"Assembly Theory" in life-origin models: A critical review.

Any homeostatic protometabolism would have required orchestration of disparate biochemical pathways into integrated circuits. Extraordinarily specific molecular assemblies were also required at the right time and place. Assembly Theory conflated with its cousins-Complexity Theory, Chaos theory, Quantum Mechanics, Irreversible Nonequilibrium Thermodynamics and Molecular Evolution theory- collectively have great naturalistic appeal in hopes of their providing the needed exquisite steering and controls.

Selection in molecular evolution.

Evolution requires selection. Molecular/chemical/preDarwinian evolution is no exception. One molecule must be selected over another for molecular evolution to occur and advance.

Redundancy of the genetic code enables translational pausing.

The codon redundancy ("degeneracy") found in protein-coding regions of mRNA also prescribes Translational Pausing (TP). When coupled with the appropriate interpreters, multiple meanings and functions are programmed into the same sequence of configurable switch-settings. This additional layer of Ontological Prescriptive Information (PIo) purposely slows or speeds up the translation-decoding process within the ribosome.

Dichotomy in the definition of prescriptive information suggests both prescribed data and prescribed algorithms: biosemiotics applications in genomic systems.

The fields of molecular biology and computer science have cooperated over recent years to create a synergy between the cybernetic and biosemiotic relationship found in cellular genomics to that of information and language found in computational systems. Biological information frequently manifests its "meaning" through instruction or actual production of formal bio-function. Such information is called prescriptive information (PI).

Is life unique?

Is life physicochemically unique? No. Is life unique? Yes. Life manifests innumerable formalisms that cannot be generated or explained by physicodynamics alone.

The Universal Plausibility Metric (UPM) & Principle (UPP).

Background: Mere possibility is not an adequate basis for asserting scientific plausibility. A precisely defined universal bound is needed beyond which the assertion of plausibility, particularly in life-origin models, can be considered operationally falsified. But can something so seemingly relative and subjective as plausibility ever be quantified? Amazingly, the answer is, "Yes.

The capabilities of chaos and complexity.

To what degree could chaos and complexity have organized a Peptide or RNA World of crude yet necessarily integrated protometabolism? How far could such protolife evolve in the absence of a heritable linear digital symbol system that could mutate, instruct, regulate, optimize and maintain metabolic homeostasis? To address these questions, chaos, complexity, self-ordered states, and organization must all be carefully defined and distinguished. In addition their cause-and-effect relationships and mechanisms of action must be delineated. Are there any formal (non physical, abstract, conceptual, algorithmic) components to chaos, complexity, self-ordering and organization, or are they entirely physicodynamic (physical, mass/energy interaction alone)? Chaos and complexity can produce some fascinating self-ordered phenomena.

The GS (genetic selection) Principle.

The GS (Genetic Selection) Principle states that biological selection must occur at the nucleotide-sequencing molecular-genetic level of 3'5' phosphodiester bond formation. After-the-fact differential survival and reproduction of already-living phenotypic organisms (ordinary natural selection) does not explain polynucleotide prescription and coding. All life depends upon literal genetic algorithms.

Measuring the functional sequence complexity of proteins.

Background: Abel and Trevors have delineated three aspects of sequence complexity, Random Sequence Complexity (RSC), Ordered Sequence Complexity (OSC) and Functional Sequence Complexity (FSC) observed in biosequences such as proteins. In this paper, we provide a method to measure functional sequence complexity.

Methods And Results: We have extended Shannon uncertainty by incorporating the data variable with a functionality variable.

Three subsets of sequence complexity and their relevance to biopolymeric information.

Genetic algorithms instruct sophisticated biological organization. Three qualitative kinds of sequence complexity exist: random (RSC), ordered (OSC), and functional (FSC). FSC alone provides algorithmic instruction.