Computational methodologies for modelling, analysis and simulation of signalling networks.
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.
Gilbert
David
D
Bioinformatics Research Centre, A416, Davidson Building University of Glasgow, Glasgow G12 8QQ, Scotland, UK. drg@dcs.gla.ac.uk
Fuss
Hendrik
H
Gu
Xu
X
Orton
Richard
R
Robinson
Steve
S
Vyshemirsky
Vladislav
V
Kurth
Mary Jo
MJ
Downes
C Stephen
CS
Dubitzky
Werner
W
D000465:Algorithms; D000818:Animals; D002450:Cell Communication; D002453:Cell Cycle / Q000502:physiology; D002468:Cell Physiological Phenomena; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D020794:Receptor Protein-Tyrosine Kinases / Q000378:metabolism; D015398:Signal Transduction; D012984:Software; D049490:Systems Biology
D020794:Receptor Protein-Tyrosine Kinases