Co-evolution models postulate that the emergence of translation was necessary for the replication of nucleic acids. This is in contrast to the RNA world hypothesis which believes the emergence of translation was preceded by the era of self-replicating RNAs. Co-evolution scenarios require the highly unlikely simultaneous emergence of two classes of organic molecules, as well as the emergence of synchronized replication and translation. But the authors point out the major advantage of co-evolution models is that they explain the development of processive and much more accurate protein-dependent replication.
Wong believes that genetic information arose from replicator induction by metabolite in accordance with the metabolic expansion law. Messenger RNA and transfer RNA stemmed from a template for binding the aminoacyl-RNA synthetase ribozymes employed to synthesize peptide prosthetic groups on RNAs in the Peptidated RNA World. Coevolution of the genetic code with amino acid biosynthesis is believed to have generated tRNA paralogs that identify a last universal common ancestor (LUCA) of extant life close to Methanopyrus. This might suggest that archaeal tRNA introns were the most primitive introns. The anticodon usage of Methanopyrus might have been an ancient mode of wobble.
Missing from co-evolution models is pragmatic directionality and the steering and control needed to orchestrate a more holistic formal scheme. Although both Digiulio and Guimaraes deal with “coding,” their approach seeks to reduce “code” to mere physicodynamics. The essence of formal representationalism using symbolically-coded instructions and message meaning is lost.
4.1. Biosemiotics and Code Biology
Biosemiosis interest began with Charles Peirce [
120]. Many have contributed to the development of this field, with their most recent contributions including von Uexküll [
121], Varela FJ [
122,
123], Pattee [
124,
125,
126,
127], Rosen [
128,
129,
130,
131], Sebeok [
132,
133], Hoffmeyer [
134,
135,
136], Emmeche [
137] Kull [
138,
139,
140], El-Hani [
141], Sharov [
142,
143,
144,
145], Barbieri (cited below), and Kull [
138,
140,
146,
147,
148,
149]
Rosen’s contribution was considerable [
128,
129,
130,
131]. Most significant was his dichotomy between formal vs physico-dynamic. But from an abiogenist’s perspective, his notion of selection was unfortunately limited to the very inadequate after-the-fact, secondary, passive selection of “natural selection.” The orchestration of even a protometabolism required active, not passive selection. It required selection “in order to . . . ,” not merely selection “from among” already optimized functions or organisms.
Marcello Barbieri has extensively emphasized the role of a myriad of codes in biology [
150,
151,
152,
153,
154,
155].
The Biosemiosis and Code Biology movements in general [
150,
152,
156,
157,
158,
159,
160] stress the reality of life’s subcellular
messaging. Little is found in biosemiotic literature, however, addressing the need for
prescription of message content (instructions) at the prebiotic subcellular level [
161]. Not even a protometabolism just happens. Biochemical pathways have to lead or pushed somewhere useful. Sophisticated bifunctionality and biosystems have to be integrated into connected pathway circuits with configurable switch settings. This orchestration had to have arisen in an inanimate pre-evolutionary environment prior to LUCA (Last Universal Common Ancestor). Feedback mechanisms depend upon already- existing biochemical pathways.
Barbieri in the last decade has pursued a much more materialistic approach to biological codes than most other biosemioticians. Probably fearing the accusation that biosemiotics emphasis would be accused of not being scientific, Barbieri shifted his emphasis to “Code Biology.” He apparently felt this would de-emphasize the essential meaning aspects of message content. But the reality of biomessage meaning is unavoidably inherent in programming and other forms of biological instruction and control. Life is computation. Computation is formal, not physical.
Many others have pursued the biosemiotic realities of subcellular life. [
69,
159,
160,
161,
162,
163,
164,
165,
166,
167,
168,
169,
170].
Most biosemioticians are more willing than Barbieri to acknowledge the need for “interpretation” of message meaning. Almost none admit to the need for active selection of signs/symbols. For the most part, biosemioticians still remain locked into a primarily materialistic axiom that precludes investigating the required active selection of prebiotic sign and rule selection. Physicodynamics offers no hint of explanation for any message meaning or purpose, including the co-evolution models of “code” discussed above.
