Models and computational strategies linking physiological response to molecular networks from large-scale data.
An important area of research in systems biology involves the analysis and integration of genome-wide functional datasets. In this context, a major goal is the identification of a putative molecular network controlling physiological response from experimental data. With very fragmentary mechanistic information, this is a challenging task. A number of methods have been developed, each one with the potential to address an aspect of the problem. Here, we review some of the most widely used methodologies and report new results in support of the usefulness of modularization and other modelling techniques in identifying components of the molecular networks that are predictive of physiological response. We also discuss how system identification in biology could be approached, using a combination of methodologies that aim to reconstruct the relationship between molecular pathways and physiology at different levels of the organizational complexity of the molecular network.
School of Biosciences and IBR, University of Birmingham, Birmingham B15 2TT, UK.
Fernando
F
Ortega
Katrin
K
Sameith
Nil
N
Turan
Russell
R
Compton
Victor
V
Trevino
Marina
M
Vannucci
Francesco
F
Falciani
D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D009369:Neoplasms / Q000503:physiopathology; D010641:Phenotype; D010827:Physiology; D049490:Systems Biology