{"product_id":"evolutionary-computation-in-gene-regulatory-network-research-isbn-9781118911518","title":"Evolutionary Computation in Gene Regulatory Network Research","description":"\u003cp\u003e\u003cb\u003eIntroducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e• Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC)\u003c\/p\u003e \u003cp\u003e• Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications\u003c\/p\u003e \u003cp\u003e• Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology\u003c\/p\u003e \u003cp\u003e• Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEvolutionary Computation in Gene Regulatory Network Research\u003c\/i\u003e is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eHitoshi Iba\u003c\/b\u003e 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 \u003ci\u003eIEEE Transactions on Evolutionary Computation\u003c\/i\u003e and the journal of\u003ci\u003e Genetic Programming and Evolvable Machines\u003c\/i\u003e.\u003cbr\u003e \u003cbr\u003e\u003cb\u003eNasimul Noman\u003c\/b\u003e 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 \u003ci\u003eBioMed Research International \u003c\/i\u003ejournal. His research interests include computational biology, synthetic biology, and bioinformatics.\u003c\/p\u003e \u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003eContributors xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Preliminaries\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 A Brief Introduction to Evolutionary and other Nature-Inspired Algorithms 3\u003cbr\u003e \u003ci\u003eNasimul Noman and Hitoshi Iba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Mathematical Models and Computational Methods for Inference of Genetic Networks 30\u003cbr\u003e \u003ci\u003eTatsuya Akutsu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Gene Regulatory Networks: Real Data Sources and Their Analysis 49\u003cbr\u003e \u003ci\u003eYuji Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII EAs for Gene Expression Data Analysis and GRN Reconstruction\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4 Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms 69\u003cbr\u003e \u003ci\u003eAlan Wee-Chung Liew\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Inference of Vohradský’s Models of Genetic Networks Using a Real-Coded Genetic Algorithm 96\u003cbr\u003e \u003ci\u003eShuhei Kimura\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 GPU-Powered Evolutionary Design of Mass-Action-Based Models of Gene Regulation 118\u003cbr\u003e \u003ci\u003eMarco S. Nobile, Davide Cipolla, Paolo Cazzaniga and Daniela Besozzi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Modeling Dynamic Gene Expression in \u003ci\u003eStreptomyces Coelicolor\u003c\/i\u003e: Comparing Single and Multi-Objective Setups 151\u003cbr\u003e \u003ci\u003eSpencer Angus Thomas, Yaochu Jin, Emma Laing and Colin Smith\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Reconstruction of Large-Scale Gene Regulatory Network Using S-system Model 185\u003cbr\u003e \u003ci\u003eAhsan Raja Chowdhury and Madhu Chetty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII EAs for Evolving GRNs and Reaction Networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9 Design Automation of Nucleic Acid Reaction System Simulated by Chemical Kinetics Based on Graph Rewriting Model 213\u003cbr\u003e \u003ci\u003eIbuki Kawamata and Masami Hagiya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Using Evolutionary Algorithms to Study the Evolution of Gene Regulatory Networks Controlling Biological Development 240\u003cbr\u003e \u003ci\u003eAlexander Spirov and David Holloway\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Evolving GRN-inspired \u003ci\u003eIn Vitro \u003c\/i\u003eOscillatory Systems 269\u003cbr\u003e \u003ci\u003eQuang Huy Dinh, Nathanael Aubert, Nasimul Noman, Hitoshi Iba and Yannic Rondelez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Application of GRN with EAs\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12 Artificial Gene Regulatory Networks for Agent Control 301\u003cbr\u003e \u003ci\u003eSylvain Cussat-Blanc, Jean Disset, St\u003c\/i\u003e\u003ci\u003eéphane Sanchez and Yves Duthen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Evolving H-GRNs for Morphogenetic Adaptive Pattern Formation of Swarm Robots 327\u003cbr\u003e \u003ci\u003eHyondong Oh and Yaochu Jin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Regulatory Representations in Architectural Design 362\u003cbr\u003e \u003ci\u003eDaniel Richards and Martyn Amos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Computing with Artificial Gene Regulatory Networks 398\u003cbr\u003e \u003ci\u003eMichael A. Lones\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 425\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eHitoshi Iba\u003c\/b\u003e 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\u003ci\u003e IEEE Transactions on Evolutionary Computation\u003c\/i\u003e and the \u003ci\u003eJournal of Genetic Programming and Evolvable Machines.\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eNasimul Noman\u003c\/b\u003e 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 \u003ci\u003eBioMed Research International\u003c\/i\u003e journal. His research interests include computational biology, synthetic biology, and bioinformatics.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eIntroducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eThis book is a step-by-step guideline for research in gene regulatory networks (GRNs) 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 of this book presents the EC approaches for analysis and reconstruction of GRNs 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides a reference for current and future research in gene regulatory networks (GRNs) using evolutionary computation (EC)\u003c\/li\u003e \u003cli\u003eCovers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, and applications\u003c\/li\u003e \u003cli\u003eContains useful contents for courses in GRNs, systems biology, computational biology, and synthetic biology\u003c\/li\u003e \u003cli\u003eDelivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eEvolutionary Computation in Gene Regulatory Network Research\u003c\/i\u003e is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as for upper undergraduate, graduate, and postgraduate students.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989179154661,"sku":"NP9781118911518","price":156.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118911518.jpg?v=1761783109","url":"https:\/\/k12savings.com\/products\/evolutionary-computation-in-gene-regulatory-network-research-isbn-9781118911518","provider":"K12savings","version":"1.0","type":"link"}