Computational methodologies for modelling, analysis and simulation of signalling networks

• Published in: Briefings in bioinformatics Vol. 7; no. 4; pp. 339 - 353
• Main Authors: Gilbert, David, Fuss, Hendrik, Gu, Xu, Orton, Richard, Robinson, Steve, Vyshemirsky, Vladislav, Kurth, Mary Jo, Downes, C Stephen, Dubitzky, Werner
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.12.2006
• Subjects: Algorithms; Animals; Bioinformatics; Cell Communication; Cell Cycle - physiology; Cell Physiological Phenomena; Cellular biology; Computational Biology - methods; Computer Simulation; Data analysis; Humans; Models, Biological; Receptor Protein-Tyrosine Kinases - metabolism; Research methodology; Signal Transduction; Software; Systems Biology
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbl043

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.