title: Inferring network interactions within a cell.
abstract: The continuing growth in high-throughput data acquisition has led to a proliferation of network models to represent and analyse biological systems. These networks involve distinct interaction types detected by a combination of methods, ranging from directly observed physical interactions based in biochemistry to interactions inferred from phenotype measurements, genomic expression and comparative genomics. The discovery of interactions increasingly requires a blend of experimental and computational methods. Considering yeast as a model system, recent analytical methods are reviewed here and specific aims are proposed to improve network interaction inference and facilitate predictive biological modelling.
authors.affiliation: Institute for Systems Biology, 1441 N. 34th Street, Seattle, WA 98103, USA. gcarter@systemsbiology.org
authors.forename: Greg W
authors.initials: GW
authors.lastname: Carter
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D015966:Gene Expression Regulation, Fungal / Q000502:physiology; D008954:Models, Biological; D040081:Protein Array Analysis / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D029701:Saccharomyces cerevisiae Proteins