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Stochastic information processing biological systems

H M Hastings, R Pekelney

    Bio Systems
    |January 1, 1982
    PubMed
    Summary
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    We introduce a biochemical model using stochastic automata for information processing in biological systems. This model explains gradualism, learning, and evolutionary dynamics like punctuated equilibria.

    Area of Science:

    • Computational Biology
    • Theoretical Neuroscience
    • Evolutionary Genetics

    Background:

    • Classical automata are deterministic, limiting their application to inherently stochastic biological processes.
    • Previous probabilistic automata research focused on error correction, not intrinsic stochasticity.
    • Biological systems, including brains and genetics, rely on complex, stochastic information processing.

    Purpose of the Study:

    • To propose a biochemically-based model for stochastic information processing.
    • To extend the definition of classical automata to incorporate intrinsic stochasticity.
    • To provide a unified framework for understanding biological information processing from neural to evolutionary levels.

    Main Methods:

    • Development of intrinsically stochastic probabilistic automata, termed biochemical automata.

    Related Experiment Videos

  • Modeling reaction-diffusion processes using these novel automata.
  • Extending classical automata theory to include probabilistic and biochemical principles.

    • Main Results:

    • The model successfully describes phenomena like gradualism and learning in biological systems.
    • It offers a potential resolution for the low probability of simultaneous point mutations in genetics.
    • The genetic model derived from this framework suggests an evolutionary dynamic of punctuated equilibria.

    Conclusions:

    • Biochemical automata provide a powerful framework for modeling stochastic information processing in biology.
    • This approach unifies concepts across neuroscience, genetics, and evolutionary theory.
    • The model highlights the fundamental role of intrinsic stochasticity in biological complexity and evolution.