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First Gene: Functional sequence complexity (FSC) found in biopolymers as well as human languages and computer code

Here’s the Abstract for Chapter 5 of The First Gene:

Functional Sequence Complexity in Biopolymers

Kirk K. Durston & David K.Y. Chiu

Department of computer science, Bioinformatics, University of Guelph

ABSTRACT. It is generally recognized that biopolymers such as DNA, RNA and proteins demonstrate a form of sequence complexity. Recent work has provided a more detailed insight into biopolymeric complexity by introducing three types of sequence complexity, Random Sequence Complexity (RSC), Ordered Sequence Complexity (OSC) and Functional Sequence Complexity (FSC). The primary feature of FSC that distinguishes it from RSC and OSC, is the imposition of functional controls upon the sequence. In this paper, we propose that it can be measured using an extended form of Shannon uncertainty that includes a variable of functionality. Clearly, FSC can be found in human languages and carefully designed computer code, but the measure we propose in this paper reveals that it is also found in biopolymers. In the case of proteins, the measure of FSC provides an estimate for the target size of a protein family in the amino acid sequence space, revealing that functional sequences occupyan extremely small fraction of sequence space. Due to the miniscule size of functional sequence space for a given protein family, as mutations accumulate there will be an increasing likelihood of moving the mutated sequence outside that space, with a corresponding deleterious effect on FSC.

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One Response to First Gene: Functional sequence complexity (FSC) found in biopolymers as well as human languages and computer code

  1. Intelligent Design – Kirk Durston – video

    Excerpted clip from preceding video

    Mathematically Defining Functional Information In Molecular Biology – Kirk Durston – video

    a few relevant papers:

    Measuring the functional sequence complexity of proteins – Kirk K Durston, David KY Chiu, David L Abel and Jack T Trevors – 2007
    Excerpt: We have extended Shannon uncertainty by incorporating the data variable with a functionality variable. The resulting measured unit, which we call Functional bit (Fit), is calculated from the sequence data jointly with the defined functionality variable. To demonstrate the relevance to functional bioinformatics, a method to measure functional sequence complexity was developed and applied to 35 protein families.,,,

    Three subsets of sequence complexity and their relevance to biopolymeric information – Abel, Trevors
    Excerpt: Shannon information theory measures the relative degrees of RSC and OSC. Shannon information theory cannot measure FSC. FSC is invariably associated with all forms of complex biofunction, including biochemical pathways, cycles, positive and negative feedback regulation, and homeostatic metabolism. The algorithmic programming of FSC, not merely its aperiodicity, accounts for biological organization. No empirical evidence exists of either RSC of OSC ever having produced a single instance of sophisticated biological organization. Organization invariably manifests FSC rather than successive random events (RSC) or low-informational self-ordering phenomena (OSC).,,,

    Testable hypotheses about FSC

    What testable empirical hypotheses can we make about FSC that might allow us to identify when FSC exists? In any of the following null hypotheses [137], demonstrating a single exception would allow falsification. We invite assistance in the falsification of any of the following null hypotheses:

    Null hypothesis #1
    Stochastic ensembles of physical units cannot program algorithmic/cybernetic function.

    Null hypothesis #2
    Dynamically-ordered sequences of individual physical units (physicality patterned by natural law causation) cannot program algorithmic/cybernetic function.

    Null hypothesis #3
    Statistically weighted means (e.g., increased availability of certain units in the polymerization environment) giving rise to patterned (compressible) sequences of units cannot program algorithmic/cybernetic function.

    Null hypothesis #4
    Computationally successful configurable switches cannot be set by chance, necessity, or any combination of the two, even over large periods of time.

    We repeat that a single incident of nontrivial algorithmic programming success achieved without selection for fitness at the decision-node programming level would falsify any of these null hypotheses. This renders each of these hypotheses scientifically testable. We offer the prediction that none of these four hypotheses will be falsified.

    The Law of Physicodynamic Insufficiency – Dr David L. Abel – November 2010
    Excerpt: “If decision-node programming selections are made randomly or by law rather than with purposeful intent, no non-trivial (sophisticated) function will spontaneously arise.”,,, After ten years of continual republication of the null hypothesis with appeals for falsification, no falsification has been provided. The time has come to extend this null hypothesis into a formal scientific prediction: “No non trivial algorithmic/computational utility will ever arise from chance and/or necessity alone.”

    The Law of Physicodynamic Incompleteness – David L. Abel – August 2011
    Summary: “The Law of Physicodynamic Incompleteness” states that inanimate physicodynamics is completely inadequate to generate, or even explain, the mathematical nature of physical interactions (the purely formal laws of physics and chemistry). The Law further states that physicodynamic factors cannot cause formal processes and procedures leading to sophisticated function. Chance and necessity alone cannot steer, program or optimize algorithmic/computational success to provide desired non-trivial utility.

    A degree-distribution based hierarchical agglomerative clustering algorithm for protein complexes identification. – David K. Y. Chiu – 2011
    Excerpt: A comprehensive comparison is performed between our method and other four representative methods. The results show that our algorithm finds more protein complexes with high biological significance and a significant improvement. Furthermore, the predicted complexes by our method, whether dense or sparse, match well with known biological characteristics.

    Further note:

    Does God Exist – Kirk Durston – video

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