Molecular networks are the information processing devices of cells and organisms, transforming signals into coherent cellular responses. Networks are remarkably flexible and can re-configure in an adaptive response to perturbation. This ability is apparent at all levels - from fast epigenetic changes in response to environmental signals or developmental cues, to re-organization to accommodate pathological changes in cancer, to genetic changes underlying network evolution under selection. Reconfiguration is essential to networks'function as well as to their ability to evolve new functionality. We understand little, however, about how specific genetic and epigenetic changes allow novel functions to emerge in complex networks. Genomics has recently made it possible to collect massive datasets about temporally changing systems. Among molecular systems, regulatory networks controlling gene transcription are the most accessible for systems-scale analysis. The availability of scalable, cost-effective genomics approaches to systematically perturb and measure all levels of a transcriptional response along with sophisticated computational methods offer an extraordinary opportunity to study network function. We propose to develop a novel integrated experimental and computational framework to systematically decipher how regulatory networks assume novel adaptive functions through fast epigenetic changes or slow genetic changes. We will distinguish two types of temporal processes. For linear trajectories we will study epigenetic reconfiguration following a nutritional change, and genetic reconfiguration in yeast and cancer cells under selection. For lineages we will characterize the development of novel transcriptional states in the hematopoiesis ontogeny and the evolution of regulatory networks in the Ascomycota phylogeny. The work will unify disparate problems - including how cell adapts to changing growth conditions, how cancer develops, and how species evolve - under a single theoretical and methodological framework. It will help establish a new paradigm for genomics research by moving us from a static snapshot view to a fully dynamic perspective on molecular processes.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
NIH Director’s Pioneer Award (NDPA) (DP1)
Project #
8DP1CA174427-06
Application #
8320148
Study Section
Special Emphasis Panel (ZGM1-NDPA-B (P2))
Program Officer
Li, Jerry
Project Start
2008-09-30
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2014-07-31
Support Year
6
Fiscal Year
2012
Total Cost
$839,025
Indirect Cost
$344,025
Name
Broad Institute, Inc.
Department
Type
DUNS #
623544785
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Lee, Mark N; Ye, Chun; Villani, Alexandra-ChloƩ et al. (2014) Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343:1246980
Schwartz, Schraga; Mumbach, Maxwell R; Jovanovic, Marko et al. (2014) Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5' sites. Cell Rep 8:284-96
Rabani, Michal; Raychowdhury, Raktima; Jovanovic, Marko et al. (2014) High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies. Cell 159:1698-710
Kumar, Roshan M; Cahan, Patrick; Shalek, Alex K et al. (2014) Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516:56-61
Shalek, Alex K; Satija, Rahul; Shuga, Joe et al. (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510:363-9
Bai, Aiping; Moss, Alan; Kokkotou, Efi et al. (2014) CD39 and CD161 modulate Th17 responses in Crohn's disease. J Immunol 193:3366-77
Shalek, Alex K; Satija, Rahul; Adiconis, Xian et al. (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498:236-40
Wu, Chuan; Yosef, Nir; Thalhamer, Theresa et al. (2013) Induction of pathogenic TH17 cells by inducible salt-sensing kinase SGK1. Nature 496:513-7
Gat-Viks, Irit; Chevrier, Nicolas; Wilentzik, Roni et al. (2013) Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli. Nat Biotechnol 31:342-9
Chindelevitch, Leonid; Stanley, Sarah; Hung, Deborah et al. (2012) MetaMerge: scaling up genome-scale metabolic reconstructions with application to Mycobacterium tuberculosis. Genome Biol 13:r6

Showing the most recent 10 out of 11 publications