Handbook Of Research On Computational Methodologies In Gene Regulatory Networks, 1st Edition

  • Published By:
  • ISBN-10: 1605666866
  • ISBN-13: 9781605666860
  • DDC: 572.8
  • Grade Level Range: College Freshman - College Senior
  • 719 Pages | eBook
  • Original Copyright 2009 | Published/Released January 2011
  • This publication's content originally published in print form: 2009

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Recent advances in gene sequencing technology are now shedding light on the complex interplay between genes that elicit phenotypic behavior characteristic of any given organism. In order to mediate internal and external signals, the daunting task of classifying an organism's genes into complex signaling pathways needs to be completed. The HANDBOOK OF RESEARCHON COMPUTATIONALMETHODOLOGIES IN GENE REGULATORY NETWORKS focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization. This innovative Handbook of Research presents a complete overview of computational intelligence approaches for learning and optimization and how they can be used in gene regulatory networks.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
List of Reviewers.
List of Contributors.
Table of Contents.
Detailed Table of Contents.
1: Introduction.
2: What Are Gene Regulatory Networks?.
3: Introduction to GRNs.
4: Network Inference.
5: Bayesian Networks for Modeling and Inferring Gene Regulatory Networks.
6: Inferring Gene Regulatory Networks from Genetical Genomics Data.
7: Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model.
8: A Bayes Regularized Ordinary Differential Equation Model for the Inference of Gene Regulatory Networks.
9: Modeling Methods.
10: Computational Approaches for Modeling Intrinsic Noise and Delays in Genetic Regulatory Networks.
11: Modeling Gene Regulatory Networks with Delayed Stochastic Dynamics.
12: Nonlinear Stochastic Differential Equations Method for Reverse Engineering of Gene Regulatory Network.
13: Modelling Gene Regulatory Networks Using Computational Intelligence Techniques.
14: Structure and Parameter Learning.
15: A Synthesis Method of Gene Regulatory Networks Based on Gene Expression by Network Learning.
16: Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Micro Array Gene Expression Measurements.
17: Problems for Structure Learning: Aggregation and Computational Complexity.
18: Analysis & Complexity.
19: Complexity of the BN and the PBN Models of GRNs and Mappings for Complexity Reduction.
20: Abstraction Methods for Analysis of Gene Regulatory Networks.
21: Improved Model Checking Techniques for State Space Analysis of Gene Regulatory Networks.
22: Determining the Properties of Gene Regulatory Networks from Expression Data.
23: Generalized Boolean Networks: How Spatial and Temporal Choices Influence Their Dynamics.
24: Heterogenous Data.
25: A Linear Programming Framework for Inferring Gene Regulatory Networks by Integrating Heterogeneous Data.
26: Integrating Various Data Sources for Improved Quality in Reverse Engineering of Gene Regulatory Networks.
27: Network Simulation Studies.
28: Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks.
29: A Model for a Heterogeneous Genetic Network.
30: Other Studies.
31: Planning Interventions for Gene Regulatory Networks as Partially Observable Markov Decision Processes.
32: Mathematical Modeling of the λ Switch: A Fuzzy Logic Approach.
33: Petri Nets and GRN Models.
Compilation of References.
About the Contributors.