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Evolutionary Computation in Gene Regulatory Network Research.

By: Contributor(s): Material type: TextTextSeries: Wiley Series in Bioinformatics SeriesPublisher: Newark : John Wiley & Sons, Incorporated, 2016Copyright date: ©2016Edition: 1st edDescription: 1 online resource (462 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119079774
Subject(s): Genre/Form: Additional physical formats: Print version:: Evolutionary Computation in Gene Regulatory Network ResearchLOC classification:
  • QH450 -- .E965 2016eb
Online resources:
Contents:
Intro -- Evolutionary Computation in Gene Regulatory Network Research -- Contents -- Preface -- Acknowledgments -- Contributors -- I Preliminaries -- 1 A Brief Introduction to Evolutionary and other Nature-Inspired Algorithms -- 1.1 Introduction -- 1.2 Classes of Evolutionary Computation -- 1.2.1 Genetic Algorithms -- 1.2.2 Genetic Programming -- 1.2.3 Evolution Strategy -- 1.2.4 Differential Evolution -- 1.2.5 Swarm Intelligence -- 1.2.6 Multi-Objective EA's -- 1.3 Advantages/Disadvantages of Evolutionary Computation -- 1.4 Application Areas Of EC -- 1.5 Conclusion -- References -- 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 -- References -- 3 Gene Regulatory Networks: Real Data Sources and Their Analysis -- 3.1 Introduction -- 3.2 Biological Data Sources -- 3.2.1 Gene Expression Data -- 3.2.2 Protein-Protein Interaction Data -- 3.2.3 Protein-DNA Interaction Data -- 3.2.4 Gene Ontology -- 3.3 Topological Analysis of Gene Regulatory Networks -- 3.3.1 Node Degree -- 3.3.2 Neighborhood Connectivity -- 3.3.3 Shortest Paths -- 3.3.4 Reconstruction of Transcriptional Regulatory Network -- 3.4 GRN Inference by Integration of Multi-Source Biological Data -- 3.4.1 Gene Module Selection -- 3.4.2 Network Motif Discovery -- 3.4.3 Gene Regulatory Module Inference -- 3.5 Conclusions and Future Directions -- Acknowledgment -- References -- II EAs for Gene Expression Data Analysis and GRN Reconstruction -- 4 Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms -- 4.1 Introduction -- 4.2 Bicluster Analysis of Data -- 4.3 Biclustering Techniques -- 4.3.1 Distance-Based Techniques.
4.3.2 Factorization-Based Techniques -- 4.3.3 Probabilistic-Based Techniques -- 4.3.4 Geometric-Based Biclustering -- 4.3.5 Biclustering for Coherent Evolution -- 4.4 Evolutionary Algorithms Based Biclustering -- 4.5 Conclusion -- References -- 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 -- 5.4.1 Problem Definition -- 5.4.2 Efficient Technique for Solving Simultaneous Equations -- 5.5 REX/JGG -- 5.5.1 JGG -- 5.5.2 REX -- 5.6 Inference of an Artificial Network -- 5.6.1 Experimental Setup -- 5.6.2 Results -- 5.7 Inference of an Actual Genetic Network -- 5.7.1 Experimental Setup -- 5.7.2 Results -- 5.8 Conclusion -- Acknowledgements -- References -- 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 -- 6.3.1 Mass-Action-Based Modeling of Gene Regulation -- 6.3.2 Cartesian Genetic Programming -- 6.3.3 Particle Swarm Optimization -- 6.3.4 General-Purpose GPU Computing -- 6.4 Design Methodology of Gene Regulation Models by Means of CGP and PSO -- 6.5 Results -- 6.5.1 ED of Synthetic Circuits with Two Genes -- 6.5.2 ED of Synthetic Circuits with Three Genes -- 6.5.3 Computational Results -- 6.6 Discussion -- 6.7 Conclusions and Future Perspectives -- References -- 7 Modeling Dynamic Gene Expression in Streptomyces Coelicolor: Comparing Single and Multi-Objective Setups -- 7.1 Introduction -- 7.1.1 Modeling Gene Expression -- 7.1.2 Reverse Engineering Biological Networks from Expression Data -- 7.1.3 The Life Cycle of Streptomyces coelicolor -- 7.1.4 The PhoP Sub-Network -- 7.1.5 Computational Approach.
