Eran Segal

Prof. Eran Segal was awarded a B.Sc. in Computer Science summa cum laude in 1998, from Tel-Aviv University, and a Ph.D. in Computer Science and Genetics in 2004, from Stanford University. In 2005, after an independent postdoctoral research position at Rockefeller University, New York, Prof. Segal joined the Computer Science department at the Weizmann Institute of Science. Prof. Segal leads a multi-disciplinary research group in Systems biology. The group has extensive experience in machine learning, computational biology, probabilistic models, and analysis of heterogeneous high-throughput genomic data from various technologies such as next-generation sequencing.

 

Prof. Segal received several awards for his work, including the 2007 EMBO young investigator award, and the 2007 Overton prize by the International Society for Bioinformatics (ISCB) for outstanding accomplishments in the field of computational biology. He published over 100 research articles and was recently selected as a member of the young Israeli academy of science.

 

The modern era is marked by an unprecedented increase in body weight and metabolic diseases worldwide, with more and more cases of obesity and diabetes. A major cause of these rises in body weight and disease is due to the rapid increase in blood glucose levels in the population. Since glucose levels mainly change in response to food intake, this hyperglycemia epidemic is a result of our dietary choices, but since different people have different blood glucose responses to the same food, food choices that are good for one person may not be good for another. A major source of variability across people is our microbiota, the unique collection of over 100 trillion microbes that we each carry in our gut and other body locations, and which is affected by what we eat, and in turn, affects our response to food. In this talk, I will describe the Personal Nutrition Project (www.personalnutrition.org), a study aimed at understanding individual variation and at constructing a truly personalized diet. For a large number of individuals, we are measuring their microbiome, genetics, and glucose response to food intake, and then using this multi-dimensional data to devise the first personalized algorithm for predicting the glycemic response of an individual to food. Our study may lead to the ability to administer person-specific dietary interventions that improve the glycemic response, thus providing direct treatment for the pre-diabetic stage and assisting in the worldwide battle against the obesity and diabetes epidemic.