Evolutionary Computation in Gene Regulatory Network Research

ISBN-13: 9781118911518 / Angielski / Twarda / 2016 / 464 str.

Hitoshi Iba; Nasimul Noman

Opis

Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. - Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) - Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications - Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology - Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines.

Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.

Spis treści

PREFACE ix

ACKNOWLEDGMENTS xiii

CONTRIBUTORS xv

I PRELIMINARIES

1 A Brief Introduction to Evolutionary and other Nature–Inspired Algorithms 3
Nasimul Noman and Hitoshi Iba

2 Mathematical Models and Computational Methods for Inference of Genetic Networks 30
Tatsuya Akutsu

3 Gene Regulatory Networks: Real Data Sources and Their Analysis 49
Yuji Zhang

II EAs FOR GENE EXPRESSION DATA ANALYSIS AND GRN RECONSTRUCTION

4 Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms 69
Alan Wee–Chung Liew

5 Inference of Vohradsk´ y s Models of Genetic Networks Using a Real–Coded Genetic Algorithm 96
Shuhei Kimura

6 GPU–Powered Evolutionary Design of Mass–Action–Based Models of Gene Regulation 118
Marco S. Nobile, Davide Cipolla, Paolo Cazzaniga and Daniela Besozzi

7 Modeling Dynamic Gene Expression in Streptomyces Coelicolor: Comparing Single and Multi–Objective Setups 151
Spencer Angus Thomas, Yaochu Jin, Emma Laing and Colin Smith

8 Reconstruction of Large–Scale Gene Regulatory Network Using S–system Model 185
Ahsan Raja Chowdhury and Madhu Chetty

III EAs FOR EVOLVING GRNs AND REACTION NETWORKS

9 Design Automation of Nucleic Acid Reaction System Simulated by Chemical Kinetics Based on Graph Rewriting Model 213
Ibuki Kawamata and Masami Hagiya

10 Using Evolutionary Algorithms to Study the Evolution of Gene Regulatory Networks Controlling Biological Development 240
Alexander Spirov and David Holloway

11 Evolving GRN–inspired In Vitro Oscillatory Systems 269
Quang Huy Dinh, Nathanael Aubert, Nasimul Noman, Hitoshi Iba and Yannic Rondelez

IV APPLICATION OF GRN WITH EAs

12 Artificial Gene Regulatory Networks for Agent Control 301
Sylvain Cussat–Blanc, Jean Disset, St´ephane Sanchez and Yves Duthen

13 Evolving H–GRNs for Morphogenetic Adaptive Pattern Formation of Swarm Robots 327
Hyondong Oh and Yaochu Jin

14 Regulatory Representations in Architectural Design 362
Daniel Richards and Martyn Amos

15 Computing with Artificial Gene Regulatory Networks 398
Michael A. Lones

INDEX

O autorze

Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the Journal of Genetic Programming and Evolvable Machines.
 
Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW,  Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. He is an Editor of the BioMed Research International Journal. His research interests include computational biology, synthetic biology, and bioinformatics.

Blurb

This book serves as a handbook for gene regulatory network research using evolutionary algorithms, with applications for computer scientists, biologists, and bioinformatics researchers

This book compiles progress on gene regulatory network (GRN) research, focusing particularly on different domains that apply evolutionary algorithms (EAs) as the computational methodology. These areas are the analysis of gene expression data to discover knowledge; the reconstruction of GRN from expression profiles; and the evolution of GRN for target behavior. The book also presents uses of GRN with EAs in applications such as architectural design, agent control and robotics. The first part of the book introduces GRN to readers with a computer science background, and EAs to readers with a life science background. The authors present the EA approaches for analysis of gene expression data.  Next, readers are guided step–by–step through the reverse engineering and evolution of GRN using EAs. Topics covered include deterministic and stochastic modelling of GRN, time series data analysis,  single and multi–objective genetic algorithms, and swarm intelligence. The last part of the book focuses on future applications of GRN with use of EAs, in the fields of agent control, robotics, and design. The fifteen chapters are authored by well–known researchers and experienced practitioners in their respective fields.

 Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary algorithms (EAs)

 Covers all sub–domains of GRN research using EAs, such as expression profile analysis, reverse engineering, GRN evolution, applications

 Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology

 Delivers state–of–the–art research in genetic algorithms, genetic programming, and swarm intelligence

Evolutionary Computation in Gene Regulatory Network Research is a great resource for students, researchers, and professionals in computer science, systems biology, and bioinformatics.

Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the Journal of Genetic Programming and Evolvable Machines.
 
Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW,  Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. He is an Editor of the BioMed Research International Journal. His research interests include computational biology, synthetic biology, and bioinformatics.