7.2 Regulatory Networks and Gene Expression Data -- 7.2.1 Bacterial Sub-Networks -- 7.2.2 Data Normalization -- 7.3 Optimization Using Evolutionary Algorithms -- 7.4 Modeling Gene Expression -- 7.4.1 Single Objective Setup -- 7.4.2 Multi-Objective Setup -- 7.4.3 Decoupled Approach -- 7.5 Results -- 7.5.1 Comparing Objectives from Un-Normalized Data -- 7.5.2 Full Network Optimization -- 7.5.3 Decoupled Network Optimization -- 7.6 Discussion -- 7.7 Conclusions -- References -- 8 Reconstruction of Large-Scale Gene Regulatory Network using S-system Model -- 8.1 Introduction -- 8.1.1 Significance of Inferring Large-Scale Gene Regulatory Networks -- 8.2 Reverse Engineering GRN with S-System Model and Evolutionary Computation -- 8.2.1 S-System Model -- 8.2.2 An Evolutionary Framework: Differential Evolution -- 8.2.3 Model Evaluation Criteria -- 8.2.4 Limitations of S-System Modeling in Inferring Large-Scale GRN -- 8.3 The Proposed Framework for Inferring Large-Scale GRN -- 8.3.1 Adapted S-System Model -- 8.3.2 New Fitness Function -- 8.3.3 Multiple-Cardinality-Based Diversification -- 8.4 Experimental Results -- 8.5 Discussions -- 8.6 Conclusion -- Acknowledgments -- References -- III EAs for Evolving GRNs and Reaction Networks -- 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 -- 9.2.1 Domain-Level Modeling -- 9.2.2 Hydrogen Bond Reactions -- 9.2.3 Enzymatic Reactions -- 9.2.4 Graph-Based Model -- 9.3 Simulation by Chemical Kinetics -- 9.3.1 Enumeration of Structure -- 9.3.2 Time Evolution of Catalytic Gate and RTRACS -- 9.4 Automatic Design of Nucleic Acid Reaction System -- 9.4.1 Algorithm of Evolutionary Computation -- 9.4.2 Genotype of Nucleic Acid Reaction System -- 9.4.3 Simulation of Phenotype, Generation, and Selection.
9.4.4 Evaluation Function of Logic Gate -- 9.4.5 Evaluation Function of Automaton -- 9.4.6 Automatically Designed Logic Gates Driven by Hybridization Reaction -- 9.4.7 Automatically Designed AND Gate Driven by Enzymatic Reaction -- 9.4.8 Automatically Designed Automaton Sensing the Stimuli from Environment -- 9.5 Discussion and Conclusion -- 9.5.1 Discussion -- 9.5.2 Conclusion -- References -- 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 -- 10.2.1 Coarse-Grained Approaches -- 10.2.2 Fine-Grained Approaches -- 10.3 Using Evolutionary Computations to Investigate Biological Evolution -- 10.3.1 Evolvability and Robustness -- 10.3.2 Crossover -- 10.3.3 GRN Outgrowth -- 10.3.4 Characterization of GRN Space -- 10.3.5 Epistasis -- 10.3.6 Body Segmentation -- 10.4 Conclusions -- Acknowledgements -- References -- 11 Evolving GRN-inspired In Vitro Oscillatory Systems -- 11.1 Introduction -- 11.2 PEN DNA Toolbox -- 11.2.1 Overview -- 11.2.2 Simplified Model -- 11.2.3 Internal State of the Templates -- 11.2.4 Sequence Dependence -- 11.2.5 Enzymatic Saturation -- 11.3 Related Work -- 11.4 Framework for Evolving Reaction Networks (ERNe) -- 11.4.1 Encoding -- 11.4.2 Mutations -- 11.4.3 Crossover -- 11.4.4 Speciation -- 11.5 ERNe for the Discovery of Oscillatory Systems -- 11.5.1 Fast-Strong Oscillator -- 11.5.2 Robust-Fast-Strong Oscillatior -- 11.6 Discussion -- 11.7 Conclusion -- References -- IV Application of GRN with EAs -- 12 Artificial Gene Regulatory Networks for Agent Control -- 12.1 Introduction -- 12.2 Computation Model -- 12.2.1 Representation of the Proteins -- 12.2.2 Dynamics -- 12.2.3 Encoding and Genetic Evolution -- 12.3 Visualizing The GRN Abilities.
