical and Applied Pyrolysis, 2024: p. 106512.
278. Vaishali Jain, S.K.T. , Overview: machine learning, in Machine Learning: An Art of Computer Thinking. 2024: IIP Series. p. 130-144.
279. Munde, A. , The Machine Learning Pipeline, in Deep Learning Concepts in Operations Research. 2024, Auerbach Publications. p. 18.
280. Anders Hansson, M.A. , Supervised Learning, in Optimization for Learning and Control. 2023.
281. Emilian, R. Vankov, K.G., Supervised Learning, in Statistical Methods in Epilepsy. 2024, Chapman and Hall/CRC. p. 30.
282. Li, H. , Summary of Supervised Learning Methods, in Machine Learning Methods. 2024, Springer Nature Singapore: Singapore. p. 273-280.
283. Nagaraj, A. , Supervised Learning Algorithms, in COVID 19 – Monitoring with IoT Devices. 2023. p. 23-75.
284. Najjar, E. and A.M. Breesam, Supervised machine learning a brief survey of approaches. Al-Iraqia Journal for Scientific Engineering Research 2023, 2, 71–82.
285. Narula, P. , Analysis of Common Supervised Learning Algorithms Through Application. Advanced Computational Intelligence: An International Journa, 2023. 10(1/2/3): p. 29-48.
286. Takahashi, K. and L. Takahashi, Supervised Machine Learning, in Materials Informatics and Catalysts Informatics: An Introduction. 2024, Springer Nature Singapore: Singapore. p. 191-226.
287. Amin, S. , An Introduction to Reinforcement Learning and Its Application in Various Domains, in Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems. 2024, IGI Global. p. 1-25.
288. Han, I., et al., Monte Carlo and Temporal Difference Methods in Reinforcement Learning [AI-eXplained]. IEEE Computational Intelligence Magazine 2023, 18, 64–65. [CrossRef]
289. ISMAIL, A.S., et al., REINFORCEMENT LEARNING MODEL FOR FINDING OPTIMAL PATH. Journal of Duhok University 2023, 26, 489–501. [CrossRef]
290. Krichen, M. Deep Reinforcement Learning. in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). 2023.
291. Moussaoui, H.E.a., Nabil; Benslimane, Mohamed, Reinforcement Learning: A review. University of Bahrain Scientific Journals, 2023. 13(1).
292. Shaik, N. , et al., 5 Reinforcement learning, in Toward Artificial General Intelligence, C.d.A. Victor Hugo, R. Pethuru, and Y. Satya Prakash, Editors. 2024, De Gruyter: Berlin, Boston. p. 109-124.
293. Sumedha Sharma, T.J., Sanidhya Chaturvedi, Swapnil Saraswat, and R. Sharma, Advancements in Reinforcement Learning - An Overview. Journal of Analysis and Computation (JAC) 2023, 17, 263–274.
294. Sonal, A.R.G., Dr. Ashima Mehta, Advancements in Reinforcement Learning. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) 2024, 4, 127–132. [Google Scholar] [CrossRef]
295. AL-Gburi, A.F.J. , et al., Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search. Algorithms 2024, 17, 355. [Google Scholar] [CrossRef]
296. Anders Hansson, M.A. , Unsupervised Learning, in Optimization for Learning and Control. 2023: John Wiley & Sons, Inc.
297. Dowrick, J.M. , et al., Unsupervised machine learning highlights the challenges of subtyping disorders of gut-brain interaction. Neurogastroenterology & Motility, 2024: p. e14898.
298. Li, H. , Introduction to Unsupervised Learning, in Machine Learning Methods. 2024, Springer Nature Singapore: Singapore. p. 281-292.
299. Takahashi, K. and L. Takahashi, Unsupervised Machine Learning and Beyond Machine Learning, in Materials Informatics and Catalysts Informatics: An Introduction. 2023, Springer. p. 227-244.
300. Vinci, G. , Unsupervised Learning, in Statistical Methods in Epilepsy. 2024: Chapman and Hall/CRC. p. 21.
301. Watson, D.S. , On the philosophy of unsupervised learning. Philosophy & Technology 2023, 36, 28. [Google Scholar]
302. Khurana, D.C.a.R. , Unsupervised Learning, in Introduction to Machine Learning with Python. 2023. p. 103-112.
303. Hsieh, W.W. , 10 - Unsupervised Learning, in Introduction to Environmental Data Science. 2023. p. 330 - 371.
304. Das, P. , et al., Deep Learning Techniques Revolutionizing Biomedical Applications: Arrhythmia Detection, Cardiac Sensed Signals, and Cell-Free Synthetic Biology, in Applications of Synthetic Biology in Health, Energy, and Environment. 2023, IGI Global. p. 68-91.
305. Kumar, S., et al., Deep learning in computational biology: Advancements, challenges, and future outlook. arXiv preprint arXiv:2310.03086, 2023.
306. Mohanta, S.K. , et al., Deep Learning is a State-of-the-Art Approach to Artificial Intelligence, in Deep Learning Concepts in Operations Research. 2024, Auerbach Publications. p. 27-43.
307. Ravanmehr, R. and R. Mohamadrezaei, Deep Learning Overview, in Session-Based Recommender Systems Using Deep Learning. 2024, Springer Nature Switzerland: Cham. p. 27-72.
308. Shafik, W. , Deep Learning Impacts in the Field of Artificial Intelligence, in Deep Learning Concepts in Operations Research. 2024, Auerbach Publications. p. 9-26.
309. Choe, D. , et al., Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment. Nature Communications 2024, 15, 2356. [Google Scholar] [CrossRef]
310. Latyshev, P. , et al., Unsupervised domain adaptation methods for cross-species transfer of regulatory code signals. Frontiers in Big Data 2023, 6, 1140663. [Google Scholar] [CrossRef]
311. Shamreen Ahamed, B. , Dharani, V., Poonkodi, M., Sangeetha, G., & Sanaa Fathima, B, Transfer learning and domain adaptation in deep networks. 2023, Jupiter Publications Consortium.
312. Xia, Y. , et al., Species-specific design of artificial promoters by transfer-learning based generative deep-learning model. Nucleic Acids Research, 2024: p. gkae429.
313. Xu, T. , et al., A locally weighted, correlated subdomain adaptive network employed to facilitate transfer learning. Image and Vision Computing 2024, 141, 104887. [Google Scholar] [CrossRef]
314. Kala, R. , Chapter 15 - An introduction to evolutionary computation, in Autonomous Mobile Robots, R. Kala, Editor. 2024, Academic Press. p. 715-759.
315. Kruse, R. , et al., Introduction to Evolutionary Algorithms, in Computational Intelligence: A Methodological Introduction. 2022, Springer International Publishing: Cham. p. 225-254.
316. Liu, L., et al. A Survey of Evolutionary Algorithms. in 2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). 2023.
317. Luo, F., G. Ranzi, and Z.Y. Dong, Chapter 7 - Evolutionary optimization, in Building Energy Management Systems and Techniques, F. Luo, G. Ranzi, and Z.Y. Dong, Editors. 2024, Elsevier. p. 111-138.
318. Plump, C., B.J. Berger, and R. Drechsler. Improving evolutionary algorithms by enhancing an approximative fitness function through prediction intervals. in 2021 IEEE Congress on Evolutionary Computation (CEC). 2021. IEEE.
319. Pon Bharathi, A. , et al., A Study on Evolutionary Algorithms and Its Applications. Electrical and Automation Engineering, 2022. 1(1).
320. Tamilselvi, S. , Introduction to Evolutionary Algorithms, in Genetic Algorithms, V. Sebastián, L. José María, and M. José María, Editors. 2022, IntechOpen: Rijeka. p. Ch. 1.
321. Yoshida, H., A. M. Adams, and O. Witkowski, Evolutionary Computation: Perspectives on Past and Future, in Frontiers in Genetics Algorithm Theory and Applications. 2024, Springer. p. 3-13.
322. Zhang, X., X. Zhang, and W. Wang, Evolutionary Computing, in Intelligent Information Processing with Matlab. 2023, Springer Nature Singapore: Singapore. p. 173-219.
323. Yan-Fu Li, E.Z. , Evolutionary Algorithms (EAs), in Reliability Assessment and Optimization: Methods and Applications. 2022: John Wiley & Sons Ltd.
324. Bansal, H. and K. Sharma, A review study on various algorithms of machine learning. Journal of emerging technologies and innovative research, 2020.
325. Nesbeth, D.N., et al., Synthetic biology routes to bio-artificial intelligence. Essays in biochemistry 2016, 60, 381–391. [CrossRef] [PubMed]
326. Tan, F. and J.-J. Slotine. A quorum sensing inspired algorithm for dynamic clustering. in 52nd IEEE Conference on Decision and Control. 2013. IEEE.
327. Wei, L. , et al., Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Briefings in Bioinformatics 2020, 21, 106–119. [Google Scholar] [PubMed]
328. A., V., Kychkin., Oleg, V., Gorshkov., Mikhail, Kukarkin, Predictive models integration with an environmental monitoring IoT platform. Прикладная инфoрматика 2022, 17, 5–16.
329. Bontorin, G., et al. A real-time closed-loop setup for hybrid neural networks. in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2007. IEEE.
330. DeBenedictis, E.A. , et al., A high-throughput platform for feedback-controlled directed evolution. BioRxiv, 2020: p. 2020.04. 01.021022.
331. Elias, J.R., et al., Real-Time Streaming and Event-driven Control of Scientific Experiments. arXiv preprint arXiv:2205.01476, 2022.
332. Kulkarni Sheetal Vijay., S.S., Mrunali Bajare, Rahul Deshmukh, Real Time Monitoring of Temperature And Humidity Using LabVIEW and ML. International Journal for Research in Applied Science & Engineering Technology (IJRA) 2024, 12, 4701–4710.
333. Sahay, M.R., et al., Environmental monitoring system using IoT and cloud service at real-time. EasyChair Preprint 2019, 968, 2.
334. Schmidt, J.Q. and B. Kerkez, Machine learning-assisted, process-based quality control for detecting compromised environmental sensors. Environmental Science & Technology 2023, 57, 18058–18066. [Google Scholar]
335. Tao, W. , Real-time data feedback artificial intelligence control system and control method thereof. 2020.
336. Jaiswal, S., et al., Using Signal Processing in Tandem With Adapted Mixture Models for Classifying Genomic Signals. arXiv preprint arXiv:2211.01603, 2022.
