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Related Experiment Videos

A graph grammar approach to artificial life.

Ole Kniemeyer1, Gerhard H Buck-Sorlin, Winfried Kurth

  • 1Brandenburgische Technische Universität Cottbus, Department of Computer Science, Chair for Practical Computer Science/Graphics Systems, P.O. Box 101344, D-03013 Cottbus, Germany. okn@informatik.tu-cottbus.de

Artificial Life
|October 14, 2004
PubMed
Summary

Relational Growth Grammars (RGGs) offer a novel formalism for Artificial Life (ALife) models, extending Lindenmayer systems. This graph rewriting approach unifies the representation of genetic, metabolic, and developmental processes within a common framework.

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Area of Science:

  • Artificial Life (ALife)
  • Computational Biology
  • Formal Systems

Background:

  • Lindenmayer systems are established for modeling biological growth.
  • Existing formalisms may lack unified representation for diverse biological entities and processes.
  • Need for a flexible framework to model genetic, metabolic, and developmental aspects.

Purpose of the Study:

  • Introduce Relational Growth Grammars (RGGs) as a novel formalism for ALife model specification.
  • Demonstrate RGGs' capability to represent complex biological systems and their development.
  • Provide a common framework for diverse ALife modeling components.

Main Methods:

  • Defined RGGs as graph rewriting systems with user-defined relations and object-associated nodes.
  • Extended parametric Lindenmayer systems with rule-based, procedural, and object-oriented features.

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  • Implemented an interactive program for RGG execution and result visualization.

    • Main Results:

    • RGGs successfully represent genes, metabolic regulatory networks, and organism morphology.
    • Developmental aspects, including mutation and gene regulation dynamics, are modeled.
    • Demonstrated RGGs on Dawkins' biomorphs and the ABC flower morphogenesis model.

    Conclusions:

    • RGGs provide a unified and powerful framework for specifying diverse ALife models.
    • The formalism simplifies the representation of complex biological interactions and development.
    • RGGs facilitate the study of evolutionary and developmental processes in silico.