Michael A. Lones
School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, Scotland, UK
Gene regulatory networks (GRNs) are the fundamental mechanisms through which biological organisms control their growth, their dynamical behavior, their interaction with their environment, and which underlie much of the complexity we see in the biosphere. Biological complexity has long been an inspiration to computer scientists, and many seek to model the biological processes that generate this complexity, using these models to generate complex behavior that can then be used to solve problems in computer science and engineering. In this chapter, the term “artificial gene regulatory network” (AGRN) is used to refer to computational models of GRNs that are used in this manner.
Unlike other computational models of GRNs, AGRNs are predominantly used to solve computational and engineering problems, not biological problems. Hence, AGRNs often differ from these other models. This chapter reviews current understanding of AGRNs, discussing what is known about their computational properties, detailing how they have previously been applied to computational problems, and speculating about how they may be used in the future. Reflecting the theme explored in this book, we focus on approaches that have used evolutionary algorithms (EAs) to design AGRNs, a task for which they are presumably well suited given ...