The reality of biological code, message and the prescription of function at distant locations within the cell is quite real rather than metaphorical [
69,
103,
113,
171,
172,
173,
174,
175,
176,
177].
Federico Vega [
178] and Kravchenko [
179] disagree with Barbieri that interpretation is irrelevant for biosemiosis. According to Vega and Kravchenko, Barbieri views coding as the sole mechanism of semiosis in the organic world. These two authors, on the other hand, argue that the concept of “code” as a one-to-one correspondence between two sets of objects (sign vehicles) cannot explain living organization. Interpretation is seen as an organism’s adaptive response to the environment. Vega and Kravchenko both advocate adopting a more comprehensive biosemiotic theory that focuses on its relational nature. They emphasize the role of selection of interpretable signs to create instruction [
178].
Semiosis is mediated through the representationalism of signs/symbols and arbitrarily agreed-upon rules by sender and recipient. This is exampled by not only the codon table, but also by superimposed translational pausing coding [
180]. Interpretation of symbols and symbol meaning is
everything if any sophisticated biofunction is to be realized. A physicodynamic cause and effect linkage is not what mediates biosemiosis. Meaning constitutes the essence of the message. But we are not just dealing with biological subcellular messaging. Abiogenists must address
prescription of formal biofunction and biosystems.
Kull wisely ponders a non-anthropomorphic biological understanding of free choice in a very recent paper [
146]. All too many biosemiosis papers center only on organismic sentience, perception, agency and psychology. They are therefore of little interest to abiogenists. Organisms do not yet exist. Despite this shortcoming, Kull’s interest in non-anthropocentric biological choice is most welcome.
Kull analyses the structure and roles of biological choice [
146]. He rightly argues that choice does not necessarily require purpose. But one wonders what choice without intent would accomplish. Of interest to abiogenists is the orchestration of protometabolism in an inanimate environment. Organismal
sentience and choice are not relevant yet. Subcellular, efficacious, active selection at many different levels had to take place prior to the realization of the many needed productive pathways and integrated circuits of any protolife. Life is undeniably programmed and cybernetically processed. How was Turing’s “halting problem” overcome? Certainly not by choice without purpose. Coin flip “choices” at mere bifurcation points are not decision nodes. Coin flips do not constitute efficacious programming. Neither do “non-equilibrium phase transition instabilities” championed by Wills and Carter [
181,
182,
183,
184]
Alexei Sharov has also addressed many semiotic and proto-cybernetic issues [
142,
143,
144,
145]. Sharov and Vehkavaara wisely distinguish between protobiosemiosis vs. eubiosemiosis [
145]. They contend that protobiosemiosis started with signs associated with actions within the origin of life. How would “associated with” translate to “causation” of any biosystem? They feel that eubiosemiosis started when evolving agents acquired the ability to track and classify objects. Do micelles and coacervates “track and classify?” They speak of “proto-signs” that can be classified into proto-icons. Proto-icons can signal via single specific interaction. Proto-indexes can combine several functions. Proto-symbols are processed by a universal subagent equipped with a set of heritable adapters [
145]. This is all very imaginative. How scientifically plausible these successively linked steps are remains problematic.
Peter Wills is among many who have attempted to find a materialistic basis for coding and semiosis [
184]. In his and Carter’s most recent papers [
181,
182,
183,
184,
185,
186,
187], we find somewhat beclouded models of origin that make falsification difficult. Empirical support for mere instabilities in non-equilibrium phase transitions writing Prescriptive Information and controls needed to orchestrate even a protometabolism remains painfully lacking.
