W3C home > Mailing lists > Public > public-semweb-lifesci@w3.org > August 2007

CFC: Computational Methodologies in Gene Regulatory Networks

From: Doina Caragea <dcaragea@ksu.edu>
Date: Sat, 18 Aug 2007 21:46:29 -0500
Message-Id: <F10294F6-1B5B-4424-B8AF-D76AFFC55E30@ksu.edu>
Cc: Doina Caragea <dcaragea@cs.iastate.edu>
To: ml@isle.org, public-semweb-lifesci@w3.org, news-announce-conferences@uunet.uu.net, dai-list@mcc.com, community@mlnet.org, bioinfo@dnalinux.com, bioinformatics@sdsc.edu, BIOINF-GENERAL@LISTS.UMN.EDU


CALL FOR CHAPTERS: COMPUTATIONAL METHODOLOGIES IN GENE REGULATORY  
NETWORKS

Dear potential author,

Please accept our apologies if you received multiple copies of this  
invitation.

We request you to submit a chapter for our forthcoming book,  
"Computational Methodologies in Gene Regulatory Networks" on a topic  
of your interest.

http://www.k-state.edu/cmgrn/

http://www.igi-pub.com/requests/details.asp?ID=205

Email: cmgrn@ksu.edu

Proposals due: September 15, 2007

Sincerely,

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


The details are as follows:

CALL FOR CHAPTERS
Submission Deadlines: proposals due on September 15, 2007, full  
manuscripts due on February 15, 2008

COMPUTATIONAL METHODOLOGIES IN GENE REGULATORY NETWORKS
URL: www.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, 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
Clustering in GRNs

SUBMISSION PROCEDURE
Researchers and practitioners are invited to submit on or before  
September 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 October 15, 2007  
about the status of their proposals and sent chapter organizational  
guidelines.
Full chapters are due on February 15, 2008.

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@ksu.edu
More information can be found at the proposed book's website:
http://www.k-state.edu/cmgrn/
or
http://www.igi-pub.com/requests/details.asp?ID=205

Individual authors can also be contacted directly:

Dr. Sanjoy Das
Elect. & Comp. Engg. 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. Stephen. M. Welch
Dept. of Agronomy
Kansas State University
welchsm@ksu.edu
Tel: (785) 532-7236

Dr. William H. Hsu
Comp. & Info. Sci. Dept.
Kansas State University
bhsu@ksu.edu
Tel: (785) 532-7905
Received on Sunday, 19 August 2007 02:53:55 GMT

This archive was generated by hypermail 2.3.1 : Tuesday, 26 March 2013 18:00:49 GMT