a specific variant of evolutionary algorithms originally focusing on the phenotypic level of evolution and adaptation, ES was developed by Rechenberg, Schwefel and co-workers [1].
Published in Chapter:
Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks
T. Steiner (Honda Research Institute Europe GmbH, Germany), Y. Jin (Honda Research Institute Europe GmbH, Germany), L. Schramm (Technische Universitaet Darmstadt, Germany), and B. Sendhoff (Honda Research Institute Europe GmbH, Germany)
Copyright: © 2010
|Pages: 25
DOI: 10.4018/978-1-60566-685-3.ch021
Abstract
In this chapter, we describe the use of evolutionary methods for the in silico generation of artificial gene regulatory networks (GRNs). These usually serve as models for biological networks and can be used for enhancing analysis methods in biology. We clarify our motivation in adopting this strategy by showing the importance of detailed knowledge of all processes, especially the regulatory dynamics of interactions undertaken during gene expression. To illustrate how such a methodology works, two different approaches to the evolution of small-scale GRNs with specified functions, are briefly reviewed and discussed. Thereafter, we present an approach to evolve medium sized GRNs with the ability to produce stable multi-cellular growth. The computational method employed allows for a detailed analysis of the dynamics of the GRNs as well as their evolution. We have observed the emergence of negative feedback during the evolutionary process, and we suggest its implication to the mutational robustness of the regulatory network which is further supported by evidence observed in additional experiments.