337. Sampara, P., et al., Advancing environmental biotechnology with microbial community modeling rooted in functional ‘omics. Current Opinion in Biotechnology 2024, 88, 103165.
338. Ebulue, C.C., et al., Environmental data in epidemic forecasting: Insights from predictive analytics. Computer Science & IT Research Journal 2024, 5, 1113–1125.
339. Konecny, J. and M. Prauzek, Environmental Monitoring Stations Data Transmission Using Reinforcement Learning Wavelet Compression Method. IFAC-PapersOnLine 2022, 55, 127–132. [Google Scholar] [CrossRef]
340. Malakar, N. , Environmental Intelligence using Machine Learning Applications. Journal of Nepal Physical Society 2022, 8, 42–47. [Google Scholar] [CrossRef]
341. Singh, G. , ENVIRONMENTAL MONITORING WITH MACHINE LEARNING. EPRA International Journal of Multidisciplinary Research (IJMR), 9 (5), 208, 2023. 212.
342. Diveev, A. and E. Shmalko, Machine Learning Feedback Control Approach Based on Symbolic Regression for Robotic Systems. Mathematics 2022, 10, 4100. [Google Scholar] [CrossRef]
343. Kuk, E., S. Bobek, and G.J. Nalepa. ML-based proactive control of industrial processes. in International Conference on Computational Science.
344. Sveen, T.R., S. Hannula, and M. Bahram, Microbial regulation of feedbacks to ecosystem change. Trends in Microbiology, 2024.
345. Yang, J. , et al., A Model Predictive Control Algorithm Based on Biological Regulatory Mechanism and Operational Research. IEEE/CAA Journal of Automatica Sinica 2023, 10, 2174–2176. [Google Scholar] [CrossRef]
346. Boada, Y. , et al., Modeling and optimization of a molecular biocontroller for the regulation of complex metabolic pathways. Frontiers in Molecular Biosciences 2022, 9, 801032. [Google Scholar] [CrossRef] [PubMed]
347. Inoue, M. and K. Kaneko, Entangled gene regulatory networks with cooperative expression endow robust adaptive responses to unforeseen environmental changes. Physical Review Research 2021, 3, 033183. [Google Scholar] [CrossRef]
348. López-Maury, L., S. Marguerat, and J. Bähler, Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nature Reviews Genetics 2008, 9, 583–593. [Google Scholar] [CrossRef]
349. Benzinger, D., S. Ovinnikov, and M. Khammash, Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression. Cell Systems 2022, 13, 353–364. [Google Scholar] [CrossRef]
350. Jiang, T. , et al., Establishing Tunable Genetic Logic Gates with Versatile Dynamic Performance by Varying Regulatory Parameters. ACS Synthetic Biology 2023, 12, 3730–3742. [Google Scholar] [CrossRef]
351. Velazquez Sanchez, A.K., et al., Tailored synthetic sRNAs dynamically tune multilayer genetic circuits. ACS synthetic biology 2023, 12, 2524–2535. [CrossRef]
352. Kaenampornpan, M. and E. O Neill, Integrating history and activity theory in context aware system design. COGNITIVE SCIENCE RESEARCH PAPER-UNIVERSITY OF SUSSEX CSRP 2005, 577, 87.
353. Lee, S.H. , et al., Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal. Metabolic Engineering 2024, 82, 262–273. [Google Scholar] [CrossRef]
354. Lengerich, B., et al., Contextualized machine learning. arXiv preprint arXiv:2310.11340, 2023.
355. Li, C.-T. , et al., Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control. npj Systems Biology and Applications 2023, 9, 7. [Google Scholar] [CrossRef] [PubMed]
356. Piga, D., F. Pura, and M. Forgione, On the adaptation of in-context learners for system identification. IFAC-PapersOnLine 2024, 58, 277–282. [CrossRef]
357. Shakiba, N. , et al., Context-aware synthetic biology by controller design: Engineering the mammalian cell. Cell systems 2021, 12, 561–592. [Google Scholar] [CrossRef] [PubMed]
358. Zeng, G., et al., Continual learning of context-dependent processing in neural networks. Nature Machine Intelligence 2019, 1, 364–372. [CrossRef]
359. Gerngross, D., N. Beerenwinkel, and S. Panke, Systematic investigation of synthetic operon designs enables prediction and control of expression levels of multiple proteins. bioRxiv, 2022: p. 2022.06. 10.495604.
360. Yamamoto, Y. , et al., Construction of a machine-learning model to predict the optimal gene expression level for efficient production of D-lactic acid in yeast. World Journal of Microbiology and Biotechnology 2023, 39, 69. [Google Scholar] [CrossRef]
361. Cobongela, S.Z.Z. , Bioremediation of Pharmaceutical Waste, in Bioremediation for Environmental Pollutants. 2023, Bentham Science Publisher. p. 303-328.
362. Khuat, T.T. , et al., Applications of machine learning in biopharmaceutical process development and manufacturing: Current trends, challenges, and opportunities. arXiv preprint arXiv:, arXiv:2310.09991, 2023.
363. Narayanan, H. , et al., A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins. Biotechnology and bioengineering 2019, 116, 2540–2549. [Google Scholar] [CrossRef]
364. Singh, M.V.P. and K.R. Shankar, Next-generation hybrid technologies for the treatment of pharmaceutical industry effluents. Journal of Environmental Management 2024, 353, 120197. [CrossRef]
365. Sudhir, S.K. , et al., Treatment of pharmaceutical pollutants from industrial wastewater, in Current Developments in Biotechnology and Bioengineering. 2023, Elsevier. p. 1-16.
366. Walsh, I. , et al., Harnessing the potential of machine learning for advancing “Quality by Design” in biomanufacturing. mAbs, 14 (1), 2013593. 2022.
367. Divya Singhal, U.C. , Ayushi Singh, Environmental monitoring in the digital age: embracing iot solutions, in Futuristic Trends in IoT. 2024: IIP Series. p. 207-217.
368. Popescu, S.M. , et al., Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Frontiers in Environmental Science 2024, 12, 1336088. [Google Scholar] [CrossRef]
369. Sachdev, A. , et al. Innovative 2D Nanomaterials-Based Electrochemical Microfluidic Sensors for Food Safety and Environmental Ecology. in Electrochemical Society Meeting Abstracts.
370. Zhou, A.Y. , et al., A portable bioelectronic sensing system (BESSY) for environmental deployment incorporating differential microbial sensing in miniaturized reactors. PloS one 2017, 12, e0184994. [Google Scholar] [CrossRef]
371. Deepak Sharma, A.P. , Deepika Sharma, Sampan Attri, Abhishek Chaudhary, Microbial Sensors, in Microbial Approaches for Sustainable Green Technologies. 2024, CRC Press. p. 20.
372. B. Jansi, S.J., Dr. B. Jansi, S.J., Dr. V. Sumalatha, Exploiting synergies for improved applications through the fusion of deep learning and machine learning, in Futuristic Trends in Information Technology. 2024, IIP Series. p. 1-16.
373. Fouché, A. and A. Zinovyev, Omics data integration in computational biology viewed through the prism of machine learning paradigms. Frontiers in Bioinformatics 2023, 3, 1191961. [Google Scholar] [CrossRef]
374. Kumaran, S., et al. An Intelligent Framework for Wireless Sensor Networks in Environmental Monitoring. in 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS). 2023. IEEE.
375. Nandhini, B. Sensor Fusion Techniques for Enhanced Environmental Monitoring in IoT. in 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA). 2023. IEEE.
376. Pokkuluri, K.S. and A. Khang, Integration of Machine Learning Augmented With Biosensors for Enhanced Water Quality Monitoring, in Agriculture and Aquaculture Applications of Biosensors and Bioelectronics. 2024, IGI Global. p. 181-192.
377. Sen, P. and M. Orešič, Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023, 13, 855. [Google Scholar] [CrossRef] [PubMed]
378. Wu, D. , et al., Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design. Synthetic and Systems Biotechnology 2024, 9, 33–42. [Google Scholar] [CrossRef] [PubMed]
379. Baral, R. , et al., A General Mechanism for the General Stress Response in Bacteria. bioRxiv, 2024: p. 2024.02. 16.580724.
380. Chan, E., M. Au, and R. Chan, Functional profiling and strategic antimicrobial manipulation of a universal nutrition-sensing network to regulate microbial virulence, antibiotic tolerance, and stress protection. Hong Kong Med J, 2017. 23(4 Supplement 5).
381. García-Contreras, R. , et al., Quorum sensing enhancement of the stress response promotes resistance to quorum quenching and prevents social cheating. The ISME journal 2015, 9, 115–125. [Google Scholar] [CrossRef] [PubMed]
382. Laslo, É., É. György, and M.-R. Szentpáli, Stress response in bacteria originated from dairy products. Acta Universitatis Sapientiae, Alimentaria 2023, 16, 90–102. [CrossRef]
383. Mills, S. , et al., Enhancing the stress responses of probiotics for a lifestyle from gut to product and back again. Microbial cell factories 2011, 10, 1–15. [Google Scholar] [CrossRef]
384. Society, A.C. , Microbial Stress Response: Mechanisms and Data Science. 2023: ACS Publications.
385. Tang, M.-X., S. Dyrma, and T. Dong, Stress responses modulate bacterial competitive fitness in polymicrobial communities, in Stress: Immunology and Inflammation. 2024, Elsevier. p. 161-171.
386. Yuan, B. , et al., Regulatory mechanisms underlying yeast chemical stress response and development of robust strains for bioproduction. Current Opinion in Biotechnology 2024, 86, 103072. [Google Scholar] [CrossRef]
387. Zoheir, A.E. , et al., A three-colour stress biosensor reveals multimodal response in single cells and spatiotemporal dynamics of biofilms. npj Biofilms and Microbiomes 2023, 9, 57. [Google Scholar] [CrossRef]
388. Haouari, B., R. Mzid, and O. Mosbahi, A reinforcement learning-based approach for online optimal control of self-adaptive real-time systems. Neural Computing and Applications 2023, 35, 20375–20401. [CrossRef]
389. Kulkarni, S., A. Marda, and K. Vaidhyanathan. Towards self-adaptive machine learning-enabled systems through QoS-aware model switching. in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). 2023. IEEE.
390. Rothman, D.L., P.B. Moore, and R.G. Shulman, The impact of metabolism on the adaptation of organisms to environmental change. Frontiers in Cell and Developmental Biology 2023, 11, 1197226.
391. Töpfer, M., et al. Online ML Self-adaptation in Face of Traps. in 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). 2023. IEEE.
