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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley Series in Bioinformatics
- Sprache: Englisch
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.
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Seitenzahl: 837
Veröffentlichungsjahr: 2016
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Wiley Series on
Bioinformatics: Computational Techniques and Engineering
A complete list of the titles in this series appears at the end of this volume.
EVOLUTIONARY COMPUTATION IN GENE REGULATORY NETWORK RESEARCH
Edited by
Hitoshi Iba
The University of Tokyo Bunkyo, Tokyo, Japan
Nasimul Noman
The University of Newcastle New South Wales, Australia
Copyright © 2016 by John Wiley & Sons, Inc. All rights reserved
Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
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Library of Congress Cataloging-in-Publication Data applied for.
ISBN: 9781118911518
CONTENTS
PREFACE
ACKNOWLEDGMENTS
CONTRIBUTORS
I PRELIMINARIES
CHAPTER 1 A BRIEF INTRODUCTION TO EVOLUTIONARY AND OTHER NATURE-INSPIRED ALGORITHMS
1.1 INTRODUCTION
1.2 CLASSES OF EVOLUTIONARY COMPUTATION
1.3 ADVANTAGES/DISADVANTAGES OF EVOLUTIONARY COMPUTATION
1.4 APPLICATION AREAS OF EC
1.5 CONCLUSION
REFERENCES
CHAPTER 2 MATHEMATICAL MODELS AND COMPUTATIONAL METHODS FOR INFERENCE OF GENETIC NETWORKS
2.1 INTRODUCTION
2.2 BOOLEAN NETWORKS
2.3 PROBABILISTIC BOOLEAN NETWORK
2.4 BAYESIAN NETWORK
2.5 GRAPHICAL GAUSSIAN MODELING
2.6 DIFFERENTIAL EQUATIONS
2.7 TIME-VARYING NETWORK
2.8 CONCLUSION
NOTES
REFERENCES
CHAPTER 3 GENE REGULATORY NETWORKS: REAL DATA SOURCES AND THEIR ANALYSIS
3.1 INTRODUCTION
3.2 BIOLOGICAL DATA SOURCES
3.3 TOPOLOGICAL ANALYSIS OF GENE REGULATORY NETWORKS
3.4 GRN INFERENCE BY INTEGRATION OF MULTI-SOURCE BIOLOGICAL DATA
3.5 CONCLUSIONS AND FUTURE DIRECTIONS
ACKNOWLEDGMENT
REFERENCES
II EAs FOR GENE EXPRESSION DATA ANALYSIS AND GRN RECONSTRUCTION
CHAPTER 4 BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING EVOLUTIONARY ALGORITHMS
4.1 INTRODUCTION
4.2 BICLUSTER ANALYSIS OF DATA
4.3 BICLUSTERING TECHNIQUES
4.4 EVOLUTIONARY ALGORITHMS BASED BICLUSTERING
4.5 CONCLUSION
REFERENCES
CHAPTER 5 INFERENCE OF VOHRADSKÝ’S MODELS OF GENETIC NETWORKS USING A REAL-CODED GENETIC ALGORITHM
5.1 INTRODUCTION
5.2 MODEL
5.3 INFERENCE BASED ON BACK-PROPAGATION THROUGH TIME
5.4 INFERENCE BY SOLVING SIMULTANEOUS EQUATIONS
5.5 REX
STAR
/JGG
5.6 INFERENCE OF AN ARTIFICIAL NETWORK
5.7 INFERENCE OF AN ACTUAL GENETIC NETWORK
5.8 CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES
CHAPTER 6 GPU-POWERED EVOLUTIONARY DESIGN OF MASS-ACTION-BASED MODELS OF GENE REGULATION
6.1 INTRODUCTION
6.2 EVOLUTIONARY COMPUTATION FOR THE INFERENCE OF BIOCHEMICAL MODELS
6.3 METHODS
6.4 DESIGN METHODOLOGY OF GENE REGULATION MODELS BY MEANS OF CGP AND PSO
6.5 RESULTS
6.6 DISCUSSION
6.7 CONCLUSIONS AND FUTURE PERSPECTIVES
NOTES
REFERENCES
CHAPTER 7 MODELING DYNAMIC GENE EXPRESSION IN
STREPTOMYCES COELICOLOR
: COMPARING SINGLE AND MULTI-OBJECTIVE SETUPS
7.1 INTRODUCTION