12.4 Growing Multicellular Organisms -- 12.4.1 Resisting to Extern Aggressions -- 12.4.2 Resisting to Aggression and Starvation -- 12.5 Driving a Virtual Car -- 12.6 Regulating Behaviors -- 12.7 Conclusion -- References -- 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 -- 13.3.1 Upper Layer: Region Generation -- 13.3.2 Lower Layer: Region-Based Shape Control -- 13.3.3 Implementation Issues -- 13.3.4 Numerical Simulations -- 13.4 Evolving H-GRN Using Network Motifs -- 13.4.1 Basic Network Motifs -- 13.4.2 Upper Layer of the EH-GRN -- 13.4.3 Lower Layer of the EH-GRN -- 13.4.4 Numerical Simulations -- 13.5 Conclusions and Future Work -- Acknowledgment -- Appendix -- A.13.1 Convergence Proof -- A.13.2 Position and Velocity Estimation -- References -- 14 Regulatory Representations in Architectural Design -- 14.1 Introduction -- 14.2 Background -- 14.3 The Need for Regulatory Representations -- 14.4 Developmental Mapping -- 14.4.1 Encoding -- 14.4.2 Representation -- 14.4.3 Experimental Results -- 14.5 Robustness and Evolutionary Adaptation in Biological Systems -- 14.5.1 Hypothesis -- 14.5.2 Experimental Results -- 14.5.3 Canalization of Gene Networks -- 14.5.4 Neutral Shaping of Canalized Gene Networks -- 14.5.5 Neutral Mutations Contribute to Evolutionary Innovations -- 14.6 Conclusions and Discussion -- Acknowledgments -- References -- 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 -- 15.6.1 Boolean Networks -- 15.6.2 Artificial Genome Models -- 15.6.3 Artificial Development -- 15.6.4 Fractal Gene Regulatory Networks.
15.6.5 Artificial Biochemical Networks.
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Intro -- Evolutionary Computation in Gene Regulatory Network Research -- Contents -- Preface -- Acknowledgments -- Contributors -- I Preliminaries -- 1 A Brief Introduction to Evolutionary and other Nature-Inspired Algorithms -- 1.1 Introduction -- 1.2 Classes of Evolutionary Computation -- 1.2.1 Genetic Algorithms -- 1.2.2 Genetic Programming -- 1.2.3 Evolution Strategy -- 1.2.4 Differential Evolution -- 1.2.5 Swarm Intelligence -- 1.2.6 Multi-Objective EA's -- 1.3 Advantages/Disadvantages of Evolutionary Computation -- 1.4 Application Areas Of EC -- 1.5 Conclusion -- References -- 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 -- References -- 3 Gene Regulatory Networks: Real Data Sources and Their Analysis -- 3.1 Introduction -- 3.2 Biological Data Sources -- 3.2.1 Gene Expression Data -- 3.2.2 Protein-Protein Interaction Data -- 3.2.3 Protein-DNA Interaction Data -- 3.2.4 Gene Ontology -- 3.3 Topological Analysis of Gene Regulatory Networks -- 3.3.1 Node Degree -- 3.3.2 Neighborhood Connectivity -- 3.3.3 Shortest Paths -- 3.3.4 Reconstruction of Transcriptional Regulatory Network -- 3.4 GRN Inference by Integration of Multi-Source Biological Data -- 3.4.1 Gene Module Selection -- 3.4.2 Network Motif Discovery -- 3.4.3 Gene Regulatory Module Inference -- 3.5 Conclusions and Future Directions -- Acknowledgment -- References -- II EAs for Gene Expression Data Analysis and GRN Reconstruction -- 4 Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms -- 4.1 Introduction -- 4.2 Bicluster Analysis of Data -- 4.3 Biclustering Techniques -- 4.3.1 Distance-Based Techniques.