392. Xavier, J.B. , Machine learning of cellular metabolic rewiring. Biology Methods and Protocols 2024, 9, bpae048. [Google Scholar] [CrossRef]
393. Kulkarni Sheetal Vijay., S.S. Kulkarni Sheetal Vijay., S.S., Mrunali Bajare, Rahul Deshmukh, Real Time Monitoring of Temperature And Humidity Using LabVIEW and ML. International Journal for Research in Applied Science & Engineering Technology (IJRA), 2024. 12(5).
394. MOE, N.P.S. , Machine-learning-assisted environment-adaptive thermal metamaterials. Cornell University, 2023.
395. Paterova, T., M. Prauzek, and J. Konecny. Robustness Analysis of Data-Driven Self-Learning Controllers for IoT Environmental Monitoring Nodes based on Q-learning Approaches. in 2022 IEEE Symposium Series on Computational Intelligence (SSCI). 2022. IEEE.
396. Saleh, S. and B. Battseren, AI-driven Solutions for Sustainable Environment Monitoring. Embedded Selforganising Systems 2023, 10, 1–2. [CrossRef]
397. Ankit Mahule, K.R. , Ankush D. Sawarkar, Sagar Lachure, Enhancing Environmental Resilience, in Artificial Intelligence for Air Quality Monitoring and Prediction. 2024, CRC Press.
398. Aida, H. , et al., Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity. Elife 2022, 11, e76846. [Google Scholar] [CrossRef] [PubMed]
399. Somathilaka, S., et al., Inferring Gene Regulatory Neural Networks for Bacterial Decision Making in Biofilms. arXiv preprint arXiv:2301.04225, 2023.
400. Somathilaka, S.S. , et al., Revealing gene regulation-based neural network computing in bacteria. Biophysical Reports, 2023. 3(3).
401. Alam, M. and S. Ghosh. Deepqmlp: A scalable quantum-classical hybrid deep neural network architecture for classification. in 2022 35th International Conference on VLSI Design and 2022 21st International Conference on Embedded Systems (VLSID). 2022. IEEE.
402. Dai, C. , et al., QSP: An open sequence database for quorum sensing related gene analysis with an automatic annotation pipeline. Water research 2023, 235, 119814. [Google Scholar] [CrossRef] [PubMed]
403. Faure, L. , et al., A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models. Nature Communications 2023, 14, 4669. [Google Scholar] [CrossRef]
404. Wu, S. , et al., Combinational quorum sensing devices for dynamic control in cross-feeding cocultivation. Metabolic Engineering 2021, 67, 186–197. [Google Scholar] [CrossRef]
405. Gotti, C. , et al., LC-SRM combined with machine learning enables fast identification and quantification of bacterial pathogens in urinary tract infections. bioRxiv, 2024: p. 2024.05. 31.596829.
406. Mishra, A. , et al., Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces. Antibiotics 2024, 13, 788. [Google Scholar] [CrossRef]
407. Rahman, M.H.-U. , et al., Machine learning-assisted raman spectroscopy and SERS for bacterial pathogen detection: clinical, food safety, and environmental applications. Chemosensors 2024, 12, 140. [Google Scholar] [CrossRef]
408. Zhang, X. , et al., Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Frontiers in Microbiology 2024, 15, 1449844. [Google Scholar]
409. Fakhar, M., M. Ahmed, and A.N. Sabri, Computational and experimental strategies for combating MBL P. aeruginosa (MBLPA) biofilms using phytochemicals: Targeting the quorum sensing network. Saudi Journal of Biological Sciences 2024, 31, 104001. [Google Scholar] [CrossRef]
410. Galvez-Llompart, M. , et al., Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR. Journal of Enzyme Inhibition and Medicinal Chemistry 2024, 39, 2330907. [Google Scholar] [CrossRef]
411. Munagala, S. , et al., Integrating QQ with Nano-techniques–A Potent Antibacterial Therapy. 2023.
412. Song, K. , et al., Whole-transcriptome analysis reveals mechanisms underlying antibacterial activity and biofilm inhibition by a malic acid combination (MAC) in Pseudomonas aeruginosa. PeerJ 2023, 11, e16476. [Google Scholar] [CrossRef] [PubMed]
413. Tang, D. , et al., Effect of L-HSL on biofilm and motility of Pseudomonas aeruginosa and its mechanism. Applied Microbiology and Biotechnology 2024, 108, 418. [Google Scholar] [CrossRef] [PubMed]
414. Basu, B. , et al., The Future of Cystic Fibrosis Care: Exploring AI's Impact on Detection and Therapy. Current Respiratory Medicine Reviews 2024, 20, 302–321. [Google Scholar] [CrossRef]
415. Graña-Miraglia, L. , et al., Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis. PLOS Computational Biology 2023, 19, e1011424. [Google Scholar] [CrossRef]
416. Nafee, N. , et al., Enzyme-Linked Lipid Nanocarriers for Coping Pseudomonal Pulmonary Infection. Would Nanocarriers Complement Biofilm Disruption or Pave Its Road? International Journal of Nanomedicine, 2024: p. 3861-3890.
417. A. Yu. Voronkova, E.K.Z., V. A. Yu. Voronkova, E.K.Z., V. D. Sherman, E. L. Amelina, A. V. Orlov, O. V. Usacheva, V. V. Tarakanova, L. V. Smirnova, E. V. Pasnova, N. D. Odinaeva, E. I. Kondratyeva, Therapy of gram-negative infection in the complex treatment of cystic fibrosis. Russian Pulmonology Journal, 2023. 33(4).
418. Chung, J. , et al., Targeting Matrix Exopolysaccharides to Disrupt Pseudomonas aeruginosa Biofilms in Cystic Fibrosis. 2023.
419. Kenneth, N. , Cystic fibrosis and pulmonary biofilms. The Southwest Respiratory and Critical Care Chronicles, 2023. 11(49).
420. Rappo, U. , et al., Late Breaking Abstract-A novel treatment for chronic P. aeruginosa pulmonary infection in CF subjects-A phase 1b/2a randomized, double-blind, placebo-controlled, multicenter study to evaluate phage therapy. 2023, Eur Respiratory Soc.
421. Fernandes, P.O. , et al., Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus. Journal of Chemical Information and Modeling 2024, 64, 1932–1944. [Google Scholar] [CrossRef]
422. Nigo, M. , et al., Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records. Nature Communications 2024, 15, 2036. [Google Scholar] [CrossRef]
423. Rusic, D. , et al., Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024, 12, 842. [Google Scholar] [CrossRef]
424. Shehadeh, F. , et al., Machine learning-assisted high-throughput screening for Anti-MRSA compounds. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024.
425. Zhang, L., et al., Quantum Long Short-Term Memory for Drug Discovery. arXiv preprint arXiv:2407.19852, 2024.
426. Ho, L.-C. , et al., Impact of the implementation of the Intelligent Antimicrobial System (iAMS) on clinical outcomes among patients with bacteraemia caused by methicillin-resistant Staphylococcus aureus. International Journal of Antimicrobial Agents 2024, 63, 107142. [Google Scholar] [CrossRef]
427. Max, S. , AI Tools in Food Quality Monitoring. 2023.
428. Mayer, C. , et al., Quorum sensing architecture network in Escherichia coli virulence and pathogenesis. FEMS Microbiology Reviews 2023, 47, fuad031. [Google Scholar] [CrossRef]
429. O’Shea, N., D. Greene, and M.A. Fenelon, Artificial Intelligence in Food Safety, in Encyclopedia of Food Safety (Second Edition), G.W. Smithers, Editor. 2024, Academic Press: Oxford. p. 178-184.
430. Wang, Z. , et al., Establishment of real-time fluorescence and visual LAMP for rapid detection of Escherichia coli O157: H7 and kits construction. Letters in Applied Microbiology 2023, 76, ovad122. [Google Scholar] [CrossRef]
431. Yadav, P.K. , et al. Detection of E. coli concentration levels using CSI-D+ handheld with UV-C fluorescence imaging and deep learning on leaf surfaces. in Sensing for Agriculture and Food Quality and Safety XVI.
432. Yi, J. , et al., AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water. Water Research 2023, 242, 120258. [Google Scholar] [CrossRef] [PubMed]
433. Zhang, C., et al., Screening of E. coli O157: H7 AI-2 QS inhibitors and their inhibitory effect on biofilm formation in combination with disinfectants. Food Bioscience 2024, 58, 103821.
434. Yin, B. , et al., Chip-based automated equipment for dual-mode point-of-care testing foodborne pathogens. Biosensors and Bioelectronics 2024, 257, 116338. [Google Scholar] [CrossRef] [PubMed]
435. Pramanik, S., V. Kadam, and S. Bhatlawande. Real Time Food Monitoring and Quality Alert System Using IoT and Streamlit. in International Conference on Information Science and Applications. 2023. Springer.
436. Radhakrishnan, A., et al. An Automated Alert System for Monitoring the Hygiene in Restaurants Using Machine Vision. in 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST). 2024. IEEE.
437. Li, Y. , et al., A novel quorum sensing regulator LuxT contributes to the virulence of Vibrio cholerae. Virulence 2023, 14, 2274640. [Google Scholar] [CrossRef] [PubMed]
438. Mahima M, K.g.K., P. Vasanthakumari, G. Jayalakshmi, Kothandapani Sundar, Quorum Sensing Based Drug Screening Against Vibrio Cholerae. Journal of Microbes and Research, 2022. 1(1).
439. Sajeevan, A., T. Ramamurthy, and A.P. Solomon, Vibrio cholerae virulence and its suppression through the quorum-sensing system. Critical Reviews in Microbiology, 2024: p. 1-22.
440. Walker, L.M. , et al., A simple mechanism for integration of quorum sensing and cAMP signalling in V. cholerae. BioRxiv, 2023.
441. El Bushra, H. , A Tool to Monitor Multi-Sectoral Response Activities During Outbreaks of Cholera in Sudan. African Journal of Health and Medical Sciences (AFJHMS) 2024, 9, 20–28. [Google Scholar] [CrossRef]
442. El Morr, C., et al., AI-based epidemic and pandemic early warning systems: A systematic scoping review. Health Informatics Journal 2024, 30, 14604582241275844. [CrossRef]
443. Fieldhouse, J., et al., How feasible or useful are timeliness metrics as a tool to optimise One Health outbreak responses? BMJ Global Health 2024, 9, e013615. [CrossRef]
444. Nwankwo, E.I., et al., Artificial Intelligence in predictive analytics for epidemic outbreaks in rural populations. International Journal of Life Science Research Archive 2024, 7, 078–094. [CrossRef]
445. Isiaka, A. , et al., Harnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks. International Journal of Innovative Research and Development, 2024.
