Del i serien Wiley Series in Bioinformatics

Evolutionary Computation in Gene Regulatory Network Research

Inbunden, Engelska, 2016

2 169 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologistsThis 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 intelligenceEvolutionary 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.

Produktinformation

Utgivningsdatum2016-03-29
Mått163 x 236 x 31 mm
Vikt771 g
FormatInbunden
SpråkEngelska
SerieWiley Series in Bioinformatics
Antal sidor464
FörlagJohn Wiley & Sons Inc
ISBN9781118911518

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Hälsoinformatik inom Medicin

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, Tokyo, 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.

Preface ixAcknowledgments xiiiContributors xvI Preliminaries1 A Brief Introduction to Evolutionary and other Nature-Inspired Algorithms 3Nasimul Noman and Hitoshi Iba2 Mathematical Models and Computational Methods for Inference of Genetic Networks 30Tatsuya Akutsu3 Gene Regulatory Networks: Real Data Sources and Their Analysis 49Yuji ZhangII EAs for Gene Expression Data Analysis and GRN Reconstruction4 Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms 69Alan Wee-Chung Liew5 Inference of Vohradský’s Models of Genetic Networks Using a Real-Coded Genetic Algorithm 96Shuhei Kimura6 GPU-Powered Evolutionary Design of Mass-Action-Based Models of Gene Regulation 118Marco S. Nobile, Davide Cipolla, Paolo Cazzaniga and Daniela Besozzi7 Modeling Dynamic Gene Expression in Streptomyces Coelicolor: Comparing Single and Multi-Objective Setups 151Spencer Angus Thomas, Yaochu Jin, Emma Laing and Colin Smith8 Reconstruction of Large-Scale Gene Regulatory Network Using S-system Model 185Ahsan Raja Chowdhury and Madhu ChettyIII EAs for Evolving GRNs and Reaction Networks9 Design Automation of Nucleic Acid Reaction System Simulated by Chemical Kinetics Based on Graph Rewriting Model 213Ibuki Kawamata and Masami Hagiya10 Using Evolutionary Algorithms to Study the Evolution of Gene Regulatory Networks Controlling Biological Development 240Alexander Spirov and David Holloway11 Evolving GRN-inspired In Vitro Oscillatory Systems 269Quang Huy Dinh, Nathanael Aubert, Nasimul Noman, Hitoshi Iba and Yannic RondelezIV Application of GRN with EAs12 Artificial Gene Regulatory Networks for Agent Control 301Sylvain Cussat-Blanc, Jean Disset, Stéphane Sanchez and Yves Duthen13 Evolving H-GRNs for Morphogenetic Adaptive Pattern Formation of Swarm Robots 327Hyondong Oh and Yaochu Jin14 Regulatory Representations in Architectural Design 362Daniel Richards and Martyn Amos15 Computing with Artificial Gene Regulatory Networks 398Michael A. LonesIndex 425