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Computational methodologies for modelling, analysis and simulation of signalling networks

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Gilbert, David, Fuss, Hendrik, Gu, Xu, Orton, Richard, Robinson, Steve, Vyshemirsky, Vladislav, Kurth, Mary Jo, Downes, Stephen and Dubitzky, Werner (2006) Computational methodologies for modelling, analysis and simulation of signalling networks. BRIEFINGS IN BIOINFORMATICS, 7 (4). pp. 339-353. [Journal article]

Full text not available from this repository.

DOI: 10.1093/bib/bbl043

Abstract

This article is a critical review of computational techniques used to model, analyse and simulate signalling networks. We propose a conceptual framework, and discuss the role of signalling networks in three major areas: signal transduction, cellular rhythms and cell-to-cell communication. In order to avoid an overly abstract and general discussion, we focus on three case studies in the areas of receptor signalling and kinase cascades, cell-cycle regulation and wound healing. We report on a variety of modelling techniques and associated tools, in addition to the traditional approach based on ordinary differential equations (ODEs), which provide a range of descriptive and analytical powers. As the field matures, we expect a wider uptake of these alternative approaches for several reasons, including the need to take into account low protein copy numbers and noise and the great complexity of cellular organisation. An advantage offered by many of these alternative techniques, which have their origins in computing science, is the ability to perform sophisticated model analysis which can better relate predicted behaviour and observations.

Item Type:Journal article
Faculties and Schools:Faculty of Life and Health Sciences
Faculty of Life and Health Sciences > School of Biomedical Sciences
Research Institutes and Groups:Biomedical Sciences Research Institute
Biomedical Sciences Research Institute > Molecular Medicine
Biomedical Sciences Research Institute > Molecular Medicine > Nano Systems Biology
ID Code:3382
Deposited By:Professor Stephen Downes
Deposited On:15 Dec 2009 11:44
Last Modified:10 Jun 2010 11:01

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