title: Estimating the parameters of a model for protein-protein interaction graphs.
abstract: We find accurate approximations for the expected number of three-cycles and unchorded four-cycles under a stochastic distribution for graphs that has been proposed for modelling yeast two-hybrid protein-protein interaction networks. We show that unchorded four-cycles are characteristic motifs under this model and that the count of unchorded four-cycles in the graph is a reliable statistic on which to base parameter estimation. Finally, we test our model against a range of experimental data, obtain parameter estimates from these data and investigate possible improvements in the model. Characterization of this model lays the foundation for its use as a prior distribution in a Bayesian analysis of yeast two-hybrid networks that can potentially aid in identifying false-positive and false-negative results.
authors.affiliation: Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775, Salt Lake City, UT 84112-5750, USA. vgdl@utah.edu
authors.forename: Vikrant
authors.initials: V
authors.lastname: Deshmukh
authors.forename: Chris
authors.initials: C
authors.lastname: Cannings
authors.forename: Alun
authors.initials: A
authors.lastname: Thomas
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D013269:Stochastic Processes; D020798:Two-Hybrid System Techniques