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.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Bonazzi, Vivien
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University of Minnesota Twin Cities
Biostatistics & Other Math Sci
Schools of Engineering
United States
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