446. Kuik, C. , et al., Matrix-assisted laser desorption/ionization mass spectrometry imaging for quorum sensing. AMB Express 2024, 14, 45. [Google Scholar] [CrossRef]
447. Roy, M., M. Adhikari, and B. Tiwary, Phytocompound-Mediated Inhibition of Quorum-Sensing Cascade in Pathogenic Bacteria, in Natural Products. 2024, CRC Press. p. 79-101.
448. Charlebois, D.A. , Quantitative systems-based prediction of antimicrobial resistance evolution. NPJ Systems Biology and Applications 2023, 9, 40. [Google Scholar] [CrossRef]
449. Kim, J.I. , et al., Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective. Clinical microbiology reviews 2022, 35, e00179–21. [Google Scholar] [CrossRef] [PubMed]
450. Kolluru, V., et al., Use of Predictive Analytics in Antimicrobial Resistance: A Review. Vinoth Kumar Kolluru et al., Cognizance Journal of Multidisciplinary Studies. 2024, 4, 404–414.
451. Nsubuga, M. , et al., Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa. BMC genomics 2024, 25, 287. [Google Scholar] [CrossRef]
452. Roche-Lima, A. , et al., ML-SyPred–Machine Learning Synergy Predictor: A Tool to Predict Drug Combinations. 2022.
453. Weis, C. , et al., Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning. bioRxiv 2020, 1, 1–35. [Google Scholar]
454. Yang, J. , et al., Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections. npj Antimicrobials and Resistance 2023, 1, 14. [Google Scholar] [CrossRef]
455. Yun, B. , et al., Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens. CyTA-Journal of Food 2024, 22, 2324024. [Google Scholar] [CrossRef]
456. Ashreetha, B., et al. Precision Medicine in Oncology-Leveraging Machine Learning for Accurate Neoplasm Diagnosis. in 2024 5th International Conference for Emerging Technology (INCET). 2024. IEEE.
457. Bhuvaneswari, R. , et al., Advancing Precision Medicine: Integrating AI and Machine Learning for Personalized Healthcare Solutions, in Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care. 2024, IGI Global. p. 344-361.
458. Deliu, N. and B. Chakraborty, Artificial Intelligence-based Decision Support Systems for Precision and Digital Health. arXiv preprint arXiv:2407.16062, 2024.
459. Folguera-Blasco, N. , et al., Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: its pAIns and gAIns. Frontiers in Systems Biology 2024, 4, 1380685. [Google Scholar] [CrossRef]
460. Ganga, S., et al. The use of ML Algo Techniques in Translational Way of HC System (Medicine). in 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 2024. IEEE.
461. Pfeil, J. , et al., tauX: A Gene Expression Ratio Strategy to Improve Machine Learning Applications in Precision Medicine. bioRxiv, 2024: p. 2024.07. 01.601595.
462. Rehan, H. , Advancing Cancer Treatment with AI-Driven Personalized Medicine and Cloud-Based Data Integration. Journal of Machine Learning in Pharmaceutical Research 2024, 4, 1–40. [Google Scholar]
463. Viduedo, P.H.Z., et al., Harnessing the Power of Ai and Machine Learning for Next-Generation Sequencing Data Analysis: A Comprehensive Review of Applications, Challenges, and Future Directions in Precision Oncology. Revista Ibero-Americana de Humanidades, Ciências e Educação. 2024, 10, 2898–2904.
464. Reena Lokare, J.W. , Sunita Patil, Ganesh Wadmare, Darshan Patil, Transparent precision: Explainable AI empowered breast cancer recommendations for personalized treatment. IAES International Journal of Artificial Intelligence 2024, 13, 2694–2694. [Google Scholar]
465. Bansal, A. , et al., Gene Therapy and Its Applications. Journal of Medical Evidence, 2023. 4(1).
466. Carneiro, D.C., et al., Therapeutic applications of synthetic gene/genetic circuits: a patent review. Frontiers in Bioengineering and Biotechnology 2024, 12, 1425529.
467. Deyhimfar, R. , et al., The clinical impact of mRNA therapeutics in the treatment of cancers, infections, genetic disorders, and autoimmune diseases. Heliyon, 2024.
468. He, Y., A. P. Johnston, and C.W. Pouton, Therapeutic applications of cell engineering using mRNA technology. Trends in Biotechnology, 2024.
469. Li, Y. mRNA therapeutics in diseases: Light inspect into a new horizon. in Theoretical and Natural Science. 2024.
470. Mohandoss, K. , A gene therapy approach to the design of personalized medicine, in Futuristic Trends in Pharmacy & Nursing. 2024: IIP Series. p. 167-181.
471. Datta, B.N. and B. Sahoo, Machine learning, regression and optimization. Data Science and SDGs: Challenges, Opportunities and Realities, 2021: p. 177-197.
472. Hanzely, S., Adaptive Optimization Algorithms for Machine Learning. arXiv preprint arXiv:2311.10203, 2023.
473. Kumar, D., et al., Machine learning's role in personalized medicine & treatment optimization. World Journal of Advanced Research and Reviews 2024, 21, 1675–1686.
474. Zhang, B., Y.J. Ong, and T. Nakamura. Simpo: Simultaneous prediction and optimization. in 2022 IEEE International Conference on Services Computing (SCC). 2022. IEEE.
475. Pradeep Verma, N.M.a.V.S. , Machine learning for personalized medicine: Tailoring treatment strategies through data analysis. The Pharma Innovation Journal 2019, 8, 11–14. [Google Scholar] [CrossRef]
476. Behdani, A.M., et al., Optimizing pharmacogenomic decision-making by data science. PLOS Digital Health 2024, 3, e0000451.
477. Li, P. , et al., Utility based approach in individualized optimal dose selection using machine learning methods. Statistics in medicine 2022, 41, 2957–2977. [Google Scholar] [CrossRef]
478. Mhatre, S. , et al., AI and ML for development of cell and gene therapy for personalized treatment. Bioinformatics Tools for Pharmaceutical Drug Product Development, 2023: p. 371-400.
479. Szűcs, T.D., et al. Model predictive fuzzy control in chemotherapy optimization. in 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI). 2023. IEEE.
480. Elangkovan, N. and G. Dickson, Gene therapy for Duchenne muscular dystrophy. Journal of neuromuscular diseases, 2021. 8(s2): p. S303-S316.
481. Happi Mbakam, C. and J.P. Tremblay, Gene therapy for Duchenne muscular dystrophy: an update on the latest clinical developments. Expert Review of Neurotherapeutics 2023, 23, 905–920. [CrossRef]
482. Markati, T., et al., Emerging therapies for Duchenne muscular dystrophy. The Lancet Neurology 2022, 21, 814–829. [CrossRef]
483. Olson, E.N. , Toward the correction of muscular dystrophy by gene editing. Proceedings of the National Academy of Sciences 2021, 118, e2004840117. [Google Scholar] [CrossRef]
484. Podkalicka, P. , et al., Molecular mechanisms of Duchenne muscular dystrophy and new therapeutic strategies. Postepy Biochemii 2022, 68, 109–122. [Google Scholar]
485. Richard, I. , Basic notions about gene therapy from the nucleic acid perspective and applications in a pediatric disease: Duchenne muscular dystrophy. Archives de Pédiatrie.
486. Saad, F.A., et al., Duchenne muscular dystrophy gene therapy. Current Gene Therapy 2024, 24, 17–28. [CrossRef] [PubMed]
487. Superson, M., et al., Applications of gene modification technologies in the treatment of inherited diseases. Journal of Education, Health and Sport 2024, 66, 50073–50073. [CrossRef]
488. Damane, B.P. , et al., Applying artificial intelligence prediction tools for advancing precision oncology in immunotherapy: future perspectives in personalized care, in Artificial intelligence and precision oncology: bridging cancer research and clinical decision support. 2023, Springer. p. 239-258.
489. Leek, L.V. , et al., Integrative multi-omic machine learning model predicts neoadjuvant immunotherapy response using molecular data and deep learning-derived features from digital pathology. Cancer Research, 2024. 84(6_Supplement): p. 4951-4951.
490. Li, Y. , et al., Informing immunotherapy with multi-omics driven machine learning. npj Digital Medicine 2024, 7, 67. [Google Scholar] [CrossRef] [PubMed]
491. Ying, X. and B. Li, Machine-learning Modeling for Personalized Immunotherapy-An Evaluation Module. Biomedical journal of scientific & technical research 2022, 47, 38211.
492. Arulraj, T., et al., Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology. Briefings in bioinformatics 2024, 25, bbae131.
493. Finotello, F. and F. Eduati, Multi-omics profiling of the tumor microenvironment: paving the way to precision immuno-oncology. Frontiers in Oncology 2018, 8, 430. [CrossRef]
494. Merlano, M., et al., Knowing the tumour microenvironment to optimise immunotherapy. Acta Otorhinolaryngologica Italica 2019, 39, 2. [CrossRef]
495. Pomponio, R.J. , et al., Classification of the Tumor Immune Microenvironment Using Machine-Learning-Based CD8 Immunophenotyping As a Potential Biomarker for Immunotherapy and TGF-β Blockade in Nonsmall Cell Lung Cancer. AI in Precision Oncology 2024, 1, 106–118. [Google Scholar] [CrossRef]
496. Popel, A.S. , Immunoactivating the tumor microenvironment enhances immunotherapy as predicted by integrative computational model. Proceedings of the National Academy of Sciences 2020, 117, 4447–4449. [Google Scholar] [CrossRef]
497. Sun, J. , et al., Neural-network-based immune optimization regulation using adaptive dynamic programming. IEEE Transactions on Cybernetics 2022, 53, 1944–1953. [Google Scholar] [CrossRef]
498. Ahmad, A. , et al., Precision cancer nanotherapy: evolving role of multifunctional nanoparticles for cancer active targeting. Journal of medicinal chemistry 2019, 62, 10475–10496. [Google Scholar] [CrossRef] [PubMed]
499. Broadwater, D. , et al., Counterion tuning of near-infrared organic salts dictates phototoxicity to inhibit tumor growth. ACS Applied Materials & Interfaces 2022, 14, 53511–53522. [Google Scholar]
500. Chen, J. , et al., Precise fibrin decomposition and tumor mechanics modulation with hydroxyethyl starch-based smart nanomedicine for enhanced antitumor efficacy. Journal of Materials Chemistry B 2022, 10, 8193–8210. [Google Scholar] [CrossRef] [PubMed]
501. Haze, H. , et al., Modulation of the Tumor Stroma and Associated Novel Nanoparticle Strategies to Enhance Tumor Targeting. Surgeries 2024, 5, 49–62. [Google Scholar] [CrossRef]
502. Manzari, M.T., et al., Targeted drug delivery strategies for precision medicines. Nature Reviews Materials 2021, 6, 351–370. [CrossRef]
503. Vishwakarma, M. , et al., Cationic nanocarriers: A potential approach for targeting negatively charged cancer cell. Advances in Colloid and Interface Science, 2024: p. 103160.
