Krill Prize 2014
Tel Aviv University
Dr. Irit Gat-Viks (ד”ר עירית גת-ויקס)
Research Interests: Computation Genetics
The genetic, molecular and cellular mechanisms underlying inter-individual variations in complex physiological responses remain largely unknown despite their enormous medical implications. Past quantitative genetic studies have typically connected genetic variants with physiological phenotypes, ignoring the intermediate molecular and cellular mechanisms. Recent advances in genomic technologies create extraordinary opportunities to systematically decipher these mechanisms. My ultimate goal is to decipher and characterize the regulatory mechanisms underlying variation in susceptibility to complex diseases. My research is focused on immune response variability and its influence on susceptibility to infectious diseases of bacterial, fungal or viral origin. My team investigates whether and how variability in gene expression translates genetic and environmental variations into changes in activity of immune cells and in susceptibility to disease agents. My main working hypothesis is that the regulatory mechanisms underlying disease may reveal themselves when we combine genetics with genomic information across diverse biological contexts, including various cell types, tissues, extracellular stimulations (pathogens) and time points. The main challenge comes from the computational problems involved in developing algorithms that are scalable for a large number of genetic variants, genes and contexts across a large cohort of individuals. My group addresses this challenge by developing novel sophisticated algorithms to combine multi-dimensional genomic data with genetic studies in a systematic, unbiased manner. For example, we have developed a scalable algorithm for combining genetics with genome-wide gene expression time-course data of up to several hundreds of time points29*. We have also created a computational framework for investigating cell- specific inherited variations across hundreds of immune cell types simultaneously26,32. In addition, we have devised a probabilistic algorithm for modeling patient-specific regulatory circuits based on the integration of genetic information with genomic input across a variety of pathogenic stimulations28,31. We focus on several selected infectious diseases caused by different types of microorganisms. For viruses, we are currently studying a combination of (hundreds of) cell types, tissues, time courses and genetic backgrounds, following exposure to the Influenza virus. For fungi, we are studying disease mechanisms using gene expression measurements of Aspergillus-infected spleen and liver in mice. For bacteria, we are studying inherited variations in primary bone marrow-derived immune dendritic cells following exposure to both Gram+ and Gram− bacterial components. By combining enetics with multiple- context genomics and advanced statistical algorithms, we hope to understand the critical mechanisms leading to disease susceptibility. The immediate results of our research will directly impact our understanding of Influenza and Aspergillus pathogenesis in mammals. The methodologies we establish will provide a new paradigm for studying the genetic, molecular and immune basis of susceptibility to infection or immune disorders.