Analyzing cellular biochemistry in terms of molecular networks.
One way to understand cells and circumscribe the function of proteins is through molecular networks. These networks take a variety of forms including webs of protein-protein interactions, regulatory circuits linking transcription factors and targets, and complex pathways of metabolic reactions. We first survey experimental techniques for mapping networks (e.g., the yeast two-hybrid screens). We then turn our attention to computational approaches for predicting networks from individual protein features, such as correlating gene expression levels or analyzing sequence coevolution. All the experimental techniques and individual predictions suffer from noise and systematic biases. These problems can be overcome to some degree through statistical integration of different experimental datasets and predictive features (e.g., within a Bayesian formalism). Next, we discuss approaches for characterizing the topology of networks, such as finding hubs and analyzing subnetworks in terms of common motifs. Finally, we close with perspectives on how network analysis represents a preliminary step toward a systems approach for modeling cells.
Xia
Yu
Y
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA. yuxia@csb.yale.edu
Yu
Haiyuan
H
Jansen
Ronald
R
Seringhaus
Michael
M
Baxter
Sarah
S
Greenbaum
Dov
D
Zhao
Hongyu
H
Gerstein
Mark
M
D001669:Biochemical Phenomena; D001671:Biochemistry; D002477:Cells / Q000737:chemistry* / Q000378:metabolism*; D016208:Databases, Factual; D066298:In Vitro Techniques; D046911:Macromolecular Substances; D008954:Models, Biological; D040081:Protein Array Analysis; D011506:Proteins / Q000378:metabolism; D013598:Systems Theory; D020798:Two-Hybrid System Techniques
D046911:Macromolecular Substances; D011506:Proteins