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Prof. Esti Yeger-Lotem

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Research Field

The Yeger-Lotem laboratory uses computational systems biology to advance our understanding of biological systems. To do so, they develop tools that integrate big data – genomics, transcriptomics and proteomics – to answer particular questions. Examples include identifying the mode-of-action of mutations, exposing drug targets, and prioritizing disease-related genes.

Molecules work by interacting with other molecules, and hundreds of thousands of such interactions among human genes and proteins have already been mapped. The Yeger-Lotem team portrays these interactions as network models that describe how genes and proteins function in living cells, and uses these models to infer what transpires when mutations, changes in expression, and other perturbations occur.

In recent years, Yeger-Lotem and colleagues have constructed weighted network models for human tissues and cell types. One of their goals is to harness these models to better understand the molecular basis of hereditary diseases. Many hereditary diseases are tissue-selective. For example, familial mutations in the BRCA1 gene increase the risk for breast and ovarian cancers but not for other cancers. By uncovering mechanisms that allow certain tissues to remain unaffected, Yeger-Lotem hopes to enhance the search for cures.

The approaches and tools that developed by the Yeger-Lotem group can also aid in drug repositioning (i.e. finding new applications for existing drugs), or in exposing potential drug targets and assessing them in silico prior to embarking on clinical trials.

Prof. Esti Yeger-Lotem

Prof. Yeger-Lotem is a computational biologist who develops integrative methods that rely on big data to understand disease mechanisms. Inspired by the observation that proteins work by interacting with other molecules, Yeger-Lotem’s research uses molecular interaction networks to model cellular processes. The models she builds provide a skeleton of information that help elucidate the impacts of mutations and the effects of drugs, and exposes previously hidden drug targets. The open web-tools that her lab has developed provide valuable output for NIBN members and industry alike by, for example, deciphering and giving meaning to large-scale sequencing and expression profiles, and by effectively prioritizing a list of potential drug targets.

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