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Cellular automata approaches to biological modeling

G B Ermentrout1, L Edelstein-Keshet

  • 1Department of Mathematics and Statistics, University of Pittsburgh, PA 15260.

Journal of Theoretical Biology
|January 7, 1993
PubMed
Summary

This review explores biologically motivated cellular automata (CA) for modeling complex biological systems. We propose methods for faster, more realistic simulations of pattern formation in various biological contexts.

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Area of Science:

  • Computational Biology
  • Mathematical Biology
  • Systems Biology

Background:

  • Cellular automata (CA) are increasingly used to model complex biological phenomena.
  • Existing CA models often face limitations in speed and realism.
  • Applications span excitable media, developmental biology, neurobiology, and population dynamics.

Purpose of the Study:

  • To review biologically motivated cellular automata (CA) across diverse biological fields.
  • To propose technical and theoretical enhancements for CA models.
  • To demonstrate improved CA applications in pattern formation.

Main Methods:

  • Review of existing literature on biologically motivated CA.
  • Development of theoretical arguments for enhanced CA speed and realism.
  • Application of proposed enhancements to classical pattern formation examples.

Main Results:

  • Identification of key CA models in excitable media, developmental, neuro-, and population biology.
  • Demonstration of improved simulation speed and realism through proposed methods.
  • Successful application to models of fibroblast aggregation, branching networks, trail following, and neuronal maps.

Conclusions:

  • Biologically motivated CA offer a powerful framework for simulating complex biological systems.
  • Proposed enhancements significantly improve the efficiency and accuracy of CA simulations.
  • CA provide versatile tools for understanding pattern formation and emergent behaviors in biology.

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