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Modeling adaptive biological systems.

R J Bagley1, J D Farmer, S A Kauffman

  • 1Theoretical Division, Los Alamos National Laboratory, NM 87545.

Bio Systems
|January 1, 1989
PubMed
Summary
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This study introduces novel models for evolving systems with variable dimensions, simulating prebiotic chemical networks and immune system dynamics. These models enable true evolution experiments without explicit fitness functions.

Area of Science:

  • Dynamical Systems Theory
  • Computational Biology
  • Biochemistry

Background:

  • Natural systems evolve in composition and interaction.
  • Systems with changing component numbers are dynamical systems with variable-dimension phase spaces.
  • Modeling such systems requires methods that accommodate dynamic structural changes.

Purpose of the Study:

  • To present two models of biochemical systems with variable phase spaces.
  • To investigate adaptive network properties in evolving systems.
  • To explore emergent chemical networks and immune system self-regulation.

Main Methods:

  • Developed models of autocatalytic reaction networks and the idiotypic immune network.
  • Utilized meta-dynamical rules to construct equations of motion from component properties.

Related Experiment Videos

  • Employed two-level simulations: integrating equations of motion and applying algorithms to alter system dynamics.

    • Main Results:

    • Models demonstrate advantages for studying evolving systems, including emergent properties without explicit fitness functions.
    • Variable-dimension phase spaces allow exploration of otherwise inaccessible states.
    • Facilitates true evolution experiments by allowing for significant variation.

    Conclusions:

    • Meta-dynamical modeling offers a powerful approach for studying adaptive biological networks.
    • These methods can be applied to diverse real-world biological systems.
    • The models provide insights into the emergence of chemical complexity and immune system self-organization.