title: Derivation of large-scale cellular regulatory networks from biological time series data.
abstract: Pharmacological agents and other perturbants of cellular homeostasis appear to nearly universally affect the activity of many genes, proteins, and signaling pathways. While this is due in part to nonspecificity of action of the drug or cellular stress, the large-scale self-regulatory behavior of the cell may also be responsible, as this typically means that when a cell switches states, dozens or hundreds of genes will respond in concert. If many genes act collectively in the cell during state transitions, rather than every gene acting independently, models of the cell can be created that are comprehensive of the action of all genes, using existing data, provided that the functional units in the model are collections of genes. Techniques to develop these large-scale cellular-level models are provided in detail, along with methods of analyzing them, and a brief summary of major conclusions about large-scale cellular networks to date.
authors.affiliation: Rowland Institute at Harvard, Cambridge, MA, USA. debivort@netrider.rowland.org
authors.forename: Benjamin L
authors.initials: BL
authors.lastname: de Bivort
mesh_terms: D002477:Cells / Q000378:metabolism*; D003196:Computer Graphics; D008954:Models, Biological; D015203:Reproducibility of Results; D013223:Statistics as Topic; D049490:Systems Biology / Q000379:methods*; D013997:Time Factors