CORE 2: DRIVING BIOLOGICAL PROBLEMS Three driving biological problems will focus our biocomputation research. A macroscale DBP focused on neuroprosthetic dynamics, a mesoscale DBP focused on viral and cellular dynamics, and a molecular scale DBP focused on drug target dynamics. We use seven criteria to select our Driving Biological Problems (DBPs): Canonical: The problem should be an archetype of problems in an entire field of inquiry to guarantee broad applicability of the tools we develop. ? Collectively cover a range of scales: It is critical that activities of the center cover scales from molecular through cellular to organismal levels. ? Physics-based: The DBP should present a problem that can be addressed by representing and analyzing the geometry and physics of the biological system. ? Data rich: Biology is dominated by experimental data. These data provide the real-world constraints that drive and validate models and simulations. ? World-class, engaged experimentalists: We seek close integration and deep interactions among the biological and computational participants. ? Have important implications for disease: We ensure that our DBPs are related to disease processes or treatments to ensure that they contribute to the advancement of human health. Our past DBPs met these criteria and enabled us to develop software that is now used by a very broad community of researchers. Our next three DBPs also satisfy these criteria and are briefly reviewed below.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54GM072970-09
Application #
8534869
Study Section
Special Emphasis Panel (ZRG1-BST-K)
Project Start
Project End
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
9
Fiscal Year
2013
Total Cost
$299,115
Indirect Cost
$124,266
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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Kohlhoff, Kai J; Shukla, Diwakar; Lawrenz, Morgan et al. (2014) Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem 6:15-21
Sáez, Pablo; Peña, Estefania; Martínez, Miguel Angel et al. (2014) Computational modeling of hypertensive growth in the human carotid artery. Comput Mech 53:1183-1196
Silva, Daniel-Adriano; Weiss, Dahlia R; Pardo Avila, Fátima et al. (2014) Millisecond dynamics of RNA polymerase II translocation at atomic resolution. Proc Natl Acad Sci U S A 111:7665-70

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