Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation
1 Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, San Diego, CA 92093, USA
2 Department of Neuroscience, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA
3 Laboratory of Molecular Immunology, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
BMC Systems Biology 2010, 4:128 doi:10.1186/1752-0509-4-128Published: 10 September 2010
An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task.
Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes.
This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.