We are a Computational and Systems Biology group, focusing on two major directions:
- Quantitative models for gene regulation. We are interested in understanding how biological information is encoded in DNA sequences,
and how sequence variation among human individuals generates phenotypic diversity. We focus on transcription, chromatin, and translation regulation,
and employ several high-throughout experimental methods with the aim of developing quantitative and mechanistic models for these processes.
Ultimately, these models should allow us to predict the effect that sequence variation among individuals has on gene expression and
thus on more complex phenotypes and disease
- Microbiome and Nutrition. We initiated the Personal Nutrition Project, aimed at understanding how the
enormous microbial composition of individuals affects their response to food and at
finding which dietary choices are best for each person. We are measuring the microbiome,
genetics, and glucose response to food intake of many individuals, and then using this multi-dimensional data to devise the first personalized algorithm for predicting
the glycemic response of an individual to food. This 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.
- inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nature Biotechnology, 2012.
- Manipulating nucleosome disfavoring sequences allows fine-tune regulation of gene expression in yeast. Nature Genetics, 2012.
- Genome-wide measurement of RNA secondary structure in yeast. Nature, 2010.
- From DNA sequence to transcriptional behaviour: a quantitative approach.
Nature Reviews Genetics, 2009.
- The DNA-Encoded Nucleosome Organization of a Eukaryotic Genome. Nature, 2009.
- Predicting expression patterns from regulatory sequence in Drosophila segmentation. Nature, 2008.
- The role of site accessibility in microRNA target recognition. Nature Genetics, 2007.
- A Genomic Code for Nucleosome Positioning.
Nature, 2006. The New York Times, 2006.
Ziskind bldg., Room 149