Connectionists: Call for Chapters: Computational Methodologies in Gene Regulatory Networks

[sdas@ksu.edu]

CALL FOR CHAPTERS
Submission Deadlines: proposals due on June 15, 2007, full manuscripts
due on December 15, 2007

COMPUTATIONAL METHODOLOGIES IN GENE REGULATORY NETWORKS
URL: www.ksu.edu/cmgrn
Email: cmgrn at ksu.edu
A book edited by Doina Caragea, Sanjoy Das, W. H. Hsu, Stephen M. Welch,
Kansas State University, USA.
(The list of authors is in alphabetical order)

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, 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.

OVERALL OBJECTIVES
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.

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.

RECOMMENDED TOPICS
Recommended topics include, but are not limited to, the following:
Introduction to GRNs
Introduction to graphical approaches for GRNs
Bayesian network models for gene network models
Petri nets and GRN models
Dynamic Bayesian network GRNs
Structure learning of GRNs
Neural network based GRNs
Boolean GRNs
Temporal Boolean GRNs
Probabilistic Boolean GRNs
Machine learning in Boolean networks for GRNs
Differential equation based GRNs
Stochastic optimization algorithms for GRNs
Evolutionary optimization in GRNs
GRNs using the S-system formalism
Optimization of S-system GRNs

SUBMISSION PROCEDURE
Researchers and practitioners are invited to submit on or before June
15, 2007, a 2-5 page manuscript proposal clearly explaining the mission
and concerns of the proposed chapter. Authors of accepted proposals
will be notified by July 15, 2007 about the status of their proposals
and sent chapter organizational guidelines. Full chapters are due on
December 15, 2007. 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
Inquiries and submissions can be forwarded electronically (pdf or word
document) to: cmgrn at ksu.edu
More information can be found at the proposed book's website:
www.ksu.edu/cmgrn
Individual authors can also be contacted directly:

Dr. Sanjoy Das
Elect. & Comp. Engg. Dept.
Kansas State University
sdas at ksu.edu
Tel: (785) 532-4642

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

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

Dr. William H. Hsu
Comp. & Info. Sci. Dept.
Kansas State University
bhsu at ksu.edu
Tel: (785) 532-6350