We are part of the Systems Biology community at the Weizmann Institute, with affiliations to the Department of Computer Science and Applied Mathematics and the Department of Biological Regulation.

Epigenetic memory in embryogenesis and cancer

How do cells establish and maintain cell-type specific gene regulatory programs even though their genome, which is encoding all genes and their regulatory elements, is 99.999% conserved during development? The field of epigenetics deals with mechanisms that allow constant genomic information to be interpreted in many different ways. Deep understanding of the epigenetic factors that define the cell’s identity is immensely important, for example when targeting diseased cells that aberrantly reprogrammed their epigenetic state, or when aiming to program stem cells toward specific functional cell types.

Our group is developing new experimental and computational approaches to understand how the dynamics of transcription factors, DNA methylation and chromosomal conformation collectively determine epigenetic landscapes and gene regulation. We are observing remarkable correlations between gene activity and repression and the underlying epigenetics, and our goal is to transform such correlations into mechanistic and causal understanding.

Two major questions in the lab are how does a dynamic epigenetic markup defines specific cell types and lineages during early embryogenesis, and how the canonical epigenetic landscape in somatic tissues deteriorate and become reprogrammed during aging and carcinogenesis. In both cases, we wish to understand the role of the epigenetic mechanism in defining or altering the cells’ functional states. We do this by using single cell analysis and perturbation of the machinery upstream epigenetic regulators, sampling embryonic tissues and tumors in-vivo, and modelling differentiation processes in-vitro.

Single cell (epi)-genomics and computational single cell genomics

Our lab is at the forefront of the development of new approaches for single cell Hi-C, single cell DNA methylation analysis and single cell RNA-seq. These methods open up numerous opportunities for  characterizing the dynamics of gene regulation and epigenetic programming/reprogramming. For example we can study heterogeneous cell populations precisely since we are not averaging molecular behaviors across different cell types, and we can model cellular proliferation and differentiation and map precisely each individual cell to its correct state within a spectrum of molecular behaviors.

Single cell genomics experiments are performed in our lab or in collaboration with others generate remarkably rich and complex datasets, and we develop and implement powerful algorithms to facilitate their in-depth analysis. We also develop approaches to model the evolution of genome regulation and cell types, using comparative single cell transcriptomics and comparative epigenomics.

Modern approaches to electronic health record analysis

We use our tools and expertise in data sciences to develop new models and algorithms for understanding medical states and medical histories from patients records. We are using a unique large scale data cohort covering millions of patients and nearly 20 years of history. Our approach is specifically geared toward integration of existing data with new genomic and post-genomic (including single cell genomics) assays for molecular profiling of disease samples. This rapidly evolving field is combining data analysis, machine learning and interdisciplinary research at the interface between medicine, computer science and biology.