Since the identification of regulatory sequences associated with genes in the 1960s, the research in the field of gene regulatory network (GRN) is ever increasing—not only for understanding the dynamics of these complex systems but also for uncovering how they control the development, behavior, and fate of biological organisms. Dramatic progress is being made in understanding gene networks of organisms, thanks to the recent revival of evolutionary developmental biology (evo-devo). For example, there have been many startling discoveries regarding the Hox genes (master control genes that define segment structures in most metazoa). At the same time, neuroscientists and evolutionary biologists think that the modularity of gene networks (combination of functionally related structures and separation of unrelated structures) is crucial to the development of complex structures.

Gene control network, which is a representative concept in the evo-devo approach, is considered to be the central process that achieves the functionality of a molecular machine (flow of DNA-RNA-protein-metabolite) and models interactions between genes. Therefore, analysis of gene networks may provide insights into the fundamental mechanisms of life phenomena. These include robustness and possibility of evolution—two mechanisms have been observed at various levels of organisms, from gene control to fitness value of an organism. Stuart Kauffman used the random Boolean graph model to experimentally prove that gene networks in a certain critical condition can be simultaneously robust and capable of evolution under genetic changes. Besides, today it is also believed, based on experimental evidence, that the understanding and control of tumor like complex disease is deep-rooted in completing the GRN wiring diagrams.

As we enter the era of synthetic biology, the research interest and emphasis in GRN research have received a new thrust. After establishing the promise and prospect of this field through the construction of synthetic circuits like oscillators and counters, synthetic biologists now aspire to design complex artificial gene networks that are capable of sensing and adjusting metabolite activities in cells and use those circuits for therapeutic purpose. However, with the growth in size and complexity of the circuit, the experimental construction becomes infeasible and assistance from effective and efficient computational approaches becomes essential.

Because of their enormous capability of generating complex behavior, GRNs are now used for modeling different computational and engineering problems beyond biological realm. Very recently, some fascinating applications of GRN have been used in different fields that ranges from agent control to design. These applications harness the power of knowledge encoding in GRN and the ability of creating complex systems through computer simulations.

All of the research activities related to GRN, whether those are focused on understanding the mechanism of evolution, on uncovering the development of a fatal disease, or on forming an adaptive pattern in swarm robots for monitoring purpose, involve computational approaches. Consequently, the latest development in artificial intelligence and machine learning has been widely applied in the research related to GRN over the last decades. Perhaps evolutionary algorithms and other nature-inspired algorithms (commonly called evolutionary computation (EC)) are the most broadly practiced computational approach, next to machine learning, in this research domain. EC is a branch of optimization that is useful when we do not have enough information regarding the system for which the optimum solution is sought. They are also useful when the problem is non-convex, non-linear, and non-smooth, which makes most techniques incapable of finding the global minimum. Furthermore, EC is also handy when the function to optimize is noisy and irregular, which also dampens the performance of most classic optimization schemes. Since all these characteristics apply in case of GRN analysis and inference, EC has become a very useful methodology and a robust and reliable tool in this research paradigm. Consequently, EC has been used extensively for analysis, reverse-engineering, and automatic construction of GRN both for systems and synthetic biology, thus creating an independent research domain of its own.

The purpose of this book is to create a guidebook for this research field that will be useful for the audience of both background—computer science and biology. This title presents a handbook for research on GRN using EC that contains a compilation of introductory materials for the novice researcher, highlights of the recent progress in this research field for the current practitioner, and guidelines to new prospects and future trends of the field for the advanced researcher. Keeping in mind the diverse backgrounds of the researchers in this interdisciplinary field, this book delivers materials in a way equally attractive for a reader with training in computation or biology.

This book delivers a step-by-step guideline for research in gene regulatory networks using evolutionary computation. Keeping in mind the various applications of EC in GRN research and for addressing the needs of readers from diverse research backgrounds, the book is organized into four parts. Each of these sections, authored by well-known researchers and experienced practitioners, delivers the relevant materials for the interested readers.

The first part gives an introductory background to the field. Taking into account that prospective readers come with either of the two major backgrounds, this introductory material is divided into three chapters providing necessary training on EC for biologists, introducing the relevant concepts and notions of gene regulatory networks for computer scientists, and familiarizing the data sources and analysis methods for GRN research, respectively. Nevertheless, the material presented in this section can be used as a reference by the regular practitioners of the field.

The second part of the book presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The first chapter in this section presents EC as an effective method for information extraction from gene expression data techniques using bi-cluster analysis. Inference of GRN from gene expression data is the sub-field that has seen the most number of applications of EC. Researchers have used different types of models, data, and different classes of EC for reverse engineering GRNs. The other four chapters in this part cover the most recent and advanced usages of EC for reconstruction of GRN from expression profiles using different models and algorithms.

The second largest application of EC in GRN research is the automatic construction of gene regulatory and reaction networks. This field has become particularly attractive for the synthetic biologists to relieve them from the painstaking trial-and-error methods of gene circuit construction. The third part of the book comprises three chapters that covers the contemporary advancements in this topic and gives direction and guideline for future research.

Finally, the last part of this book focuses on applications of GRNs with EC in other fields. We have seen some compelling applications of GRN with EC for constructing complex system or behavior in diverse fields such as art, design, and engineering. These applications have shown promising signs for a new research philosophy and methodology worth further investigation and exploration. Carefully chosen such advanced and cutting edge research topics that have attracted much attention have been organized in four chapters in the last part of the book.

It has been more than 15 years since GRN research started using EC as a useful and effective computational approach. Researchers have used various classes of EC that showed promising results under different topics of the broader research field. Today, EC is an established and effective research methodology in GRN research. In order to sustain and promote research in this active field, some handbook that covers the prospects and challenges of the field is necessary. It is the editors' expectation that this edited title that brings together the background, current status, and future developments of this field will serve this purpose.

HITOSHI IBA AND NASIMUL NOMAN

March 31, 2015