Evolutionary Computation in Gene Regulatory Network Research

CHAPTER 7MODELING DYNAMIC GENE EXPRESSION IN STREPTOMYCES COELICOLOR: COMPARING SINGLE AND MULTI-OBJECTIVE SETUPS

Spencer Angus Thomas and Yaochu Jin

Department of Computing, University of Surrey, Guildford, UK

Emma Laing and Colin Smith

Department of Microbial and Cellular Sciences, University of Surrey, Guildford, UK

7.1 INTRODUCTION

Even the simplest of organisms can have an extremely complex network [1] spanning many levels of interactions from cellular to gene, to protein, and beyond. It is possible to model biological networks using gene regulatory networks (GRNs), which are groups of genes that interact through the production of their proteins. How these GRNs are constructed for large biological networks and how transcription factors are able to regulate the expression of thousands of genes in response to environmental changes is a fundamental problem in biology [2]. Furthermore, the reverse engineering of biological networks from expression data and the inference of the complexity in the networks is a current problem in computational and biological sciences [3]. The challenges of network reconstruction increase with the size of the network and suffer “underdeterminism,” where there are insufficient data to build statistical models for the number of genes in the network [4]. Hase et al. [5] observed that in order to infer large-scale networks, it is vital to know a priori information of high performance algorithms so methods can be selected based on the details of the ...