7.2 REGULATORY NETWORKS AND GENE EXPRESSION DATA
7.3 OPTIMIZATION USING EVOLUTIONARY ALGORITHMS
7.4 MODELING GENE EXPRESSION
7.5 RESULTS
7.6 DISCUSSION
7.7 CONCLUSIONS
REFERENCES
CHAPTER 8 RECONSTRUCTION OF LARGE-SCALE GENE REGULATORY NETWORK USING S-SYSTEM MODEL
8.1 INTRODUCTION
8.2 REVERSE ENGINEERING GRN WITH S-SYSTEM MODEL AND EVOLUTIONARY COMPUTATION
8.3 THE PROPOSED FRAMEWORK FOR INFERRING LARGE-SCALE GRN
8.4 EXPERIMENTAL RESULTS
8.5 DISCUSSIONS
8.6 CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
III EAs FOR EVOLVING GRNs AND REACTION NETWORKS
CHAPTER 9 DESIGN AUTOMATION OF NUCLEIC ACID REACTION SYSTEM SIMULATED BY CHEMICAL KINETICS BASED ON GRAPH REWRITING MODEL
9.1 INTRODUCTION
9.2 NUCLEIC ACID REACTION SYSTEM
9.3 SIMULATION BY CHEMICAL KINETICS
9.4 AUTOMATIC DESIGN OF NUCLEIC ACID REACTION SYSTEM
9.5 DISCUSSION AND CONCLUSION
REFERENCES
CHAPTER 10 USING EVOLUTIONARY ALGORITHMS TO STUDY THE EVOLUTION OF GENE REGULATORY NETWORKS CONTROLLING BIOLOGICAL DEVELOPMENT
10.1 INTRODUCTION
10.2 COMPUTATIONAL APPROACHES FOR THE EVOLUTION OF DEVELOPMENTAL GRNS
10.3 USING EVOLUTIONARY COMPUTATIONS TO INVESTIGATE BIOLOGICAL EVOLUTION
10.4 CONCLUSIONS
ACKNOWLEDGEMENTS
REFERENCES
CHAPTER 11 EVOLVING GRN-INSPIRED
IN VITRO
OSCILLATORY SYSTEMS
11.1 INTRODUCTION
11.2 PEN DNA TOOLBOX
11.3 RELATED WORK
11.4 FRAMEWORK FOR EVOLVING REACTION NETWORKS (ERNE)
11.5 ERNE FOR THE DISCOVERY OF OSCILLATORY SYSTEMS
11.6 DISCUSSION
11.7 CONCLUSION
REFERENCES
IV APPLICATION OF GRN WITH EAs
CHAPTER 12 ARTIFICIAL GENE REGULATORY NETWORKS FOR AGENT CONTROL
12.1 INTRODUCTION
12.2 COMPUTATION MODEL
12.3 VISUALIZING THE GRN ABILITIES
12.4 GROWING MULTICELLULAR ORGANISMS
12.5 DRIVING A VIRTUAL CAR
12.6 REGULATING BEHAVIORS
12.7 CONCLUSION
NOTES
REFERENCES
CHAPTER 13 EVOLVING H-GRNS FOR MORPHOGENETIC ADAPTIVE PATTERN FORMATION OF SWARM ROBOTS
13.1 INTRODUCTION
13.2 PROBLEM STATEMENT
13.3 H-GRN MODEL WITH REGION-BASED SHAPE CONTROL
13.4 EVOLVING H-GRN USING NETWORK MOTIFS
13.5 CONCLUSIONS AND FUTURE WORK
ACKNOWLEDGMENT
APPENDIX
REFERENCES
CHAPTER 14 REGULATORY REPRESENTATIONS IN ARCHITECTURAL DESIGN
14.1 INTRODUCTION
14.2 BACKGROUND
14.3 THE NEED FOR REGULATORY REPRESENTATIONS
14.4 DEVELOPMENTAL MAPPING
14.5 ROBUSTNESS AND EVOLUTIONARY ADAPTATION IN BIOLOGICAL SYSTEMS
14.6 CONCLUSIONS AND DISCUSSION
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 15 COMPUTING WITH ARTIFICIAL GENE REGULATORY NETWORKS
15.1 INTRODUCTION
15.2 BIOLOGICAL GRNs
15.3 COMPUTATIONAL MODELS
15.4 MODELING DECISIONS
15.5 COMPUTATIONAL PROPERTIES OF AGRNs
15.6 AGRN MODELS AND APPLICATIONS
15.7 FUTURE RESEARCH DIRECTIONS
15.8 CONCLUSIONS
REFERENCES
INDEX
SERIES
EULA
List of Tables
Chapter 4
Table 4.1
Table 4.2
Table 4.3
Table 4.4
Table 4.5
Table 4.6
Table 4.7
Chapter 5
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Chapter 7
Table 7.1
Chapter 9
Table 9.1
Table 9.2
Chapter 11
Table 11.1
Chapter 15
Table 15.1
Table 15.2
Guide
Cover
Table of Contents
Preface
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PREFACE
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.
