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Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks

Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks

T. Steiner, Y. Jin, L. Schramm, B. Sendhoff
Copyright: © 2010 |Pages: 25
ISBN13: 9781605666853|ISBN10: 1605666858|EISBN13: 9781605666860
DOI: 10.4018/978-1-60566-685-3.ch021
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MLA

Steiner, T., et al. "Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks." Handbook of Research on Computational Methodologies in Gene Regulatory Networks, edited by Sanjoy Das, et al., IGI Global, 2010, pp. 498-522. https://doi.org/10.4018/978-1-60566-685-3.ch021

APA

Steiner, T., Jin, Y., Schramm, L., & Sendhoff, B. (2010). Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks. In S. Das, D. Caragea, S. Welch, & W. Hsu (Eds.), Handbook of Research on Computational Methodologies in Gene Regulatory Networks (pp. 498-522). IGI Global. https://doi.org/10.4018/978-1-60566-685-3.ch021

Chicago

Steiner, T., et al. "Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks." In Handbook of Research on Computational Methodologies in Gene Regulatory Networks, edited by Sanjoy Das, et al., 498-522. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-685-3.ch021

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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.

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