4.3.2 Factorization-Based Techniques -- 4.3.3 Probabilistic-Based Techniques -- 4.3.4 Geometric-Based Biclustering -- 4.3.5 Biclustering for Coherent Evolution -- 4.4 Evolutionary Algorithms Based Biclustering -- 4.5 Conclusion -- References -- 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 -- 5.4.1 Problem Definition -- 5.4.2 Efficient Technique for Solving Simultaneous Equations -- 5.5 REX/JGG -- 5.5.1 JGG -- 5.5.2 REX -- 5.6 Inference of an Artificial Network -- 5.6.1 Experimental Setup -- 5.6.2 Results -- 5.7 Inference of an Actual Genetic Network -- 5.7.1 Experimental Setup -- 5.7.2 Results -- 5.8 Conclusion -- Acknowledgements -- References -- 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 -- 6.3.1 Mass-Action-Based Modeling of Gene Regulation -- 6.3.2 Cartesian Genetic Programming -- 6.3.3 Particle Swarm Optimization -- 6.3.4 General-Purpose GPU Computing -- 6.4 Design Methodology of Gene Regulation Models by Means of CGP and PSO -- 6.5 Results -- 6.5.1 ED of Synthetic Circuits with Two Genes -- 6.5.2 ED of Synthetic Circuits with Three Genes -- 6.5.3 Computational Results -- 6.6 Discussion -- 6.7 Conclusions and Future Perspectives -- References -- 7 Modeling Dynamic Gene Expression in Streptomyces Coelicolor: Comparing Single and Multi-Objective Setups -- 7.1 Introduction -- 7.1.1 Modeling Gene Expression -- 7.1.2 Reverse Engineering Biological Networks from Expression Data -- 7.1.3 The Life Cycle of Streptomyces coelicolor -- 7.1.4 The PhoP Sub-Network -- 7.1.5 Computational Approach.

7.2 Regulatory Networks and Gene Expression Data -- 7.2.1 Bacterial Sub-Networks -- 7.2.2 Data Normalization -- 7.3 Optimization Using Evolutionary Algorithms -- 7.4 Modeling Gene Expression -- 7.4.1 Single Objective Setup -- 7.4.2 Multi-Objective Setup -- 7.4.3 Decoupled Approach -- 7.5 Results -- 7.5.1 Comparing Objectives from Un-Normalized Data -- 7.5.2 Full Network Optimization -- 7.5.3 Decoupled Network Optimization -- 7.6 Discussion -- 7.7 Conclusions -- References -- 8 Reconstruction of Large-Scale Gene Regulatory Network using S-system Model -- 8.1 Introduction -- 8.1.1 Significance of Inferring Large-Scale Gene Regulatory Networks -- 8.2 Reverse Engineering GRN with S-System Model and Evolutionary Computation -- 8.2.1 S-System Model -- 8.2.2 An Evolutionary Framework: Differential Evolution -- 8.2.3 Model Evaluation Criteria -- 8.2.4 Limitations of S-System Modeling in Inferring Large-Scale GRN -- 8.3 The Proposed Framework for Inferring Large-Scale GRN -- 8.3.1 Adapted S-System Model -- 8.3.2 New Fitness Function -- 8.3.3 Multiple-Cardinality-Based Diversification -- 8.4 Experimental Results -- 8.5 Discussions -- 8.6 Conclusion -- Acknowledgments -- References -- III EAs for Evolving GRNs and Reaction Networks -- 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 -- 9.2.1 Domain-Level Modeling -- 9.2.2 Hydrogen Bond Reactions -- 9.2.3 Enzymatic Reactions -- 9.2.4 Graph-Based Model -- 9.3 Simulation by Chemical Kinetics -- 9.3.1 Enumeration of Structure -- 9.3.2 Time Evolution of Catalytic Gate and RTRACS -- 9.4 Automatic Design of Nucleic Acid Reaction System -- 9.4.1 Algorithm of Evolutionary Computation -- 9.4.2 Genotype of Nucleic Acid Reaction System -- 9.4.3 Simulation of Phenotype, Generation, and Selection.

