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Evolutionary Computation in Gene Regulatory Network Research

Author Nasimul Noman , Hitoshi Iba
Release Date: 2016/02/01
ISBN: 9781118911518
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Book Description

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.

Table of Contents

  1. PREFACE
  2. ACKNOWLEDGMENTS
  3. CONTRIBUTORS
  4. I PRELIMINARIES
    1. 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
    2. 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
    3. 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
  5. II EAs FOR GENE EXPRESSION DATA ANALYSIS AND GRN RECONSTRUCTION
    1. 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
    2. 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 REXSTAR/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
    3. 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
    4. 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
    5. 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
  6. III EAs FOR EVOLVING GRNs AND REACTION NETWORKS
    1. 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
    2. 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
    3. 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
  7. IV APPLICATION OF GRN WITH EAs
    1. 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
    2. 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
    3. 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
    4. 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
  8. INDEX
  9. SERIES
  10. EULA