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: [EMAIL PROTECTED]
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: [EMAIL PROTECTED]
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
[EMAIL PROTECTED]
Tel: (785) 532-4642
Dr. Doina Caragea
Comp. & Info. Sci. Dept.
Kansas State University
[EMAIL PROTECTED]
Tel: (785) 532-7908
Dr. William H. Hsu
Comp. & Info. Sci. Dept.
Kansas State University
[EMAIL PROTECTED]
Tel: (785) 532-6350
Dr. Stephen. M. Welch
Dept. of Agronomy
Kansas State University
[EMAIL PROTECTED]
Tel: (785) 532-7236
