Home Page Icon
Home Page
Table of Contents for
Evolutionary Computation in Gene Regulatory Network Research
Close
Evolutionary Computation in Gene Regulatory Network Research
by Nasimul Noman, Hitoshi Iba
Evolutionary Computation in Gene Regulatory Network Research
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 REXSTAR/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
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Next
Next Chapter
Evolutionary Computation in Gene Regulatory Network Research
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset