Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data.
BACKGROUND
Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data.


RESULTS
We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.
Nariai
Naoki
N
Human Genome Center, Institute of Medical Science, University of Tokyo, Shirokanedai, Minato-ku, Tokyo, Japan. nariai@ims.u-tokyo.ac.jp
Tamada
Yoshinori
Y
Imoto
Seiya
S
Miyano
Satoru
S
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation / Q000502:physiology*; D008957:Models, Genetic; D025941:Protein Interaction Mapping / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D029701:Saccharomyces cerevisiae Proteins