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.
Price
Nathan D
ND
Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA.
Shmulevich
Ilya
I
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*