Aviv Regev

Associate Professor of Biology at MIT and Core Member of the Broad Institute

Broad Institute
NE30-6031
7 Cambridge Center
Cambridge, MA 02142
Tel: 617-714-7021
Email: aregev@broad.mit.edu

Website:

http://www.broadinstitute.org/scientific-community/science/core-faculty-labs/regev-lab/regev-lab-home
Lab Size: Between 5 and 10

Summary

Molecular circuits are the information processing devices of cells and organisms, transforming extra- and intra-cellular signals into coherent cellular responses. Past studies to chart key circuits in mechanistic and functional detail have typically required decades of serial work to identify and connect a few components or interactions at a time. In recent years, there has been hope that genomic approaches would make it possible to reconstruct circuitry on a systems level. However, genomic studies have largely been observational and rarely involve large-scale testing of the models and subsequent refinement.

We address this challenge with a systematic computational and experimental approach, based on iterating three steps. First, we measure the circuit’s output (e.g. mRNA levels) or internal state (e.g. protein-DNA or protein modification states) along a relevant time course using genomic tools. Next, we create a computational model that explains the observed data, using algorithms we develop. We perturb every key component proposed by our model (e.g. using RNAi). Then, we repeat the process by measuring the circuit’s output or internal state following the perturbation, refining the model, and testing it again, until data and model converge.

All circuits are dynamic, and rewire in response to perturbation, at time scales from minutes to eons, as cells respond to new environmental conditions, differentiate, or evolve. We focus on a selected model system at each time scale. For short-term responses, we study the regulatory circuit of primary mouse dendritic cells (DCs) responding to pathogen components. For long-term responses, we study the differentiation of immune cells, especially T helper cells. For evolutionary changes, we study the rewiring of nutrient responses in 15 yeast species.

We develop and apply an extensive experimental and computational toolbox for each step. Experimentally, this includes novel methods to profile RNA, proteins and their interactions, nanotechnology-based delivery to primary cells, and mesoscale ‘signatures’ to monitor the effect of hundreds of perturbations. Computationally, we have pioneered sophisticated algorithms to reconstruct dynamic circuit models from time course and perturbation data, to design time course, perturbation and signature experiments, and to facilitate analysis of large-scale genomics datasets, especially RNA-Seq.

Publications

A.K. Shalek*, R. Satija*, X. Adiconis, R. S. Gertner, J. T. Gaublomme, R. Raychowdhury, S. Schwartz, N. Yosef, C. Malboeuf, D. Lu, J. T. Trombetta, D. Gennert, A. Gnirke, A. Goren, N. Hacohen, J. Z. Levin, H. Park, A. Regev (2013). Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 498: 236–240.

M. Rabani, J.Z. Levin, L. Fan, X. Adiconis, R. Raychowdhury, M. Garber, A. Gnirke, C. Nusbaum, N. Hacohen, N. Friedman, I. Amit, A. Regev (2011). Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nature Biotechnology. 29: 436-442.

N. Yosef*, A. K. Shalek*, J. T. Gaublomme*, H. Jin, Y. Lee, A. Awasthi, C. Wu, K. Karwacz, S. Xiao, M. Jorgolli, D. Gennert, R. Satija, A. Shakya, D. Y. Lu, J. J. Trombetta, M. Pillai, P. J. Ratcliffe, M. L. Coleman, M. Bix, D. Tantin, H. Park, V. K. Kuchroo, and A. Regev (2013). Dynamic regulatory network that controls Th17 cell differentiation. Nature. 496: 461–468.

I. Amit, M. Garber*, N. Chevrier*, A.P. Leite*, Y. Donner*, T. Eisenhaure, M. Guttman, J.K. Grenier, W. Li, O. Zuk, L.A. Schubert, B. Birditt, T. Shay, A. Goren, X. Zhang, Z. Smith, R. Deering, R.C. McDonald, M. Cabili, B.E. Bernstein, J.L. Rinn, A. Meissner, D.E. Root, N. Hacohen, A. Regev (2009). Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science. 326:257-263.

D.A. Thompson, S. Roy, M. Chan, M.P. Styczynsky, J. Pfiffner, C. French, A. Socha, A. Thielke, S. Napolitano, P. Muller, M. Kellis, J.H. Konieczka, I. Wapinski, A. Regev A (2013). Evolutionary principles of modular gene regulation in yeasts. eLife, 2:e00603.