Biosemioticians, in particular, frequently attempt to perform delicate materialistic dances around formal representational concepts such as “signs,” ‘symbols,” “tokens, “meaning,” arbitrary “rules” (as opposed to laws), and “agency” in the selection of symbols that alone makes semiosis possible. Although tokens may be “physical symbol vehicles,” they still must be chosen to generate any “meaning” in any material symbol system (MSS) [
188,
189].
Rocha has continued to address codes and semiosis in greater detail [
190,
191,
192,
193,
194], although the semiotic processes supposedly elucidated in all of these models often resembles more of a shell game and linguistic obfuscation than anything scientific.
Kalevi Kull is perhaps the bravest of all biosemioticians in acknowledging the third fundamental category of reality [
172], Choice Causation, and its necessity for any form of semiosis, biosemiosis included. But Kull does not seem to address the necessity of choice causation at the sub-cellular, pre-biotic, molecular biological level of abiogenesis. It is not sufficient to discuss organisms’ choices. Choice Causation is necessary to orchestrate any sub-cellular protometabolism or initial protocell.
In 2022, Chatterjee and Yadav [
195] defined three different prebiotic information systems: analog, hybrid, and digital. They hypothesized that the Analog Information System (AIS) was manifest early in abiogenesis, was expressed in chiral selection, nucleotide formation, self-assembly, polymerization, encapsulation of polymers, and division of protocells. AIS created noncoding RNAs by polymerizing nucleotides that gave rise to the Hybrid Information System (HIS). In their scenario, the HIS employed different species of noncoding RNAs, such as ribozymes, pre-tRNA and tRNA, ribosomes, and functional enzymes, including bridge peptides, pre-aaRS, and aaRS (aminoacyl-tRNA synthetase). Some of these hybrid components supposedly built the translation machinery step-by-step. Note the hidden presupposition of “goal” in “step-by-step.” The HIS ushered in the Digital Information System (DIS), where tRNA molecules become molecular architects for designing mRNAs step-by-step, employing their two distinct genetic codes. They then compared the three kinds of biological information systems with similar types of human-made computer systems. The authors seemed oblivious to the full import of their own analogous comparison with human cybernetics. They could not see the obvious steering and control mechanisms that would have been required for bona fide organization and orchestration of any one biosystem they described. No explanation is provided for how mere physico-chemical causation could have accomplished any one of these biological computational haltings. No perception was apparent of the need for Prescriptive Information (PI) [
68,
69] (instructions and recipe), and the processing of that programming required for the engineering and progression of the integrated circuits that they describe.
Marijuán and Navarro [
196] focus on biomolecular information flow and processing in the development of biological complexity. They incorporate fundamental conceptualizations on the mechanisms of molecular recognition and informational architectures, the life cycle, and the characterization of meaning. Their informational approach depicts an indefinite series of recursion processes performed in the open-ended environment of the real world, potentially affected by multiple contingencies modifying the informational architectures involved in recursion. They pursue a power law that might interconnect the variability outcomes of the different evolutionary “vehicles” or variation modes.
Prinz [
197]
believes that biocomplexity can be attributed to the presence of codes, a unique organizational principle of living systems. He distinguishes biocomplexity from mere complexity, and attempts to measure “biocomplexity.” Prinz u
ses a code-based measure of biocomplexity expressed as a simple formula: “codes/components = complexity.” He feels this allows quantification of biological complexity across a wide range of biosystems and biological taxa. His method combines informational concepts (i.e., Marcello Barbieri's conception of biological codes [
152]
) with quantitative behavior of biomolecular components. The proposed formula incorporates both the number of components in a cell and the number of interactions constituting its underlying molecular networks. All molecular processes in between anabolism and catabolism require tight regulation and coordination. The control of these processes helps define Prinz’s definition of “biocomplexity.”
