Computational Methodologies in Gene Regulatory Networks

URL: http://ww.k-state.edu/cmgrn

Email: cmgrn@ksu.edu


A book edited by 

Sanjoy Das, Doina Caragea, W. H. Hsu, Stephen M. Welch  

Kansas State University, USA.

INTRODUCTION

Recent advances in gene sequencing technology are shedding light on the complex interplay between genes that elicit phenotypic behavior characteristic of any given organism. It is now known that in order to mediate external as well as internal signals, an organism's genes are organized into complex signaling pathways. Unfortunately, unraveling the specific details about how these genetic pathways interact to regulate development, life histories, evolve over time, and respond to environmental cues, is proving to be a daunting task. A wide variety of models depicting gene-gene interactions, that are commonly referred to as gene regulatory networks (GRNs), have been proposed. A wide variety of computational tools are available for modeling gene regulatory networks.

The Overall Objective of the Book
A gene regulatory network (GRN) must be able to mimic experimentally observed behavior and also be computationally tractable. Under these circumstances, model simplicity is an important trade-off for functional fidelity. Modeling approaches taken by researchers are wide and disparate. Some gene regulatory networks are modeled entirely using non-parametric approaches such as Bayesian or neural networks, while some others represent genes in very physically realistic differential equation formats. The book will focus on the computational methods widely used in modeling gene regulatory networks, including structure discovery, learning and optimization. Both research and survey papers are welcome.

The Target Audience
Biologists: The book can provide a comprehensive overview of computational intelligence approaches for learning and optimization and their use in gene regulatory networks to biologists. Computer Scientists: The book can assist computer scientists interested in gene regulatory network modeling. Classroom instructors and students: Although not a textbook, the book can serve as an excellent reference or supplementary material. Graduate students: As the book would bridge the gap between artificial intelligence and genomic research communities, it will be very useful to graduate students considering interdisciplinary research in this direction. Practicing computer scientists and geneticists: The book would be useful to those interested in gene regulatory network modeling.

ACCEPTED CHAPTER PROPOSALS

P01 Computational Approaches for Modeling Intrinsic Noise and Delays in Genetic Regulatory Networks
P02 Modeling Gene Regulatory Networks with Delayed Stochastic Dynamics
P03 What are Gene Regulatory Networks?
P04 Inferring Gene Regulatory Networks from Genetical Genomics data
P05 Dynamic Bayesian Networks for Modeling and Inferring Gene Regulatory Networks
P06 Problems for Structure Learning: Aggregation and Computational Complexity
P07 Synthesis method of gene regulatory networks based on expression patterns
P08 Efficient Inferring Method of Genetic Interactions Based on Time Series of Gene Expression Profiles: Application of Conceptual Modeling by S-system Formalism
P09 Inferring gene regulatory networks: a case study in Drosophila melanogaster using SEBINI-CABIN
P10 Gene Regulatory Networks (GRNs) in the S-system Formalism 
P11 Planning Interventions for Gene Regulatory Networks as Partially Observable Markov Decision Processes
P12 A Model for an Heterogeneous Genetic Network
P13 Inferring genetic regulatory interactions with Bayesian logic-based model
P14 Determining the Properties of Gene Regulatory Networks from Expression Data
P15 Generalized Boolean Networks: How Spatial and Temporal Choices Influence Their Dynamics
P16 Mathematical Modeling of the Lamda Switch–A Fuzzy Logic Approach
P17 Petri nets and GRN models
P18 A Bayes Regularized ODE Model for the Inference of Gene Regulatory Networks
P19 Introduction to GRNs
P20 Complexity of the BN and the PBN models of GRNs and Mappings for Complexity Reduction
P21 Dynamical correlation for clustering expression trajectories in di erentiating stem cells
P22 A Comparison Study of a Quantum-Inspired Evolutionary Algorithm and a Hybrid Differential Swarm Algorithm for Modeling Gene Regulatory Networks
P23 Abstraction Methods for Analysis of Gene Regulatory Networks
P24 Optimization of S-system GRNs
P25 Dynamic links and evolutionary history in gene regulatory networks (GRNs) 
P26 Finding Optimal Models for Gene Networks
P27 (Spot Reserved)
P28 Nonlinear stochastic differential equations method for reverse engineering of gene regulatory networks
P29 Structure Learning of Genetic Regulatory Networks Based on Knowledge Derived from Literature and Microarray Gene Expression
P30 Computational approaches to cis-regulatory binding site prediction
P31 Inferring Gene Regulatory Network by Integrating Heterogeneous Data in a Linear Programming Framework
P32 Integrating expression data, protein-protein interaction data and TF-binding data for improved quality in reverse engineering of gene regulatory networks
P33 A Proposal to Apply Fuzzy C-maps to Model Gene Regulatory Networks
P34 Gene Expression Regulatory Regions in Yeast Amino Acid Biosynthetic Pathways Unveiled by Quantitative Trait Locus Mapping
P35 Multi-objective Optimization for the Parameter Estimation of Gene Regulatory Networks

(Chapter proposals are arranged in no particular order)

If you wish to write a chapter that is not in this list of accepted proposals, please contact the editors as soon as possible.

INSTRUCTION FOR AUTHORS

Full chapters are due on February 15, 2008

Each full chapter should be within 8,000 and 10,000 words, excluding figures and tables.

The APA (American Psychological Association) style must be followed for the references. 

The final manuscripts must be in MS Word format. LaTex files will not be accepted.

ORGANIZATION GUIDELINES

Detailed instructions and a sample chapter will be mailed to the authors separately.

Abstract: Your chapter should include an abstract, consisting of 100-150 words, that provides your readers with an overview of the content of your chapter.

Introduction: Describe the general perspective of this chapter. Toward the end, specifically state the objectives of the chapter.

Background: Provide broad definitions and discussions of the topic and incorporate views of others.

Main Thrust of Chapter: Present your perspective on the issues, controversies, problems, etc. Discuss solutions and recommendations in dealing with the issues.

Conclusion: Provide discussion and concluding remarks.

Future Research Directions: Briefly describe future research directions, opportunities, emerging trends, or additional ideas you have to offer.

References: References should relate only to material cited within the manuscript and be listed in alphabetical order and in APA format.

Additional Reading: Provide a list of 25-50 additional readings (e.g. journal articles, book chapters, case studies, etc.), in APA format.

All submitted chapters will be reviewed on a double-blind review basis. 

The book is scheduled to be published by IGI Global, www.igi-pub.com , publisher of the IGI Publishing (formerly Idea Group Publishing), Information Science Publishing, IRM Press, CyberTech Publishing and Information Science Reference (formerly Idea Group Reference) imprints.

Inquiries and submissions can be forwarded electronically to: cmgrn@ksu.edu
More information can be found at the forthcoming book's website: http://ww.k-state.edu/cmgrn
Individual authors can also be contacted directly:

Dr. Sanjoy Das
Elec. & Comp. Eng. Dept.
Kansas State University
sdas@ksu.edu
Tel: (785) 532-4642

Dr. Doina Caragea
Comp. & Info. Sci. Dept.
Kansas State University
dcaragea@ksu.edu
Tel: (785) 532-7908

Dr. William H. Hsu
Comp. & Info. Sci. Dept.
Kansas State University
bhsu@ksu.edu
Tel: (785) 532-7905

Dr. S. M. Welch
Dept. of Agronomy
Kansas State University
welchsm@ksu.edu
Tel: (785) 532-7236