9.4.4 Evaluation Function of Logic Gate -- 9.4.5 Evaluation Function of Automaton -- 9.4.6 Automatically Designed Logic Gates Driven by Hybridization Reaction -- 9.4.7 Automatically Designed AND Gate Driven by Enzymatic Reaction -- 9.4.8 Automatically Designed Automaton Sensing the Stimuli from Environment -- 9.5 Discussion and Conclusion -- 9.5.1 Discussion -- 9.5.2 Conclusion -- References -- 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 -- 10.2.1 Coarse-Grained Approaches -- 10.2.2 Fine-Grained Approaches -- 10.3 Using Evolutionary Computations to Investigate Biological Evolution -- 10.3.1 Evolvability and Robustness -- 10.3.2 Crossover -- 10.3.3 GRN Outgrowth -- 10.3.4 Characterization of GRN Space -- 10.3.5 Epistasis -- 10.3.6 Body Segmentation -- 10.4 Conclusions -- Acknowledgements -- References -- 11 Evolving GRN-inspired In Vitro Oscillatory Systems -- 11.1 Introduction -- 11.2 PEN DNA Toolbox -- 11.2.1 Overview -- 11.2.2 Simplified Model -- 11.2.3 Internal State of the Templates -- 11.2.4 Sequence Dependence -- 11.2.5 Enzymatic Saturation -- 11.3 Related Work -- 11.4 Framework for Evolving Reaction Networks (ERNe) -- 11.4.1 Encoding -- 11.4.2 Mutations -- 11.4.3 Crossover -- 11.4.4 Speciation -- 11.5 ERNe for the Discovery of Oscillatory Systems -- 11.5.1 Fast-Strong Oscillator -- 11.5.2 Robust-Fast-Strong Oscillatior -- 11.6 Discussion -- 11.7 Conclusion -- References -- IV Application of GRN with EAs -- 12 Artificial Gene Regulatory Networks for Agent Control -- 12.1 Introduction -- 12.2 Computation Model -- 12.2.1 Representation of the Proteins -- 12.2.2 Dynamics -- 12.2.3 Encoding and Genetic Evolution -- 12.3 Visualizing The GRN Abilities.

12.4 Growing Multicellular Organisms -- 12.4.1 Resisting to Extern Aggressions -- 12.4.2 Resisting to Aggression and Starvation -- 12.5 Driving a Virtual Car -- 12.6 Regulating Behaviors -- 12.7 Conclusion -- References -- 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 -- 13.3.1 Upper Layer: Region Generation -- 13.3.2 Lower Layer: Region-Based Shape Control -- 13.3.3 Implementation Issues -- 13.3.4 Numerical Simulations -- 13.4 Evolving H-GRN Using Network Motifs -- 13.4.1 Basic Network Motifs -- 13.4.2 Upper Layer of the EH-GRN -- 13.4.3 Lower Layer of the EH-GRN -- 13.4.4 Numerical Simulations -- 13.5 Conclusions and Future Work -- Acknowledgment -- Appendix -- A.13.1 Convergence Proof -- A.13.2 Position and Velocity Estimation -- References -- 14 Regulatory Representations in Architectural Design -- 14.1 Introduction -- 14.2 Background -- 14.3 The Need for Regulatory Representations -- 14.4 Developmental Mapping -- 14.4.1 Encoding -- 14.4.2 Representation -- 14.4.3 Experimental Results -- 14.5 Robustness and Evolutionary Adaptation in Biological Systems -- 14.5.1 Hypothesis -- 14.5.2 Experimental Results -- 14.5.3 Canalization of Gene Networks -- 14.5.4 Neutral Shaping of Canalized Gene Networks -- 14.5.5 Neutral Mutations Contribute to Evolutionary Innovations -- 14.6 Conclusions and Discussion -- Acknowledgments -- References -- 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 -- 15.6.1 Boolean Networks -- 15.6.2 Artificial Genome Models -- 15.6.3 Artificial Development -- 15.6.4 Fractal Gene Regulatory Networks.

15.6.5 Artificial Biochemical Networks.

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