Version 1
: Received: 3 September 2024 / Approved: 4 September 2024 / Online: 5 September 2024 (05:01:04 CEST)
How to cite:
Montgomery, R. M. Modeling Enzyme Dynamics in Metabolomics: A Topological Approach to Visualizing Adaptive Network Interactions Under Environmental Disturbances. Preprints2024, 2024090342. https://doi.org/10.20944/preprints202409.0342.v1
Montgomery, R. M. Modeling Enzyme Dynamics in Metabolomics: A Topological Approach to Visualizing Adaptive Network Interactions Under Environmental Disturbances. Preprints 2024, 2024090342. https://doi.org/10.20944/preprints202409.0342.v1
Montgomery, R. M. Modeling Enzyme Dynamics in Metabolomics: A Topological Approach to Visualizing Adaptive Network Interactions Under Environmental Disturbances. Preprints2024, 2024090342. https://doi.org/10.20944/preprints202409.0342.v1
APA Style
Montgomery, R. M. (2024). Modeling Enzyme Dynamics in Metabolomics: A Topological Approach to Visualizing Adaptive Network Interactions Under Environmental Disturbances. Preprints. https://doi.org/10.20944/preprints202409.0342.v1
Chicago/Turabian Style
Montgomery, R. M. 2024 "Modeling Enzyme Dynamics in Metabolomics: A Topological Approach to Visualizing Adaptive Network Interactions Under Environmental Disturbances" Preprints. https://doi.org/10.20944/preprints202409.0342.v1
Abstract
Metabolomics studies require a deep understanding of enzyme dynamics and their responses to environmental disturbances within a complex metabolic network. This paper presents a novel approach to modeling enzyme activity as a dynamic topological manifold, where each enzyme is represented as a node, and their interactions are described by differential equations. These equations account for both compensatory interactions and external perturbations, simulating how enzymes dynamically stabilize their activities in response to varying conditions. We explore the application of this model to visualize enzyme interactions, using graph theory to represent the network structure and color gradients to illustrate interaction intensities. Multiple disturbances are introduced to analyze the network's resilience and adaptability over time. The results provide insights into the compensatory mechanisms within enzymatic networks, offering a comprehensive visualization through both static and dynamic representations. Our approach allows for a better understanding of how enzymatic systems buffer against mutations and environmental stressors, contributing to the broader field of systems biology and metabolomics.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