504. Fedorov, V.D., M. Sadelain, and C.C. Kloss, Novel approaches to enhance the specificity and safety of engineered T cells. The Cancer Journal 2014, 20, 160–165. [CrossRef]
505. Gamboa, L., A.H. Zamat, and G.A. Kwong, Synthetic immunity by remote control. Theranostics 2020, 10, 3652. [CrossRef]
506. Hickey, J.W., A.K. Kosmides, and J.P. Schneck, Engineering platforms for t cell modulation. International Review of Cell and Molecular Biology 2018, 341, 277–362.
507. McBride, D.A., et al., Applications of molecular engineering in T-cell-based immunotherapies. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology 2019, 11, e1557.
508. Ti, D., et al., Adaptive T cell immunotherapy in cancer. Science China Life Sciences 2021, 64, 363–371. [CrossRef]
509. Bhandari, A. , et al., Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy. Journal of Controlled Release 2024, 370, 721–746. [Google Scholar] [CrossRef]
510. Das, K.P. , Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges. Frontiers in Medical Technology 2023, 4, 1067144. [Google Scholar] [CrossRef] [PubMed]
511. Haval, A.M., K. Shwetabh, and S.S. Dash, Machine learning-based enhanced drug delivery system and its applications–a systematic review. J Angiother 2023, 642, 123098. [Google Scholar]
512. Hristova-Panusheva, K. , et al., Nanoparticle-Mediated Drug Delivery Systems for Precision Targeting in Oncology. Pharmaceuticals 2024, 17, 677. [Google Scholar] [CrossRef] [PubMed]
513. Husseini, G.A. , et al., Deep Learning for the Accurate Prediction of Triggered Drug Delivery. IEEE Transactions on NanoBioscience, 2024.
514. Kallepalli, B. , et al., Intelligent Drug Delivery: Pioneering Stimuli-Responsive Systems to Revolutionize Disease Management-An In-depth Exploration. Current Drug Delivery, 2024.
515. R, D. , Guided therapeutics: the art and science of drug targeting, in Futuristic Trends in Pharmacy & Nursing Volume. 2024: IIP Series. p. 16-30.
516. Bräutigam, K. , Optimization of chemotherapy regimens using mathematical programming. Computers & Industrial Engineering 2024, 191, 110078. [Google Scholar]
517. Floares, A. , et al., Optimal drug dosage regimens in cancer chemotherapy with neural networks. Journal of Clinical Oncology, 2004. 22(14_suppl): p. 2134-2134.
518. Hakami, M.A. , Harnessing machine learning potential for personalised drug design and overcoming drug resistance. Journal of Drug Targeting 2024, 32, 918–930. [Google Scholar] [CrossRef]
519. Hosseinpour, Z., A.H. Nikoofard, and E. Nejabat. Model Predictive Control for Optimal Drug Administration of Cancer Chemotherapy. in 2023 31st International Conference on Electrical Engineering (ICEE). 2023. IEEE.
520. Pistikopoulos, E.N., I. Nascu, and E.G. Velliou, Modelling optimization and control of biomedical systems. 2018: John Wiley & Sons.
521. Chaudhari, D.D., S. D. Mali, and V. Chaudhari, A Short Review on Recent Approches in Targeted Drug Dilvairy System (TDDS). 2023.
522. Ezike, T.C. , et al., Advances in drug delivery systems, challenges and future directions. Heliyon, 2023. 9(6).
523. Mirić, A. and N. Milivojević. Intelligent Drug Delivery Systems. 2023. Cham: Springer International Publishing.
524. Tărăbuță, P.A. , et al., Drug Delivery Systems for Tissue Engineering, in Biomaterials and Tissue Engineering. 2023, Springer. p. 205-238.
525. Oishi, M. , et al., Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology. Drug Metabolism and Pharmacokinetics, 2024: p. 101020.
526. Sagingalieva, A. , et al., Hybrid quantum neural network for drug response prediction. Cancers 2023, 15, 2705. [Google Scholar] [CrossRef]
527. Dhiwin Samrich J, D.S.J.N.R. , Quantum-Inspired Machine Learning: Transformative Applications and Implications for Industry Disruption. International Journal of Research Publication and Reviews 2024, 5, 13006–13012. [Google Scholar]
528. Alsberg, E., H.A. von Recum, and M.J. Mahoney, Environmental cues to guide stem cell fate decision for tissue engineering applications. Expert opinion on biological therapy 2006, 6, 847–866. [CrossRef]
529. Kong, Y. , et al., Evaluating Differentiation Status of Mesenchymal Stem Cells by Label-Free Microscopy System and Machine Learning. Cells 2023, 12, 1524. [Google Scholar] [CrossRef]
530. Li, C., Y. Fan, and L. Zheng, Differentiation of stem cells regulated by biophysical cues. Sheng wu yi xue Gong Cheng xue za zhi= Journal of Biomedical Engineering= Shengwu Yixue Gongchengxue Zazhi 2023, 40, 609–616.
531. Park, D.B. , et al., Tissue specific stem cell therapy for airway regeneration. Cell Proliferation, 2024: p. e13662.
532. Song, E. , et al., P157 Differentiation of Tonsil-derived mesenchymal stem cells to intestinal stem cells-like cells for cell therapy of inflammatory bowel disease. Journal of Crohn's and Colitis, 2024. 18(Supplement_1): p. i462-i462.
533. Yang, X. , et al., A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems. Cell Discovery 2023, 9, 53. [Google Scholar] [CrossRef] [PubMed]
534. Adil, A. , et al., Stem cell therapy in the era of machine learning, in Computational Biology for Stem Cell Research. 2024, Elsevier. p. 77-84.
535. Ashraf, M., M. Khalilitousi, and Z. Laksman, Applying machine learning to stem cell culture and differentiation. Current Protocols 2021, 1, e261. [CrossRef] [PubMed]
536. Guo, J.L., M. Januszyk, and M.T. Longaker, Machine learning in tissue engineering. Tissue Engineering Part A, 2023. 29(1-2): p. 2-19.
537. Yadav, M.K. , et al., Application of machine learning–based approaches in stem cell research, in Computational biology for stem cell research. 2024, Elsevier. p. 65-76.
538. Zaman, W.S.W.K. , et al., Machine learning in stem cells research: application for biosafety and bioefficacy assessment. IEEE Access 2021, 9, 25926–25945. [Google Scholar] [CrossRef]
539. Goyal, P. and R. Malviya, Developments in Stem Cell Therapy by Utilizing Artificial Intelligence. Current Pharmaceutical Design 2023, 29, 2223–2228. [CrossRef]
540. Richard Webb, A.M. , Alwyn D'Souza, Regenerative medicine, in Medical Innovation. 2023: CRC Press. p. 13.
541. Chen, M. , et al., Advancements in tissue engineering for articular cartilage regeneration. Heliyon, 2024.
542. Gharravi, A.M. , et al., Stem Cell for Cartilage Repair, in Handbook of Stem Cell Therapy, K.H. Haider, Editor. 2022, Springer Nature Singapore: Singapore. p. 1-35.
543. Gupta, A. and S. Singh, Potential role of growth factors controlled release in achieving enhanced neuronal trans-differentiation from mesenchymal stem cells for neural tissue repair and regeneration. Molecular Neurobiology, 2022: p. 1-19.
544. Liu, Y.Y.F. , et al., Machine learning to predict mesenchymal stem cell efficacy for cartilage repair. PLoS Computational Biology 2020, 16, e1008275. [Google Scholar] [CrossRef]
545. Velikic, G. , et al., Harnessing the stem cell niche in regenerative medicine: innovative avenue to combat neurodegenerative diseases. International journal of molecular sciences 2024, 25, 993. [Google Scholar] [CrossRef]
546. Wang, P. , et al., Treatment and application of stem cells from different sources for cartilage injury: a literature review. Annals of Translational Medicine, 2022. 10(10).
547. Baranwal, M. , et al., Deep learning enables design of multifunctional synthetic human gut microbiome dynamics. bioRxiv, 2021: p. 2021.09. 27.461983.
548. Lebovich, M., M. Zeng, and L.B. Andrews, Algorithmic programming of sequential logic and genetic circuits for recording biochemical concentration in a probiotic bacterium. ACS synthetic biology 2023, 12, 2632–2649. [Google Scholar] [CrossRef]
549. Liu, Y., Z. Zhu, and L. Jiang, Programming therapeutic probiotics by self-tunable sense-and-respond genetic circuits. Trends in Microbiology, 2023.
550. Xia, J.Y., et al., Engineered calprotectin-sensing probiotics for IBD surveillance in humans. Proceedings of the National Academy of Sciences 2023, 120, e2221121120.
551. Zhou, J. , et al., Programmable probiotics modulate inflammation and gut microbiota for inflammatory bowel disease treatment after effective oral delivery. Nature Communications 2022, 13, 3432. [Google Scholar] [CrossRef]
552. Baranwal, M. , et al., Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics. Elife 2022, 11, e73870. [Google Scholar] [CrossRef] [PubMed]
553. Barra, M., T. Danino, and D. Garrido, Engineered probiotics for detection and treatment of inflammatory intestinal diseases. Frontiers in Bioengineering and Biotechnology 2020, 8, 265.
554. Fonseca, D.C. , et al., Evaluation of gut microbiota predictive potential associated with phenotypic characteristics to identify multifactorial diseases. Gut Microbes 2024, 16, 2297815. [Google Scholar] [CrossRef]
555. Li, M. , et al., Engineered probiotics with sustained release of interleukin-2 for the treatment of inflammatory bowel disease after oral delivery. Biomaterials 2024, 309, 122584. [Google Scholar] [CrossRef] [PubMed]
556. Westfall, S. , et al., Optimization of probiotic therapeutics using machine learning in an artificial human gastrointestinal tract. Scientific reports 2021, 11, 1067. [Google Scholar] [CrossRef]
557. Wu, S. , et al., metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model. Briefings in Bioinformatics 2024, 25, bbae085. [Google Scholar] [CrossRef]
558. Scott, B.M. , et al., Self-tunable engineered yeast probiotics for the treatment of inflammatory bowel disease. Nature medicine 2021, 27, 1212–1222. [Google Scholar] [CrossRef]
559. Ermolenko, E. , et al., Evaluation of the effectiveness of personalised therapy for the patients with irritable bowel syndrome. Beneficial Microbes 2023, 14, 119–129. [Google Scholar] [CrossRef]
560. McKay, R. , et al., A platform of genetically engineered bacteria as vehicles for localized delivery of therapeutics: Toward applications for Crohn's disease. Bioengineering & translational medicine 2018, 3, 209–221. [Google Scholar]
561. Joy, Z.H. , et al., INTEGRATING MACHINE LEARNING AND BIG DATA ANALYTICS FOR REAL-TIME DISEASE DETECTION IN SMART HEALTHCARE SYSTEMS. International Journal of Health and Medical 2024, 1, 16–27. [Google Scholar]
562. Yazzourh, S. , et al., Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes. arXiv preprint arXiv:, arXiv:2407.00364, 2024.
563. Zlobina, K. , et al., The role of machine learning in advancing precision medicine with feedback control. Cell Reports Physical Science, 2022. 3(11).
564. Mahmood, A.A.R., A. M. Jha, and K. Manivannan, Precision Medicine: Personalizing The Fight Against Cancer. International Journal of Trends in OncoScience, 2024: p. 10-18.
565. Prabhakar, P.K. , Advancements in Precision Medicine: Tailoring Healthcare to Individual Needs, in Biomedical Research Developments for Improved Healthcare, P.K. Prabhakar, Editor. 2024, IGI Global: Hershey, PA, USA. p. 310-326.
566. Seyhan, A.A. and C. Carini, Precision medicine: success stories and challenges from science to implementation, in The New Era of Precision Medicine. 2024, Elsevier. p. 83-113.
567. Tom, A. , Knowledge, Attitude and Ethical Perception Towards Precision Medicine Among Healthcare Professionals-a Questionnaire Survey. Journal of pharmaceutical research and development, 2024.
568. Altmann, P., et al., Challenges for reinforcement learning in quantum computing. arXiv preprint arXiv:2312.11337, 2023.
569. De Domenico, M., et al., Challenges and opportunities for digital twins in precision medicine: a complex systems perspective. arXiv preprint arXiv:2405.09649, 2024.
570. Li, M., et al. Hybrid Quantum Classical Machine Learning with Knowledge Distillation. in ICC 2024-IEEE International Conference on Communications. 2024. IEEE.
571. Lin, Z. , Progress and challenges in the symbiosis of AI with science and medicine. European Journal of Clinical Investigation, 2024: p. e14222-e14222.
572. Mahanty, B. , Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges. Biotechnology and Bioengineering 2023, 120, 2072–2091. [Google Scholar] [CrossRef] [PubMed]
573. Nayak, R., C. Rajaramesh, and U. Ghugar, Challenges in Building Predictive Models. Intelligent Techniques for Predictive Data Analytics, 2024: p. 25.
574. Ong, J.C.L., et al., Ethical and regulatory challenges of large language models in medicine. The Lancet Digital Health 2024, 6, e428–e432. [CrossRef] [PubMed]
575. Ullah, E. , et al., Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology–a recent scoping review. Diagnostic pathology 2024, 19, 43. [Google Scholar] [CrossRef] [PubMed]
576. Farina, L. , AI will not give us precision medicine. Ann Ist Super Sanità 2024, 60, 8–13. [Google Scholar]
577. Flores, J.E. , et al., Missing data in multi-omics integration: Recent advances through artificial intelligence. Frontiers in Artificial Intelligence 2023, 6, 1098308. [Google Scholar] [CrossRef]
578. Li, Z. , et al., Amalur: Data integration meets machine learning. IEEE Transactions on Knowledge and Data Engineering, 2024.
579. Martínez-García, M. and E. Hernández-Lemus, Data integration challenges for machine learning in precision medicine. Frontiers in medicine 2022, 8, 784455. [CrossRef]
580. Merieme, E.A., et al. A survey on the challenges of data integration. in 2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM). 2022.
581. Rapin, A., M.B. Sleiman, and J. Auwerx, Data integration in systems genetics and aging research. arXiv preprint arXiv:2207.03540, 2022.
582. Vidhya, G., D. Nirmala, and T. Manju, Quality challenges in deep learning data collection in perspective of artificial intelligence. Journal of Information Technology and Computing 2023, 4, 46–58. [CrossRef]
583. Dube, L. and T. Verster, Assessing the performance of machine learning models for default prediction under missing data and class imbalance: A simulation study. ORiON 2024, 40, 1–24. [Google Scholar]
584. Ferri, P. , et al., Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study. Computer Methods and Programs in Biomedicine 2023, 242, 107803. [Google Scholar] [CrossRef]
585. Shi, J. , et al., Implicit bias in Critical Care Data: Factors affecting sampling frequencies and missingness patterns of clinical and biological variables in ICU Patients. medRxiv, 2024: p. 2024.06. 09.24308661.
586. Emili, A. , State of the art in data integration and network biology. Authorea Preprints, 2024.
587. Gonesh Chandra Saha, M.B. , Muhammad Jahanzeb Khan, Hasi Saha,Dinesh Kumar, Avinash, Integrative Analysis of Multi-Omics Data with Deep Learning: Challenges and Opportunities in Bioinformatics. Tuijin Jishu/Journal of Propulsion Technology 2023, 44, 1384–1392. [Google Scholar]
588. Adams, A.K. , et al., Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes. Briefings in Bioinformatics 2023, 24, bbad351. [Google Scholar] [CrossRef]
589. Carrieri, A.P., et al. A fast machine learning workflow for rapid phenotype prediction from whole shotgun metagenomes. in Proceedings of the AAAI Conference on Artificial Intelligence. 2019.
590. Dghais, W., M. Alam, and Y. Chen, Real Time Modelling and Processing, in Real-Time Modelling and Processing for Communication Systems: Applications and Practices, M. Alam, W. Dghais, and Y. Chen, Editors. 2018, Springer International Publishing: Cham. p. 1-13.
591. Durge, A.R. and D.D. Shrimankar. MRQPMS: Design of a Map Reduce Bioinspired Model for Solving Quorum Planted Motif Search for High-Speed Deployments. in BIOINFORMATICS. 2023.
592. Khodabakhsh, A. , et al., Predicting decision-making time for diagnosis over ngs cycles: An interpretable machine learning approach. bioRxiv, 2023: p. 2023.03. 07.530760.
593. Nussinov, R. and J.A. Papin, How can computation advance microbiome research? 2017, Public Library of Science San Francisco, CA USA. p. e1005547.
594. Karuvally, A. and J.E.B. Moss, Model Complexity of Program Phases. arXiv preprint arXiv:2310.03865, 2023.
595. Prybutok, A.N. , et al., Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems. Current Opinion in Biotechnology 2022, 75, 102704. [Google Scholar] [CrossRef]
596. Raajaraam, L. and K. Raman, Modeling Microbial Communities: Perspective and Challenges. ACS Synthetic Biology 2024, 13, 2260–2270. [CrossRef]
597. V. Yu. Zadiraka, O.M.K. V. Yu. Zadiraka, O.M.K., Inna V. Shvidchenko, Models of Computer Calculations. Cybernetics and Computer Technologies, 2022(2): p. 38-51.
598. Gupta, I. , et al., A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
599. Sokolov, A., A. Larionov, and A. Mukhtarov. Distributed System for Scientific and Engineering Computations with Problem Containerization and Prioritization. in International Conference on Distributed Computer and Communication Networks. 2023. Springer.
600. Tiwari, R.K., A.B.B.A. Hamid, and T.E. Nyamasvisva. Potential in Quantum Machine Learning for Real-World Problems. in 2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST). 2024. IEEE.
601. Narotam Dass, A.K. , Mohit Sungroya, Optimizing real-time systems with parallel computing: techniques and challenges, in Futuristic Trends in Information Technology. 2024: IIP Series. p. 246-258.
602. Alzubaidi, L. , et al., A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. Journal of Big Data 2023, 10, 46. [Google Scholar] [CrossRef]
603. Chai, C., et al. Mitigating Data Scarcity in Supervised Machine Learning Through Reinforcement Learning Guided Data Generation. in 2024 IEEE 40th International Conference on Data Engineering (ICDE). 2024. IEEE.
604. Dens, C., et al., Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics. Journal of the American Society for Mass Spectrometry 2024, 35, 2143–2155. [CrossRef]
605. Li, Y., et al., StableQ: Enhancing Data-Scarce Quantization with Text-to-Image Data. arXiv preprint arXiv:2312.05272, 2023.
606. Niel, O., A novel algorithm can generate data to train machine learning models in conditions of extreme scarcity of real world data. arXiv preprint arXiv:2305.00987, 2023.
607. Hakami, A. , Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance. Scientific Reports 2024, 14, 9645. [Google Scholar] [CrossRef]
608. Xu, M. Machine Unlearning: Challenges in Data Quality and Access. in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24). 2024.
609. Xu, M. , et al., Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning. Frontiers in Plant Science 2023, 14, 1225409. [Google Scholar] [CrossRef]
610. Chen, J., et al., Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models. arXiv preprint arXiv:2406.12397, 2024.
611. Gandhi, S., et al., Better Synthetic Data by Retrieving and Transforming Existing Datasets. arXiv preprint arXiv:2404.14361, 2024.
612. Miletic, M. and M. Sariyar, Challenges of Using Synthetic Data Generation Methods for Tabular Microdata. Applied Sciences 2024, 14, 5975. [Google Scholar] [CrossRef]
613. Braiek, H.B. and F. Khomh, Machine Learning Robustness: A Primer. arXiv preprint arXiv:2404.00897, 2024.
614. Guillaume, F. , Genetic and environmental robustness are distinct yet correlated evolvable traits in a gene network. Peer Community In Evolutionary Biology, 2022: p. 100138.
615. Olsson, L., et al., Robustness: linking strain design to viable bioprocesses. Trends in Biotechnology 2022, 40, 918–931. [CrossRef] [PubMed]
616. Zhang, C., D. Garlan, and E. Kang. A behavioral notion of robustness for software systems. in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2020.
