Evolutionary computation in gene regulatory network research / edited by Hitoshi Iba, Nasimul Noman ; contributors, Tatsuya Akutsu [and thirty others].

Format:

Book

Contributor:

Iba, Hitoshi, editor.

Noman, Nasimul, editor.

Akutsu, Tatsuya, 1962- contributor.

Series:

Wiley series on bioinformatics.

Wiley Series in Bioinformatics: Computational Techniques and Engineering.

THEi Wiley ebooks.

Language:

English

Subjects (All):

Genetic regulation--Mathematical models.

Genetic regulation.

Gene regulatory networks--Computer simulation.

Gene regulatory networks.

Genetic algorithms.

Evolutionary computation.

Physical Description:

1 online resource (462 pages)

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2016.

Language Note:

English

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

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

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

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

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

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

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

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

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

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

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

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

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

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

Appendix

A.13.1 Convergence Proof

A.13.2 Position and Velocity Estimation

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

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.

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9781119079781

1119079780

9781119079774

1119079772

OCLC:

932302629