Prediction of Quantitative Phenotypes Based on Genetic Networks: a Case Study in Yeast Sporulation

Overview

Journal BMC Syst Biol

Publisher Biomed Central

Specialty Biology

Date 2010 Sep 11

PMID 20828418

Citations 5

Authors

Li Shen

Iouri Chepelev

Jie Liu

Wei Wang

Affiliations

Soon will be listed here.

Abstract

Background: 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.

Results: 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.

Conclusions: This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.

Citing Articles

Fused multi-modal similarity network as prior in guiding brain imaging genetic association.

He B, Xie L, Varathan P, Nho K, Risacher S, Saykin A Front Big Data. 2023; 6:1151893.

PMID: 37215688 PMC: 10196480. DOI: 10.3389/fdata.2023.1151893.

Dynamic modeling of yeast meiotic initiation.

Ray D, Su Y, Ye P BMC Syst Biol. 2013; 7:37.

PMID: 23631506 PMC: 3772702. DOI: 10.1186/1752-0509-7-37.

A checkpoints capturing timing-robust Boolean model of the budding yeast cell cycle regulatory network.

Hong C, Lee M, Kim D, Kim D, Cho K, Shin I BMC Syst Biol. 2012; 6:129.

PMID: 23017186 PMC: 3573974. DOI: 10.1186/1752-0509-6-129.

Systematic search for recipes to generate induced pluripotent stem cells.

Chang R, Shoemaker R, Wang W PLoS Comput Biol. 2012; 7(12):e1002300.

PMID: 22215993 PMC: 3245295. DOI: 10.1371/journal.pcbi.1002300.

Recipes and mechanisms of cellular reprogramming: a case study on budding yeast Saccharomyces cerevisiae.

Ding S, Wang W BMC Syst Biol. 2011; 5:50.

PMID: 21486480 PMC: 3094211. DOI: 10.1186/1752-0509-5-50.

References

Deutschbauer A, Williams R, Chu A, Davis R . Parallel phenotypic analysis of sporulation and postgermination growth in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A. 2002; 99(24):15530-5. PMC: 137751. DOI: 10.1073/pnas.202604399. View

Huttenhower C, Haley E, Hibbs M, Dumeaux V, Barrett D, Coller H . Exploring the human genome with functional maps. Genome Res. 2009; 19(6):1093-106. PMC: 2694471. DOI: 10.1101/gr.082214.108. View

Liang S, Fuhrman S, Somogyi R . Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput. 1998; :18-29. View

Guttmann-Raviv N, Martin S, Kassir Y . Ime2, a meiosis-specific kinase in yeast, is required for destabilization of its transcriptional activator, Ime1. Mol Cell Biol. 2002; 22(7):2047-56. PMC: 133691. DOI: 10.1128/MCB.22.7.2047-2056.2002. View

Friedlander G, Joseph-Strauss D, Carmi M, Zenvirth D, Simchen G, Barkai N . Modulation of the transcription regulatory program in yeast cells committed to sporulation. Genome Biol. 2006; 7(3):R20. PMC: 1557749. DOI: 10.1186/gb-2006-7-3-r20. View

Chu S, Derisi J, Eisen M, Mulholland J, Botstein D, Brown P . The transcriptional program of sporulation in budding yeast. Science. 1998; 282(5389):699-705. DOI: 10.1126/science.282.5389.699. View

Shen L, Liu J, Wang W . GBNet: deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach. BMC Bioinformatics. 2008; 9:395. PMC: 2571992. DOI: 10.1186/1471-2105-9-395. View

Karni S, Soreq H, Sharan R . A network-based method for predicting disease-causing genes. J Comput Biol. 2009; 16(2):181-9. DOI: 10.1089/cmb.2008.05TT. View

Chen J, Aronow B, Jegga A . Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinformatics. 2009; 10:73. PMC: 2657789. DOI: 10.1186/1471-2105-10-73. View

10.

Wu X, Jiang R, Zhang M, Li S . Network-based global inference of human disease genes. Mol Syst Biol. 2008; 4:189. PMC: 2424293. DOI: 10.1038/msb.2008.27. View

11.

Vershon A, Pierce M . Transcriptional regulation of meiosis in yeast. Curr Opin Cell Biol. 2000; 12(3):334-9. DOI: 10.1016/s0955-0674(00)00104-6. View

12.

Wang W, Cherry J, Nochomovitz Y, Jolly E, Botstein D, Li H . Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation. Proc Natl Acad Sci U S A. 2005; 102(6):1998-2003. PMC: 548531. DOI: 10.1073/pnas.0405537102. View

13.

Kassir Y, Adir N, Boger-Nadjar E, Raviv N, Rubin-Bejerano I, Sagee S . Transcriptional regulation of meiosis in budding yeast. Int Rev Cytol. 2003; 224:111-71. DOI: 10.1016/s0074-7696(05)24004-4. View

14.

Bonneau R, Facciotti M, Reiss D, Schmid A, Pan M, Kaur A . A predictive model for transcriptional control of physiology in a free living cell. Cell. 2007; 131(7):1354-65. DOI: 10.1016/j.cell.2007.10.053. View

15.

Suka N, Carmen A, Rundlett S, Grunstein M . The regulation of gene activity by histones and the histone deacetylase RPD3. Cold Spring Harb Symp Quant Biol. 1999; 63:391-9. DOI: 10.1101/sqb.1998.63.391. View

16.

Kim W, Krumpelman C, Marcotte E . Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy. Genome Biol. 2008; 9 Suppl 1:S5. PMC: 2447539. DOI: 10.1186/gb-2008-9-s1-s5. View

17.

Lee I, Lehner B, Crombie C, Wong W, Fraser A, Marcotte E . A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nat Genet. 2008; 40(2):181-8. DOI: 10.1038/ng.2007.70. View

18.

Zhu J, Zhang B, Smith E, Drees B, Brem R, Kruglyak L . Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet. 2008; 40(7):854-61. PMC: 2573859. DOI: 10.1038/ng.167. View

19.

McGary K, Lee I, Marcotte E . Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes. Genome Biol. 2007; 8(12):R258. PMC: 2246260. DOI: 10.1186/gb-2007-8-12-r258. View

20.

Primig M, Williams R, Winzeler E, Tevzadze G, Conway A, Hwang S . The core meiotic transcriptome in budding yeasts. Nat Genet. 2000; 26(4):415-23. DOI: 10.1038/82539. View