617. Herken, B.W. , et al., Environmental challenge rewires functional connections among human genes. bioRxiv, 2023.
618. Kochanowski, K. , et al., Dissecting the impact of metabolic environment on three common cancer cell phenotypes. bioRxiv, 2020: p. 2020.06. 23.167437.
619. Libiseller-Egger, J., et al., Environmental flexibility does not explain metabolic robustness. NPJ Systems Biology and Applications 2020, 6, 39.
620. Hanif, M.A. , et al., Robust computing for machine learning-based systems. Dependable Embedded Systems, 2021: p. 479-503.
621. Wang, S., Y. Jin, and M. Cai, Enhancing the robustness of networks against multiple damage models using a multifactorial evolutionary algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2023, 53, 4176–4188. [CrossRef]
622. Yan, Y. , Improve robustness of machine learning via efficient optimization and conformal prediction. AI Magazine, 2024.
623. Zhou, X., M. Kouzel, and H. Alemzadeh. Robustness testing of data and knowledge driven anomaly detection in cyber-physical systems. in 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). 2022. IEEE.
624. Habibullah, K.M., et al. Scoping of non-functional requirements for machine learning systems. in 2024 IEEE 32nd International Requirements Engineering Conference (RE). 2024. IEEE.
625. Liu, J. and S. Liu, Applications of Large Language Models in Clinical Practice: Path, Challenges, and Future Perspectives. OSF PREPRINTS, 2024.
626. Farazin, A.Z.A. , Advancements and Challenges in the Application of Machine Learning for Biomedical Diagnostics and Disease Prediction. SciBase Journals, 2024. 2(3).
627. Hamann, A. and S. Wölk, Performance analysis of a hybrid agent for quantum-accessible reinforcement learning. New Journal of Physics 2022, 24, 033044. [Google Scholar] [CrossRef]
628. Joshua, R. Bertram, B.R.W. and R.A. Mclean Angus L, Safe and secure practical autonomy, R. Collins, Editor. 2020.
629. Menta, S., et al., Learning Q-function approximations for hybrid control problems. IEEE Control Systems Letters 2021, 6, 1364–1369.
630. Rohde, W., M. Perepechaenko, and R. Kuang. Quantum-Secure Autonomous Factories: Hybrid TLS 1.3 for Inter-and Intra-plant Communication. in International Conference on Communication and Network Technology. 2022. Springer.
631. Wang, H., L. Stobaugh, and W. Stobaugh, Hybrid Quanvolutional Regression Neural Network: Quantum Advantage for Near-term Autonomous Machine. Authorea Preprints, 2024.
632. Zhang, H., et al., Controlling Sequential Hybrid Evolutionary Algorithm by Q-Learning [Research Frontier][Research Frontier]. IEEE Computational Intelligence Magazine 2023, 18, 84–103. [CrossRef]
633. Ziegler, J., et al., Toward robust autotuning of noisy quantum dot devices. Physical Review Applied 2022, 17, 024069. [CrossRef]
634. Bakirtzis, G., M. Chiou, and A. Theodorou, Negotiating Control: Neurosymbolic Variable Autonomy. arXiv preprint arXiv:2407.16254, 2024.
635. Da Poian, V., et al. Science Autonomy and Planetary Missions: ML and Data Science Applied to the ExoMars Mission. in 2023 IEEE Aerospace Conference. 2023. IEEE.
636. Fard, N.E., R. R. Selmic, and K. Khorasani, Public policy challenges, regulations, oversight, technical, and ethical considerations for autonomous systems: a survey. IEEE Technology and Society Magazine 2023, 42, 45–53. [Google Scholar] [CrossRef]
637. Hamad, M. and S. Steinhorst, Security Challenges in Autonomous Systems Design. arXiv preprint arXiv:2312.00018, 2023.
638. Saisubramanian, S., E. Kamar, and S. Zilberstein, Avoiding negative side effects of autonomous systems in the open world. Journal of Artificial Intelligence Research 2022, 74, 143–177. [CrossRef]
639. Samin, H., N. Bencomo, and P. Sawyer, Decision-making under uncertainty: be aware of your priorities. Software and Systems Modeling 2022, 21, 2213–2242. [CrossRef]
640. Straube, S. , et al., The Challenge of Autonomy: What We Can Learn from Research on Robots Designed for Harsh Environments, in Robots in Care and Everyday Life: Future, Ethics, Social Acceptance. 2022, Springer International Publishing Cham. p. 57-80.
641. Bruggeman, F.J., B. Teusink, and R. Steuer, Trade-offs between the instantaneous growth rate and long-term fitness: consequences for microbial physiology and predictive computational models. Bioessays 2023, 45, 2300015. [Google Scholar] [CrossRef] [PubMed]
642. Kapasiawala, M. and R.M. Murray, Metabolic perturbations to an E. coli-based cell-free system reveal a trade-off between transcription and translation. bioRxiv, 2024: p. 2023.03.22.533877.
643. Pittman, J.M., A Measure for Level of Autonomy Based on Observable System Behavior. arXiv preprint arXiv:2407.14975, 2024.
644. Sparks, E.E. and A. Rasmussen, Trade-offs in plant responses to the environment. 2023, Wiley Online Library. p. 2943-2945.
645. Abdelkader, H., et al. Robustness Attributes to Safeguard Machine Learning Models in Production. in 2023 IEEE Engineering Informatics. 2023. IEEE.
646. Pang, R.Y. and K. Reinecke, Anticipating Unintended Consequences of Technology Using Insights from Creativity Support Tools. arXiv preprint arXiv:2304.05687, 2023.
647. Shen, J., Z. Han, and W. Wang. Robustness of Modified Control Barrier Functions subject to External Disturbances. in 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA). 2023. IEEE.
648. Praveen Kumar, S.B. , Robustness Testing for AI/ML Models: Strategies for Identifying and Mitigating Vulnerabilities. International Journal of Science and Research (IJSR) 2024, 13, 923–930. [Google Scholar] [CrossRef]
649. Clay-Williams, R. , Complex systems and unintended consequences, in Implementation Science. 2022: Routledge.
650. Ferreira, R.S. Towards safety monitoring of ML-based perception tasks of autonomous systems. in 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). 2020. IEEE.
651. Uddin, J., M. Ahad, and A.H. Kafi. Wireless event-based kill-switch for safe and autonomous UAS operation. in 2023 International Conference on Electronics, Information, and Communication (ICEIC). 2023. IEEE.
652. Weiss, M. and P. Tonella. Fail-safe execution of deep learning based systems through uncertainty monitoring. in 2021 14th IEEE conference on software testing, verification and validation (ICST). 2021. IEEE.
653. Dinesh Reddy Chittibala, S.R.J. , Security in Machine Learning (ML) Workflows. International Journal of Computing and Engineering 2024, 5, 52–63. [Google Scholar] [CrossRef]
654. Causevic, A., A.V. Papadopoulos, and M. Sirjani. Towards a framework for safe and secure adaptive collaborative systems. in 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 2019. IEEE.
655. Chaudhary, S. On Adaptivity and Safety in Sequential Decision Making. in IJCAI. 2023.
656. Cho, M. and C. Sun. Constrained Meta-Reinforcement Learning for Adaptable Safety Guarantee with Differentiable Convex Programming. in Proceedings of the AAAI Conference on Artificial Intelligence. 2024.
657. Dubslaff, C., et al., Breaking the limits of redundancy systems analysis. arXiv preprint arXiv:1912.05364, 2019.
658. Henkel, E., et al. Scalable Redundancy Detection for Real-Time Requirements. in 2024 IEEE 32nd International Requirements Engineering Conference (RE). 2024. IEEE.
659. Kajmakovic, A., et al. Predictive fail-safe improving the safety of industrial environments through model-based analytics on hidden data sources. in 2018 IEEE 13th International Symposium on Industrial Embedded Systems (SIES). 2018. IEEE.
660. Ozer, E. , Safety-based prediction apparatus, system and method. 2022, Google Patents.
661. Zhu, Q., et al. Safety-assured design and adaptation of learning-enabled autonomous systems. in Proceedings of the 26th Asia and South Pacific Design Automation Conference. 2021.
662. Abdelkhalak El Hami, M.E. , Modeling of Systems with Redundancy, in Reliability-based Modeling of System Performance. 2023: Wiley Online Library.
663. Federico, C.A. and A.A. Trotsyuk, Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth. Annual Review of Biomedical Data Science, 2024. 7.
664. Johnson, S.G., G. Simon, and C. Aliferis, Regulatory Aspects and Ethical Legal Societal Implications (ELSI). Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls, 2024: p. 659-692.
665. Mestre, R., et al., Ethics and responsibility in biohybrid robotics research. Proceedings of the National Academy of Sciences 2024, 121, e2310458121.
666. Ou, Y. and S. Guo, Safety risks and ethical governance of biomedical applications of synthetic biology. Frontiers in Bioengineering and Biotechnology 2023, 11, 1292029.
667. Sharma, A.K. and R. Sharma, Navigating the Ethical Landscape: Implementing Machine Learning in Smart Healthcare Informatics. Indian Journal of Community Health 2024, 36, 149–152. [Google Scholar] [CrossRef]
668. Undheim, T.A. , The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks. Frontiers in Bioengineering and Biotechnology 2024, 12, 1359768. [Google Scholar] [CrossRef] [PubMed]
669. Kochetova, J.Y. , Ethical risks of artificial intelligence and prospects for joint decision-making in medicine. Čelovek 2024, 35, 96–106. [Google Scholar] [CrossRef]
670. Love, C.S. , “Just the Facts Ma’am”: Moral and Ethical Considerations for Artificial Intelligence in Medicine and its Potential to Impact Patient Autonomy and Hope. The Linacre Quarterly 2023, 90, 375–394. [Google Scholar] [CrossRef] [PubMed]
671. Saenz, A.D. , et al., Autonomous AI systems in the face of liability, regulations and costs. NPJ digital medicine 2023, 6, 185. [Google Scholar] [CrossRef]
672. Savulescu, J., et al., Ethics of artificial intelligence in medicine. Singapore Medical Journal 2024, 65, 150–158. [CrossRef] [PubMed]
673. Smith, D.H., et al. Ethics of Trust/worthiness in Autonomous Systems: a scoping review. in Proceedings of the First International Symposium on Trustworthy Autonomous Systems. 2023.
