Biochemical and statistical network models for systems biology.
The normal and abnormal behavior of a living cell is governed by complex networks of interacting biomolecules. Models of these networks allow us to make predictions about cellular behavior under a variety of environmental cues. In this review, we focus on two broad classes of such models: biochemical network models and statistical inference models. In particular, we discuss a number of modeling approaches in the context of the assumptions that they entail, the types of data required for their inference, and the range of their applicability.
Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA.
Nathan D
ND
Price
Ilya
I
Shmulevich
D019295:Computational Biology / Q000379:methods; D053858:Metabolic Networks and Pathways / Q000235:genetics / Q000502:physiology*; D015233:Models, Statistical; D040901:Proteomics / Q000379:methods; D049490:Systems Biology / Q000379:methods*