In 2023, Prinz [
156] draws further attention to the potential contribution of biological codes to the course and dynamics of evolution. Prinz argues that the concept of organic codes, developed by Marcello Barbieri [
150,
151,
152,
153,
154], has fundamentally changed our view of how living systems function. The notion that molecular interactions built on adaptors that arbitrarily link molecules from different "worlds" in an arbitrary/conventional, rule-based way, departs significantly from the law-based constraints imposed on living things by physical and chemical mechanisms. Prinz argues that living things operate on the basis of rules, whereas non-living things behave according to laws. He argues that this critical distinction is rarely if ever considered in current evolutionary theory. The many known codes allow quantification of codes that relate to a cell, or comparisons between different biological systems and may pave the way to a quantitative and empirical research agenda in code biology. Prinz advocates a simple dichotomous classification of structural and regulatory codes. This classification can be used as a tool to analyze and quantify key organizing principles of the living world, such as modularity, hierarchy, and robustness, based on organic codes. The implications for evolutionary research are related to the unique dynamics of codes, or “Eigendynamics” (self-momentum) and how they determine the behavior of biological systems from inside, whereas physical constraints are imposed mainly from outside. A speculation on the drivers of macroevolution in light of codes is followed by the conclusion that a meaningful and comprehensive understanding of evolution depends on including codes into the equation of life.
Douglas Axe [
198] in a quality paper in the Journal of Molecular Biology calculated the improbability of functional protein folds to be only one in 10^77. Tertiary protein structure doesn’t just happen. We have not even begun to address biochemical pathways or biosystems here. We are just talking about achieving the functional folding of a single protein! How was this statistical prohibitiveness narrowed down in a prebiotic environment to achieve the selection of what was needed? Did an inanimate environment sense or care about “needs”?
4.8. The Need for Prescriptive Information (PI)
A common contention is that the instructions to organize and orchestrate life came from a template, typically from a ribozyme or other RNA analog (an auto-catalytic RNA-like precursor). The question is, where did the templated instructions come from? Mere Clay surface? Since when does mere clay (e.g., montmorillonite) contain formal instructions to do anything sophisticated?
A short 200 mer protein has 20
200 permutations. And that phase space would be racemic. The number of permutations is way larger than 10
50. Only a very small percentage of these permutations fold into functional tertiary structures [
198]. Thus, most Protein-First models of abiogenesis are statistically prohibitive. But the real questions are, “How did inanimate nature sequence linear digital instructions out of this phase space?” How did prebiotic nature assign formal arbitrary code assignments and meaning to those assignments? What were the scientific mechanisms for achieving transcription and translation? Just two classes of aminoacyl-tRNA synthetases (aaRS’s I and II) do not provide adequate answers. These are
not chemical reaction problems. They are programming delegations. Coding and translation from one language into another are
not physico-chemical. They are abstract. Biosemiosis can be instantiated into physical symbol vehicles (tokens) within a Material Symbol System [
103,
171,
188,
189]. But the coded instructions themselves are abstract, not physical. Prescriptive Information (PI) [
68,
69,
204] cannot be reduced to physicality, non-equilibrium thermodynamic instabilities included.
We have no explanation for the interactome’s
conceptual complexity. To instruct sophisticated function requires abstract concept. Concept is formal, not physical. So is computation. Concept can be instantiated into physicality according to rules and arbitrary code assignments. But concept cannot be mustered by the laws of motion or mere physico-dynamic constraints. Even a protometabolism would have required controls rather than constraints [
217,
218,
219]. Controls emanate from concept, not fixed redundant law. They are choice contingent. Controls fall into the fundamental category of Choice Causation (CC), not Physicodynamic Causation (PC) [
67,
220,
221,
222]
Materials and Methods invariably prove the opposite of what physicalist abiogenists wanted to prove. Experimental design consistently betrays “investigator involvement.” Every reactant is carefully and actively selected at the just the needed stage of reaction sequence. Reactions are steered to desired end-points. While the title of each paper invokes the contention of “natural process,” the experimental achievements are all invariably engineered by agent-controlled lab techniques. Exact measurements, deliberate and careful sequencing of reactions and critical removals of reactants at the needed times from the reaction environment are the most common features of agent-controlled experimental design. Neglect of these details, and organic labs become tar factories every time.