674. Uygun İlikhan, S., et al., How to mitigate the risks of deployment of artificial intelligence in medicine? Turkish Journal of Medical Sciences 2024, 54, 483–492. [CrossRef]
675. Youssef, A., M. Abramoff, and D. Char, Is the algorithm good in a bad world, or has it learned to be bad? The ethical challenges of “locked” versus “continuously learning” and “autonomous” versus “assistive” ai tools in healthcare. The American Journal of Bioethics 2023, 23, 43–45. [Google Scholar] [CrossRef]
676. Ansarullah, S.I. , et al., Ethical issues around artificial intelligence, in A Biologist s Guide to Artificial Intelligence. 2024, Elsevier. p. 301-314.
677. Grabb, D., M. Lamparth, and N. Vasan, Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation. medRxiv, 2024: p. 2024.04. 07.24305462.
678. Kläes, M., et al. Handling uncertainties of data-driven models in compliance with safety constraints for autonomous behaviour. in 2021 17th European Dependable Computing Conference (EDCC). 2021. IEEE.
679. Lupo, G., B. Q. Vo, and N. Locke, Continuous automation approach for autonomous ethics-based audit of AI systems, in Trolley Crash. 2024, Elsevier. p. 141-162.
680. Prunkl, C. , Human Autonomy at Risk? An Analysis of the Challenges from AI. Minds and Machines 2024, 34, 26. [Google Scholar] [CrossRef]
681. Ong, J.C.L. , et al., Medical Ethics of Large Language Models in Medicine. NEJM AI, 2024: p. AIra2400038.
682. Sambare, G. , et al., Autonomous Healthcare Systems: Deep Learning-Based IoT Solutions for Continuous Monitoring and Adaptive Treatment. Journal of Electrical Systems, 2024. 20(1s): p. 393-407.
683. Sparrow, R. , et al., Should the use of adaptive machine learning systems in medicine be classified as research? The American Journal of Bioethics, 2024: p. 1-12.
684. Bakar, N.F.A. , et al., Environmental impact of quantum dots, in Graphene, Nanotubes and Quantum Dots-Based Nanotechnology. 2022, Elsevier. p. 837-867.
685. Dolezel, M. , et al., Challenges for the Post-Market Environmental Monitoring in the European Union Imposed by Novel Applications of Genetically Modified and Genome-Edited Organisms. BioTech 2024, 13, 14. [Google Scholar] [CrossRef]
686. James, C.A. , et al., The screening and prioritization of contaminants of emerging concern in the marine environment based on multiple biological response measures. Science of The Total Environment 2023, 886, 163712. [Google Scholar] [CrossRef]
687. Koren, K. and C.M. McGraw, Let’s Talk about Slime; or Why Biofouling Needs More Attention in Sensor Science. ACS sensors 2023, 8, 2432–2439. [Google Scholar] [CrossRef] [PubMed]
688. Lensch, A. , et al., Safety aspects of microorganisms deliberately released into the environment. EFB Bioeconomy Journal, 2023: p. 100061.
689. Tran, T.-K. , et al., Review on fate, transport, toxicity and health risk of nanoparticles in natural ecosystems: Emerging challenges in the modern age and solutions toward a sustainable environment. Science of The Total Environment, 2023: p. 169331.
690. Zeng, X., et al., Regulation and management of the biosecurity for synthetic biology. Synthetic and Systems Biotechnology 2022, 7, 784–790. [CrossRef] [PubMed]
691. Godwin, S. , et al., Environmental Health and Safety Offers a Biosafety Risk Assessment for a Theoretical Model of a Gene Therapy Process Transfer from Research and Development to Large-Scale Manufacturing. Applied Biosafety 2023, 28, 164–175. [Google Scholar] [CrossRef] [PubMed]
692. Niegowska, M. and M. Wögerbauer, Improving the risk assessment of antimicrobial resistance (AMR) along the food/feed chain and from environmental reservoirs using qMRA and probabilistic modelling. EFSA Journal 2022, 20, e200407. [Google Scholar] [CrossRef]
693. Saarimäki, L.A., G. Del Giudice, and D. Greco, Expanding adverse outcome pathways towards one health models for nanosafety. Frontiers in Toxicology 2023, 5, 1176745. [CrossRef]
694. Chen, J., V. Storchan, and E. Kurshan, Beyond fairness metrics: Roadblocks and challenges for ethical ai in practice. arXiv preprint arXiv:2108.06217, 2021.
695. Di Cara, N. , et al., Using Data Hazards to support safe and ethical digital footprint research. International Journal of Population Data Science, 2023. 8(3).
696. Janarthanan, V. , et al., Legal and Ethical Issues Associated With Challenges in the Implementation of the Electronic Medical Record System and Its Current Laws in India. Cureus, 2024. 16(3).
697. Mandeep Singh, S.B. , Kanhaiya Sharma, Ethical and Privacy Considerations in Medical Big Data: Balancing Innovation and Safeguarding Patient. International Journal of Research Publication and Reviews, 2024. 5(5).
698. Suresh, N. , et al., Ethical Considerations in AI Implementation for Patient Data Security and Privacy, in AI Healthcare Applications and Security, Ethical, and Legal Considerations. 2024, IGI Global. p. 139-147.
699. Al-kfairy, M., et al. Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective. in Informatics. 2024. MDPI.
700. Shivagouda M Patil, P.T., Mr. Hrishikesh Mogare, Regulatory Frameworks for Ethical AI Development in Coding. International Journal for Research in AppliedScience & Engineering Technology (IJRASET 2024, 12, 2018–2024. [Google Scholar]
701. Baena-Miret, S., F. Reverter, and E. Vegas, A framework for block-wise missing data in multi-omics. Plos one 2024, 19, e0307482.
702. Ivanisevic, T. and R.N. Sewduth, Multi-omics integration for the design of novel therapies and the identification of novel biomarkers. Proteomes 2023, 11, 34. [Google Scholar] [CrossRef]
703. Lac, L., C. K. Leung, and P. Hu, Computational frameworks integrating deep learning and statistical models in mining multimodal omics data. Journal of Biomedical Informatics, 2024: p. 104629.
704. Mohr, A.E. , et al., Navigating challenges and opportunities in multi-omics integration for personalized healthcare. Biomedicines 2024, 12, 1496. [Google Scholar] [CrossRef]
705. Nam, Y. , et al., Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. Annual Review of Biomedical Data Science, 2024. 7.
706. Yang, G.-H. , et al., A hybrid forecasting framework based on MCS and machine learning for higher dimensional and unbalanced systems. Physica A: Statistical Mechanics and its Applications 2024, 637, 129612. [Google Scholar] [CrossRef]
707. Zhou, Y. , et al., Utilizing AI-Enhanced Multi-Omics Integration for Predictive Modeling of Disease Susceptibility in Functional Phenotypes. Journal of Theory and Practice of Engineering Science 2024, 4, 45–51. [Google Scholar] [CrossRef] [PubMed]
708. Chetty, A. and R. Blekhman, Multi-omic approaches for host-microbiome data integration. Gut Microbes 2024, 16, 2297860.
709. Dang, T. , et al., An integrative framework of stochastic variational variable selection for joint analysis of multi-omics microbiome data. bioRxiv, 2023: p. 2023.08. 18.553796.
710. Gonçalves, D.M., R. Henriques, and R.S. Costa, Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches. Computational and Structural Biotechnology Journal 2023, 21, 4960–4973. [CrossRef] [PubMed]
711. Can, H. , et al., Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge. Cells 2023, 12, 1998. [Google Scholar] [CrossRef]
712. Gonesh Chandra Saha, M.B. , Muhammad Jahanzeb Khan, Hasi Saha,Dinesh Kumar, Avinash, Integrative Analysis of Multi-Omics Data with Deep Learning: Challenges and Opportunities in Bioinformatics. Tuijin Jishu/Journal of Propulsion Technology, 2023. 44(3).
713. Avesani, S., et al. MODIMO: Workshop on Multi-Omics Data Integration for Modelling Biological Systems. in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023.
714. Muller, E., I. Shiryan, and E. Borenstein, Multi-omic integration of microbiome data for identifying disease-associated modules. Nature Communications 2024, 15, 2621.
715. Jindal, M. , Advancements in Cloud-Based Machine Learning: Navigating Deployment and Scalability. International Journal For Multidisciplinary Research, 2023. 5(6).
716. Padmanaban, H. , Machine Learning Algorithms Scaling on Large-Scale Data Infrastructure. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2024, 3, 171–196. [Google Scholar]
717. Tarzi, C. , et al., Emerging methods for genome-scale metabolic modeling of microbial communities. Trends in Endocrinology & Metabolism, 2024.
718. Zimelewicz, E., et al. ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems. in International Conference on Software Quality. 2024. Springer.
719. Chen, M. and J. Xia, Optimization parameters for efficient scale-up of fermentation process, in Scale-up and Chemical Process for Microbial Production of Plant-Derived Bioactive Compounds. 2024, Elsevier. p. 29-42.
720. Xu, B. , et al., Quorum Quenching in Membrane Bioreactors for Fouling Retardation: Complexity Provides Opportunities. Environmental Science & Technology 2024, 58, 13171–13193. [Google Scholar]
721. Xu, J. , et al., Service Function Chain Deployment Using Deep Q Learning and Tidal Mechanism. IEEE Internet of Things Journal, 2023.
722. Li, J. and Q. Yu, Scientists’ disciplinary characteristics and collaboration behaviour under the convergence paradigm: A multilevel network perspective. Journal of Informetrics 2024, 18, 101491. [Google Scholar] [CrossRef]
723. Harle, S.M. , Integrative Approaches to Tackle Multidisciplinary Challenges: A Review of Multi-science Problem Analysis. Current materials science (Bentham Science), 2024. 17.
724. Boon, M. , Interdisciplinarity through modelling, in The Routledge Handbook of Philosophy of Scientific Modeling. 2024: Routledge. p. 395-411.
725. Busquim, G. , et al., Towards Effective Collaboration between Software Engineers and Data Scientists developing Machine Learning-Enabled Systems. arXiv preprint arXiv:, arXiv:2407.15821, 2024.
726. Pastor, Ó., et al. Towards an AI-based Genomic Medicine of Precision that Integrates Predictive and Explainable Knowledge Dimensions. in Congresso Ibero-Americano em Engenharia de Software (CIbSE). 2024. SBC.
727. Turanli, B. , et al., Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Molecular Omics 2024, 20, 234–247. [Google Scholar] [CrossRef]
728. Wu, Y. and L. Xie, AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationships. arXiv preprint arXiv:2407.06405, 2024.