title: Probabilistic prediction of unknown metabolic and signal-transduction networks.
abstract: Regulatory networks provide control over complex cell behavior in all kingdoms of life. Here we describe a statistical model, based on representing proteins as collections of domains or motifs, which predicts unknown molecular interactions within these biological networks. Using known protein-protein interactions of Saccharomyces cerevisiae as training data, we were able to predict the links within this network with only 7% false-negative and 10% false-positive error rates. We also use Markov chain Monte Carlo simulation for the prediction of networks with maximum probability under our model. This model can be applied across species, where interaction data from one (or several) species can be used to infer interactions in another. In addition, the model is extensible and can be analogously applied to other molecular data (e.g., DNA sequences).
authors.affiliation: Columbia Genome Center, Columbia University, New York, New York 10032, USA.
authors.forename: S M
authors.initials: SM
authors.lastname: Gomez
authors.forename: S H
authors.initials: SH
authors.lastname: Lo
authors.forename: A
authors.initials: A
authors.lastname: Rzhetsky
mesh_terms: D020816:Amino Acid Motifs; D005821:Genetic Techniques; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D010944:Plants / Q000235:genetics; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D017422:Sequence Analysis, DNA; D015398:Signal Transduction; D012984:Software