title: From the top down: towards a predictive biology of signalling networks.
abstract: True quantitative and predictive biology requires the ability to interpret increasingly complex datasets in new ways to reveal underlying functional interactions. This has come a step closer with two recent articles that describe a 'top-down' modelling approach to reconstructing functional networks from microarray data.
authors.affiliation: Department of Mathematics, Imperial College London, London, UK SW7 2AZ. j.stark@imperial.ac.uk
authors.forename: Jaroslav
authors.initials: J
authors.lastname: Stark
authors.forename: Robin
authors.initials: R
authors.lastname: Callard
authors.forename: Michael
authors.initials: M
authors.lastname: Hubank
mesh_terms: D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D040901:Proteomics / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*