Evolutionary computation in gene regulatory network research

Evolutionary computation in gene regulatory network research

"This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC)"-- Provided by publisher.

Detalles Bibliográficos
Otros Autores: Iba, Hitoshi, editor (editor), Noman, Nasimul, editor (contributor), Akutsu, Tatsuya, 1962- contributor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : Wiley 2016.
Edición:1st ed
Colección:Wiley series on bioinformatics.
Materias:
Genetic regulation > Mathematical models.
Gene regulatory networks > Computer simulation.
Genetic algorithms.
Evolutionary computation.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849091906719
Tabla de Contenidos:
  • 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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