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)
Project #
Application #
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Bonazzi, Vivien
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Minnesota Twin Cities
Biostatistics & Other Math Sci
Schools of Engineering
United States
Zip Code
Usaj, Matej; Tan, Yizhao; Wang, Wen et al. (2017) A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network. G3 (Bethesda) 7:1539-1549
Morales, Eduardo H; Pinto, Camilo A; Luraschi, Roberto et al. (2017) Accumulation of heme biosynthetic intermediates contributes to the antibacterial action of the metalloid tellurite. Nat Commun 8:15320
Wyche, Thomas P; Alvarenga, René F Ramos; Piotrowski, Jeff S et al. (2017) Chemical Genomics, Structure Elucidation, and in Vivo Studies of the Marine-Derived Anticlostridial Ecteinamycin. ACS Chem Biol 12:2287-2295
Davison, Jack R; Lohith, Katheryn M; Wang, Xiaoning et al. (2017) A New Natural Product Analog of Blasticidin S Reveals Cellular Uptake Facilitated by the NorA Multidrug Transporter. Antimicrob Agents Chemother 61:
van Leeuwen, Jolanda; Pons, Carles; Mellor, Joseph C et al. (2016) Exploring genetic suppression interactions on a global scale. Science 354:
Dickinson, Quinn; Bottoms, Scott; Hinchman, Li et al. (2016) Mechanism of imidazolium ionic liquids toxicity in Saccharomyces cerevisiae and rational engineering of a tolerant, xylose-fermenting strain. Microb Cell Fact 15:17
Styles, Erin B; Founk, Karen J; Zamparo, Lee A et al. (2016) Exploring Quantitative Yeast Phenomics with Single-Cell Analysis of DNA Damage Foci. Cell Syst 3:264-277.e10
Costanzo, Michael; VanderSluis, Benjamin; Koch, Elizabeth N et al. (2016) A global genetic interaction network maps a wiring diagram of cellular function. Science 353:
Becker, Jordan R; Pons, Carles; Nguyen, Hai Dang et al. (2015) Genetic Interactions Implicating Postreplicative Repair in Okazaki Fragment Processing. PLoS Genet 11:e1005659
Piotrowski, Jeff S; Okada, Hiroki; Lu, Fachuang et al. (2015) Plant-derived antifungal agent poacic acid targets ?-1,3-glucan. Proc Natl Acad Sci U S A 112:E1490-7

Showing the most recent 10 out of 35 publications