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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.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada

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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. 1.1 INTRODUCTION
2. 1.2 CLASSES OF EVOLUTIONARY COMPUTATION
3. 1.3 ADVANTAGES/DISADVANTAGES OF EVOLUTIONARY COMPUTATION
4. 1.4 APPLICATION AREAS OF EC
5. 1.5 CONCLUSION
6. REFERENCES
CHAPTER 2 MATHEMATICAL MODELS AND COMPUTATIONAL METHODS FOR INFERENCE OF GENETIC NETWORKS
1. 2.1 INTRODUCTION
2. 2.2 BOOLEAN NETWORKS
3. 2.3 PROBABILISTIC BOOLEAN NETWORK
4. 2.4 BAYESIAN NETWORK
5. 2.5 GRAPHICAL GAUSSIAN MODELING
6. 2.6 DIFFERENTIAL EQUATIONS
7. 2.7 TIME-VARYING NETWORK
8. 2.8 CONCLUSION
9. NOTES
10. REFERENCES
CHAPTER 3 GENE REGULATORY NETWORKS: REAL DATA SOURCES AND THEIR ANALYSIS
1. 3.1 INTRODUCTION
2. 3.2 BIOLOGICAL DATA SOURCES
3. 3.3 TOPOLOGICAL ANALYSIS OF GENE REGULATORY NETWORKS
4. 3.4 GRN INFERENCE BY INTEGRATION OF MULTI-SOURCE BIOLOGICAL DATA
5. 3.5 CONCLUSIONS AND FUTURE DIRECTIONS
6. ACKNOWLEDGMENT
7. REFERENCES

II EAs FOR GENE EXPRESSION DATA ANALYSIS AND GRN RECONSTRUCTION

CHAPTER 4 BICLUSTERING ANALYSIS OF GENE EXPRESSION DATA USING EVOLUTIONARY ALGORITHMS
1. 4.1 INTRODUCTION
2. 4.2 BICLUSTER ANALYSIS OF DATA
3. 4.3 BICLUSTERING TECHNIQUES
4. 4.4 EVOLUTIONARY ALGORITHMS BASED BICLUSTERING
5. 4.5 CONCLUSION
6. REFERENCES
CHAPTER 5 INFERENCE OF VOHRADSKÝ’S MODELS OF GENETIC NETWORKS USING A REAL-CODED GENETIC ALGORITHM
1. 5.1 INTRODUCTION
2. 5.2 MODEL
3. 5.3 INFERENCE BASED ON BACK-PROPAGATION THROUGH TIME
4. 5.4 INFERENCE BY SOLVING SIMULTANEOUS EQUATIONS
5. 5.5 REX^STAR/JGG
6. 5.6 INFERENCE OF AN ARTIFICIAL NETWORK
7. 5.7 INFERENCE OF AN ACTUAL GENETIC NETWORK
8. 5.8 CONCLUSION
9. ACKNOWLEDGEMENTS
10. REFERENCES
CHAPTER 6 GPU-POWERED EVOLUTIONARY DESIGN OF MASS-ACTION-BASED MODELS OF GENE REGULATION
1. 6.1 INTRODUCTION
2. 6.2 EVOLUTIONARY COMPUTATION FOR THE INFERENCE OF BIOCHEMICAL MODELS
3. 6.3 METHODS
4. 6.4 DESIGN METHODOLOGY OF GENE REGULATION MODELS BY MEANS OF CGP AND PSO
5. 6.5 RESULTS
6. 6.6 DISCUSSION
7. 6.7 CONCLUSIONS AND FUTURE PERSPECTIVES
8. NOTES
9. REFERENCES
CHAPTER 7 MODELING DYNAMIC GENE EXPRESSION IN STREPTOMYCES COELICOLOR: COMPARING SINGLE AND MULTI-OBJECTIVE SETUPS
1. 7.1 INTRODUCTION
2. 7.2 REGULATORY NETWORKS AND GENE EXPRESSION DATA
3. 7.3 OPTIMIZATION USING EVOLUTIONARY ALGORITHMS
4. 7.4 MODELING GENE EXPRESSION
5. 7.5 RESULTS
6. 7.6 DISCUSSION
7. 7.7 CONCLUSIONS
8. REFERENCES
CHAPTER 8 RECONSTRUCTION OF LARGE-SCALE GENE REGULATORY NETWORK USING S-SYSTEM MODEL
1. 8.1 INTRODUCTION
2. 8.2 REVERSE ENGINEERING GRN WITH S-SYSTEM MODEL AND EVOLUTIONARY COMPUTATION
3. 8.3 THE PROPOSED FRAMEWORK FOR INFERRING LARGE-SCALE GRN
4. 8.4 EXPERIMENTAL RESULTS
5. 8.5 DISCUSSIONS
6. 8.6 CONCLUSION
7. ACKNOWLEDGMENTS
8. 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
1. 9.1 INTRODUCTION
2. 9.2 NUCLEIC ACID REACTION SYSTEM
3. 9.3 SIMULATION BY CHEMICAL KINETICS
4. 9.4 AUTOMATIC DESIGN OF NUCLEIC ACID REACTION SYSTEM
5. 9.5 DISCUSSION AND CONCLUSION
6. REFERENCES
CHAPTER 10 USING EVOLUTIONARY ALGORITHMS TO STUDY THE EVOLUTION OF GENE REGULATORY NETWORKS CONTROLLING BIOLOGICAL DEVELOPMENT
1. 10.1 INTRODUCTION
2. 10.2 COMPUTATIONAL APPROACHES FOR THE EVOLUTION OF DEVELOPMENTAL GRNS
3. 10.3 USING EVOLUTIONARY COMPUTATIONS TO INVESTIGATE BIOLOGICAL EVOLUTION
4. 10.4 CONCLUSIONS
5. ACKNOWLEDGEMENTS
6. REFERENCES
CHAPTER 11 EVOLVING GRN-INSPIRED IN VITRO OSCILLATORY SYSTEMS
1. 11.1 INTRODUCTION
2. 11.2 PEN DNA TOOLBOX
3. 11.3 RELATED WORK
4. 11.4 FRAMEWORK FOR EVOLVING REACTION NETWORKS (ERNE)
5. 11.5 ERNE FOR THE DISCOVERY OF OSCILLATORY SYSTEMS
6. 11.6 DISCUSSION
7. 11.7 CONCLUSION
8. REFERENCES

