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Novartis Found Symp. 2002;247:104-16; discussion 116-8, 119-28, 244-52.

Bioinformatics of cellular signalling.

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  • 1Department of Bioengineering, The University of California at San Diego and The San Diego Supercomputer Center, La Jolla, CA 92037, USA.

Abstract

The completion of the human genome sequencing provides a unique opportunity to understand the complex functioning of cells in terms of myriad biochemical pathways. Of special significance are pathways involved in cellular signalling. Understanding how signal transduction occurs in cells is of paramount importance to medicine and pharmacology. The major steps involved in deciphering signalling pathways are: (a) identifying the molecules involved in signalling; (b) figuring out who talks to whom, i.e. deciphering molecular interactions in a context specific manner; (c) obtaining the spatiotemporal location of the signalling events; (d) reconstructing signalling modules and networks evoked in specific response to input; (e) correlating the signalling response to different cellular inputs; and (f) deciphering cross-talk between signalling modules in response to single and multiple inputs. High-throughput experimental investigations offer the promise of providing data pertaining to the above steps. A major challenge, then, is the organization of this data into knowledge in the form of hypothesis, models and context-specific understanding. The Alliance for Cellular Signaling (AfCS) is a multi-institution, multidisciplinary project and its primary objective is to utilize a multitude of high throughput approaches to obtain context-specific knowledge of cellular response to input. It is anticipated that the AfCS experimental data in combination with curated gene and protein annotations, available from public repositories, will serve as a basis for reconstruction of signalling networks. It will then be possible to model the networks mathematically to obtain quantitative measures of cellular response. In this paper we describe some of the bioinformatics strategies employed in the AfCS.

PMID:
12539952
[PubMed - indexed for MEDLINE]
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