The goal of this proposal is to elucidate the eukaryotic DNA damage response through an integrated experimental/computational approach leading to in-silico models of signaling and regulatory networks. Comparative modeling of the networks induced by different damaging agents is likely to reveal rich new insights into cellular toxicity and, ultimately, cancer progression. Experimentally, we will focus on how the yeast DNA damage control network is reprogrammed by exposure to methyl methanesulfonate (MMS; years 1-2) or methyl-N'-nitro-N-nitrosoguanidine (MNNG; years 3-4), two different types of alkylating agent. The network will be characterized using high-throughput genomic technologies including chromatin immunoprecipitation in conjunction with promoter microarrays to identify protein-DNA interactions (chlP- chip); coimmunopreciptation followed by mass spectrometry to identify protein-protein interactions; and DNA microarrays to monitor genome-wide expression profiles resulting from systematic single and double gene knockouts. Computationally, we will integrate and model these data using tools for comparison of networks across multiple conditions (PathBLAST), statistical identification of expression-activated network regions (ActiveModules), and a specialized visualization platform and database we have developed for operating on network models (Cytoscape). A systems approach will be crucial for revealing the complex web of interactions between diverse cellular damage responses ranging from base-excision repair and homologous recombination to cell-cycle arrest, apoptosis, general stress response, protein metabolism, and those yet to be discovered. ? ?

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
Project #
Application #
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Balshaw, David M
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Diego
Engineering (All Types)
Schools of Arts and Sciences
La Jolla
United States
Zip Code
Ma, Jianzhu; Yu, Michael Ku; Fong, Samson et al. (2018) Using deep learning to model the hierarchical structure and function of a cell. Nat Methods 15:290-298
Jaeger, Philipp A; Ornelas, Lilia; McElfresh, Cameron et al. (2018) Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping. Mol Cell 69:321-333.e3
Shen, John Paul; Ideker, Trey (2018) Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls. J Mol Biol 430:2900-2912
Neal, Sonya; Jaeger, Philipp A; Duttke, Sascha H et al. (2018) The Dfm1 Derlin Is Required for ERAD Retrotranslocation of Integral Membrane Proteins. Mol Cell 69:306-320.e4
Bui, Nam; Huang, Justin K; Bojorquez-Gomez, Ana et al. (2018) Disruption of NSD1 in Head and Neck Cancer Promotes Favorable Chemotherapeutic Responses Linked to Hypomethylation. Mol Cancer Ther 17:1585-1594
Shen, John Paul; Zhao, Dongxin; Sasik, Roman et al. (2017) Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat Methods 14:573-576
Moder, Martin; Velimezi, Georgia; Owusu, Michel et al. (2017) Parallel genome-wide screens identify synthetic viable interactions between the BLM helicase complex and Fanconi anemia. Nat Commun 8:1238
Kramer, Michael H; Farré, Jean-Claude; Mitra, Koyel et al. (2017) Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy. Mol Cell 65:761-774.e5
Srivas, Rohith; Shen, John Paul; Yang, Chih Cheng et al. (2016) A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy. Mol Cell 63:514-25
Hofree, Matan; Carter, Hannah; Kreisberg, Jason F et al. (2016) Challenges in identifying cancer genes by analysis of exome sequencing data. Nat Commun 7:12096

Showing the most recent 10 out of 47 publications