The overall goal of the Community Interactions and Outreach Core will be to develop and implement a robust program so that the data generated by the DTSGC can be widely disseminated and critically analyzed by a many extramural researchers, both experimental and computational in academia as well as industry. For this we plan to have a multi-faceted approach that involves software development and release as well as educational activities both online and in person. We will develop a website that will serve as the gateway for all of the experimental and computational data generated by the DTSGC. We will use this website to provide a range of services to the community and to receive input from the community. As part of these outreach efforts we plan to develop computational and visualization tools for sharing the raw and processed data with the community at large and the LINCS Data Coordinating Center. All of the experimental and computational data will be shared on an ongoing basis with releases every month or every two months. Data release will be integrated both conceptually and temporally with web -based educational and instructional materials. We will also make available cloud computing capability so that our data can be analyzed using our tools by others with different perspectives. We anticipate three major classes of consumers for this data: biomedical researchers in academic institutions;computational researchers in both academia and industry;and drug discovery and drug action focused researchers in industry. We will develop web-based tools for data visualization and de novo analysis so that each class of researchers can fully utilize the data we generate. We will run web-based courses using Coursera for data utilization and development of signature-based research projects. We will also have series of mini-courses on our Center website. We will conduct 4-6 personalized workshops to enable academic researchers to utilize our signatures to develop research projects that can compete for individual research grant funding. We run workshops for industry researchers so that the Center can interact with them in both precompetitive and competitive space. All of these outreach activities together should enable the DTSGC to have a broad and deep impact on the pharmacology and therapeutics research community.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54HG008098-01
Application #
8915874
Study Section
Special Emphasis Panel (ZRG1-CB-D (50))
Program Officer
Ajay, Ajay
Project Start
Project End
Budget Start
2014-09-10
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
$199,546
Indirect Cost
$82,351
Name
Icahn School of Medicine at Mount Sinai
Department
Type
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029
Xiong, Yuguang; Soumillon, Magali; Wu, Jie et al. (2017) A Comparison of mRNA Sequencing with Random Primed and 3'-Directed Libraries. Sci Rep 7:14626
Shim, Jaehee V; Chun, Bryan; van Hasselt, Johan G C et al. (2017) Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics. Front Physiol 8:651
Stern, Alan D; Rahman, Adeeb H; Birtwistle, Marc R (2017) Cell size assays for mass cytometry. Cytometry A 91:14-24
Sobie, Eric A; Williams, George S B; Lederer, W J (2017) Ambiguous interactions between diastolic and SR Ca2+ in the regulation of cardiac Ca2+ release. J Gen Physiol 149:847-855
Keenan, Alexandra B; Jenkins, Sherry L; Jagodnik, Kathleen M et al. (2017) The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Syst :
Smith, Gregory R; Birtwistle, Marc R (2016) A Mechanistic Beta-Binomial Probability Model for mRNA Sequencing Data. PLoS One 11:e0157828
Birtwistle, Marc R (2015) Analytical reduction of combinatorial complexity arising from multiple protein modification sites. J R Soc Interface 12:
Klinke 2nd, David J; Birtwistle, Marc R (2015) In silico model-based inference: an emerging approach for inverse problems in engineering better medicines. Curr Opin Chem Eng 10:14-24
Gallo, James M; Birtwistle, Marc R (2015) Network pharmacodynamic models for customized cancer therapy. Wiley Interdiscip Rev Syst Biol Med 7:243-51