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Developmental selection and self-organization

S A Frank1

  • 1Department of Ecology and Evolutionary Biology, University of California, Irvine 92697-2525, USA. safrank@uci.edu

Bio Systems
|January 1, 1997
PubMed
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Developmental selection, the differential survival of cellular units, offers insights into self-organization. This study reveals formal analogies between developmental and genetic selection, providing a framework for analyzing complex evolving systems.

Area of Science:

  • Evolutionary Biology
  • Developmental Biology
  • Systems Biology

Background:

  • Developmental selection, the differential survival and proliferation of developmental units like cellular lineages, is proposed to explain self-organization in diverse biological systems.
  • Understanding developmental selection is hindered by its unclear relationship to genetic selection and evolution.
  • Existing models often lack a unified framework for analyzing selective systems within development.

Purpose of the Study:

  • To establish formal analogies between models of developmental selection and genetic selection.
  • To develop a general method for analyzing self-organizing selective systems.
  • To apply this analytical framework to a model of self-organization in ant colonies.

Main Methods:

Related Experiment Videos

  • Formal comparison of mathematical models for developmental and genetic selection.
  • Partitioning self-organizing selective systems into generative rules (variation) and selective filters (target-directed change).
  • Utilizing aggregate statistical measures, such as trait-fitness covariance, to analyze evolving systems.

    • Main Results:

    • Demonstrated formal analogies between developmental and genetic selection models.
    • Outlined a general analytical method for partitioning and measuring selective systems.
    • Successfully applied the framework to an ant colony self-organization model.

    Conclusions:

    • Developmental selection shares formal similarities with genetic selection, enabling a unified analytical approach.
    • The proposed method provides a robust framework for dissecting and understanding self-organizing systems.
    • This approach facilitates the study of evolution and self-organization across different biological scales.