Computational approaches for modeling regulatory cellular networks.
Cellular components interact with each other to form networks that process information and evoke biological responses. A deep understanding of the behavior of these networks requires the development and analysis of mathematical models. In this article, different types of mathematical representations for modeling signaling networks are described, and the advantages and disadvantages of each type are discussed. Two experimentally well-studied signaling networks are then used as examples to illustrate the insight that could be gained through modeling. Finally, the modeling approach is expanded to describe how signaling networks might regulate cellular machines and evoke phenotypic behaviors.
Eungdamrong
Narat J
NJ
Department of Pharmacology and Biological Chemistry, Mount Sinai School of Medicine, New York, NY 10029, USA.
Iyengar
Ravi
R
D000818:Animals; D002477:Cells / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D010641:Phenotype; D015398:Signal Transduction; D013598:Systems Theory