IV APPLICATION OF GRN WITH EAs

CHAPTER 12 ARTIFICIAL GENE REGULATORY NETWORKS FOR AGENT CONTROL
1. 12.1 INTRODUCTION
2. 12.2 COMPUTATION MODEL
3. 12.3 VISUALIZING THE GRN ABILITIES
4. 12.4 GROWING MULTICELLULAR ORGANISMS
5. 12.5 DRIVING A VIRTUAL CAR
6. 12.6 REGULATING BEHAVIORS
7. 12.7 CONCLUSION
8. NOTES
9. REFERENCES
CHAPTER 13 EVOLVING H-GRNS FOR MORPHOGENETIC ADAPTIVE PATTERN FORMATION OF SWARM ROBOTS
1. 13.1 INTRODUCTION
2. 13.2 PROBLEM STATEMENT
3. 13.3 H-GRN MODEL WITH REGION-BASED SHAPE CONTROL
4. 13.4 EVOLVING H-GRN USING NETWORK MOTIFS
5. 13.5 CONCLUSIONS AND FUTURE WORK
6. ACKNOWLEDGMENT
7. APPENDIX
8. REFERENCES
CHAPTER 14 REGULATORY REPRESENTATIONS IN ARCHITECTURAL DESIGN
1. 14.1 INTRODUCTION
2. 14.2 BACKGROUND
3. 14.3 THE NEED FOR REGULATORY REPRESENTATIONS
4. 14.4 DEVELOPMENTAL MAPPING
5. 14.5 ROBUSTNESS AND EVOLUTIONARY ADAPTATION IN BIOLOGICAL SYSTEMS
6. 14.6 CONCLUSIONS AND DISCUSSION
7. ACKNOWLEDGMENTS
8. REFERENCES
CHAPTER 15 COMPUTING WITH ARTIFICIAL GENE REGULATORY NETWORKS
1. 15.1 INTRODUCTION
2. 15.2 BIOLOGICAL GRNs
3. 15.3 COMPUTATIONAL MODELS
4. 15.4 MODELING DECISIONS
5. 15.5 COMPUTATIONAL PROPERTIES OF AGRNs
6. 15.6 AGRN MODELS AND APPLICATIONS
7. 15.7 FUTURE RESEARCH DIRECTIONS
8. 15.8 CONCLUSIONS
9. REFERENCES

INDEX

SERIES

EULA

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

Edited by Hitoshi Iba The University of Tokyo Bunkyo, Tokyo, Japan Nasimul Noman The University of Newcastle New South Wales, Australia

Edited by

Hitoshi Iba

The University of Tokyo
Bunkyo, Tokyo, Japan

Nasimul Noman

The University of Newcastle
New South Wales, Australia