title: Resources for integrative systems biology: from data through databases to networks and dynamic system models.
abstract: In systems biology, biologically relevant quantitative modelling of physiological processes requires the integration of experimental data from diverse sources. Recent developments in high-throughput methodologies enable the analysis of the transcriptome, proteome, interactome, metabolome and phenome on a previously unprecedented scale, thus contributing to the deluge of experimental data held in numerous public databases. In this review, we describe some of the databases and simulation tools that are relevant to systems biology and discuss a number of key issues affecting data integration and the challenges these pose to systems-level research.
authors.affiliation: Bioinformatics and Systems Biology Group, Ludwig Institute for Cancer Research, University College London Branch, 91 Riding House Street, London W1W 7BS, UK.
authors.forename: Aylwin
authors.initials: A
authors.lastname: Ng
authors.forename: Borisas
authors.initials: B
authors.lastname: Bursteinas
authors.forename: Qiong
authors.initials: Q
authors.lastname: Gao
authors.forename: Ewan
authors.initials: E
authors.lastname: Mollison
authors.forename: Marketa
authors.initials: M
authors.lastname: Zvelebil
mesh_terms: D000818:Animals; D003198:Computer Simulation; D003625:Data Collection / Q000379:methods* / Q000592:standards; D030541:Databases, Genetic; D015870:Gene Expression; D023281:Genomics; D006801:Humans; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D008958:Models, Molecular; D010641:Phenotype; D040901:Proteomics; D012984:Software; D049490:Systems Biology / Q000379:methods*