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 organisms genes into complex signaling pathways needs to be completed. The Handbook of Research on Computational Methodologies 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.
Author(s): Sanjoy Das, Doina Caragea, Stephen M. Welch, William H. Hsu, Sanjoy Das, Doina Caragea, Stephen M. Welch, William H. Hsu
Series: Handbook of Research On...
Edition: 1
Publisher: Medical Information Science Reference
Year: 2009
Language: English
Pages: 741
Title
......Page 2
List of Reviewers......Page 4
List of Contributors......Page 5
Table of Contents......Page 7
Detailed Table of Contents......Page 12
Preface......Page 23
Acknowledgment......Page 30
What are Gene Regulatory
Networks?......Page 32
Introduction to GRNs......Page 59
Bayesian Networks for
Modeling and Inferring Gene
Regulatory Networks......Page 88
Inferring Gene Regulatory
Networks from Genetical
Genomics Data......Page 110
Inferring Genetic Regulatory
Interactions with Bayesian
Logic-Based Model......Page 139
A Bayes Regularized Ordinary
Differential Equation Model
for the Inference of Gene
Regulatory Networks......Page 170
Computational Approaches
for Modeling Intrinsic Noise
and Delays in Genetic
Regulatory Networks......Page 200
Modeling Gene Regulatory
Networks with Delayed
Stochastic Dynamics......Page 229
Nonlinear Stochastic
Differential Equations Method
for Reverse Engineering of
Gene Regulatory Network......Page 250
Modelling Gene Regulatory
Networks Using Computational
Intelligence Techniques......Page 275
A Synthesis Method of
Gene Regulatory Networks
based on Gene Expression
by Network Learning......Page 297
Structural Learning of Genetic
Regulatory Networks Based
on Prior Biological Knowledge
and Microarray Gene
Expression Measurements......Page 320
Problems for Structure
Learning......Page 341
Complexity of the BN
and the PBN Models of
GRNs and Mappings for
Complexity Reduction......Page 365
Abstraction Methods
for Analysis of Gene
Regulatory Networks......Page 383
Improved Model Checking
Techniques for State
Space Analysis of Gene
Regulatory Networks......Page 417
Determining the Properties
of Gene Regulatory Networks
from Expression Data......Page 436
Generalized Boolean Networks......Page 460
A Linear Programming
Framework for Inferring
Gene Regulatory Networks by
Integrating Heterogeneous Data......Page 481
Integrating Various Data
Sources for Improved Quality
in Reverse Engineering of
Gene Regulatory Networks......Page 507
Dynamic Links and Evolutionary
History in Simulated Gene
Regulatory Networks......Page 529
A Model for a Heterogeneous
Genetic Network......Page 554
Planning Interventions for
Gene Regulatory Networks
as Partially Observable
Markov Decision Processes......Page 577
Mathematical Modeling of the λ Switch......Page 604
Petri Nets and GRN Models......Page 635
Compilation of References......Page 669
About the Contributors......Page 719
Index......Page 734
