Recent studies suggest that many diseases, particularly those that commonly afflict our population, result from interactions among multiple alleles. In an attempt to understand these complex phenotypes, recent experimental efforts in model organisms have focused on measuring such interactions by engineering combinatorial genetic perturbations. Due to the enormous space of possible mutants, brute-force experimental investigation is simply not feasible, and thus, there is a critical need for computational strategies for intelligent exploration of genetic interaction networks. The specific objective of this application is to develop a computational framework for leveraging the existing genomic or proteomic data to enable intelligent direction of combinatorial perturbation studies. The rationale for the proposed research is that although current knowledge of genetic interactions is sparse, the integration of existing genomic and proteomic data can enable the inference of network models that suggest promising candidates for high-throughput interaction screens. Using such computational guidance should enable more efficient characterization of network structure, and ultimately, better understanding of how genes contribute to complex phenotypes. Based on strong findings in preliminary studies, this objective will be accomplished through two specific aims: (1) development of critical normalization methods and quantitative models for colony array-based interaction assays, and (2) novel machine learning-based approaches for iterative model refinement and optimal interaction screen selection. The proposed research is innovative because it would represent one of the first efforts to couple genomic data integration and network inference technology with a large-scale experimental effort, where several months of experimental investigation are based entirely on computational direction. Such an approach will yield insight into how combinatorial perturbations can be used to characterize global modularity and organization, and more generally, would serve as a prototype for hybrid computational-experimental strategies in other genomic contexts.

Public Health Relevance

Many common diseases result from interactions among multiple genes. One approach to studying multigenic interactions is to introduce combinations of mutations in model organisms and observe how they affect the cell. This project proposes to develop computational strategies to guide and interpret these combinatorial perturbation studies, which will ultimately help us better understand and treat multigenic diseases.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG005084-03
Application #
8280356
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Bonazzi, Vivien
Project Start
2010-08-25
Project End
2014-05-31
Budget Start
2012-06-01
Budget End
2014-05-31
Support Year
3
Fiscal Year
2012
Total Cost
$218,662
Indirect Cost
$68,662
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Fung, Shan-Yu; Sofiyev, Vladimir; Schneiderman, Julia et al. (2014) Unbiased screening of marine sponge extracts for anti-inflammatory agents combined with chemical genomics identifies girolline as an inhibitor of protein synthesis. ACS Chem Biol 9:247-57
VanderSluis, Benjamin; Hess, David C; Pesyna, Colin et al. (2014) Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection. Genome Biol 15:R64
Wyche, Thomas P; Piotrowski, Jeff S; Hou, Yanpeng et al. (2014) Forazoline?A: marine-derived polyketide with antifungal in?vivo efficacy. Angew Chem Int Ed Engl 53:11583-6
Deshpande, Raamesh; Vandersluis, Benjamin; Myers, Chad L (2013) Comparison of profile similarity measures for genetic interaction networks. PLoS One 8:e68664
Baryshnikova, Anastasia; Costanzo, Michael; Myers, Chad L et al. (2013) Genetic interaction networks: toward an understanding of heritability. Annu Rev Genomics Hum Genet 14:111-33
Baryshnikova, Anastasia; VanderSluis, Benjamin; Costanzo, Michael et al. (2013) Global linkage map connects meiotic centromere function to chromosome size in budding yeast. G3 (Bethesda) 3:1741-51
Deshpande, Raamesh; Asiedu, Michael K; Klebig, Mitchell et al. (2013) A comparative genomic approach for identifying synthetic lethal interactions in human cancer. Cancer Res 73:6128-36
Wagih, Omar; Usaj, Matej; Baryshnikova, Anastasia et al. (2013) SGAtools: One-stop analysis and visualization of array-based genetic interaction screens. Nucleic Acids Res 41:W591-6
Sharifpoor, Sara; van Dyk, Dewald; Costanzo, Michael et al. (2012) Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs. Genome Res 22:791-801
Davidson, Marta B; Katou, Yuki; Keszthelyi, Andrea et al. (2012) Endogenous DNA replication stress results in expansion of dNTP pools and a mutator phenotype. EMBO J 31:895-907

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