We propose to establish a Biomedical Technology Research Center (BTRC) in High Performance Computing for Multiscale Modeling of Biological Systems, as a joint effort between the U of Pittsburgh (Pitt;lead institution), Carnegie Mellon U (CMU), the Pittsburgh Supercomputing Center (PSC) and Salk Institute. The Center will develop technology and tools to facilitate research and training at the interface between computing technology and life sciences, and focus on a deeper understanding ofthe molecular and cellular organization and mechanisms that underlie synaptic signaling and regulation, thus facilitating the discovery of new treatments against nervous system disorders. The goal is to start filling the gap between modeling efforts at disparate scales of structural biology, cellular microphysiology and large scale image analysis. Computational technology research and development (TR&D) studies will be conducted in (i) molecular modeling and simulations, (ii) cell modeling and simulations, and (iii) bioimage processing and analysis. These activities will emphasize developing tools to tackle the spatial and molecular complexity inherent in signal transmission and will be driven by five experimental driving biomedical projects (at Pitt, Caltech, Harvard and UT Southwestern Medical Center) on (i) neurotransmitter transport by excitatory amino acid transporters, (ii) activation of CaMKII in spines, (iii) dopamine transporter trafficking in dopamie neurons, (iv) Itk as a regulator of T cell signaling, and (iv) neuronal circuit reconstruction from serial section transmission electron microscopy images. The Center will carry out a vigorous training and dissemination program in its areas of concentration, it will leverage the capabilities of the PSC which has been home to a BTRC for more than twenty years and, besides its biomedical technology research, has a long track record of service, training and dissemination. It will also take advantage ofthe unique strengths ofthe Computational &Systems Biology Department at Pitt, and the Lane Center for Computational Biology at CMU, building on numerous successful collaborative research and training efforts between the two universities, and cutting-edge research at the Computational Neurobiology Laboratory at the Salk Institute.

Public Health Relevance

This project will develop powerful computational tools for analyzing and relating observations of neural systems and the brain at scales varying from molecular to cellular to tissue. These, will provide insights into mechanisms that distinguish normal from defective proteins, cells or organisms. These results will help accelerate the discovery of new pharmacological approaches for treating nervous system diseases associated with signaling disorders

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Biotechnology Resource Grants (P41)
Project #
5P41GM103712-03
Application #
8720022
Study Section
Special Emphasis Panel (ZRG1-BST-N (40))
Program Officer
Ravichandran, Veerasamy
Project Start
2012-09-24
Project End
2017-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
3
Fiscal Year
2014
Total Cost
$1,401,579
Indirect Cost
$150,992
Name
University of Pittsburgh
Department
Biology
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Faeder, James R; Morel, Penelope A (2016) Reductionism Is Dead: Long Live Reductionism! Systems Modeling Needs Reductionist Experiments. Biophys J 110:1681-3
Roybal, Kole T; Buck, Taráz E; Ruan, Xiongtao et al. (2016) Computational spatiotemporal analysis identifies WAVE2 and cofilin as joint regulators of costimulation-mediated T cell actin dynamics. Sci Signal 9:rs3
Jo, Sunhwan; Qi, Yifei; Im, Wonpil (2016) Preferred conformations of N-glycan core pentasaccharide in solution and in glycoproteins. Glycobiology 26:19-29
Qi, Yifei; Jo, Sunhwan; Im, Wonpil (2016) Roles of glycans in interactions between gp120 and HIV broadly neutralizing antibodies. Glycobiology 26:251-60
Liu, Bing; Liu, Qian; Yang, Lei et al. (2016) Innate immune memory and homeostasis may be conferred through crosstalk between the TLR3 and TLR7 pathways. Sci Signal 9:ra70
Suárez, Ernesto; Pratt, Adam J; Chong, Lillian T et al. (2016) Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses. Protein Sci 25:67-78
Murphy, Robert F (2016) Building cell models and simulations from microscope images. Methods 96:33-9
Li, Ying; Majarian, Timothy D; Naik, Armaghan W et al. (2016) Point process models for localization and interdependence of punctate cellular structures. Cytometry A 89:633-43
Kolouri, Soheil; Tosun, Akif B; Ozolek, John A et al. (2016) A continuous linear optimal transport approach for pattern analysis in image datasets. Pattern Recognit 51:453-462
Schiffer, Jamie M; Feher, Victoria A; Malmstrom, Robert D et al. (2016) Capturing Invisible Motions in the Transition from Ground to Rare Excited States of T4 Lysozyme L99A. Biophys J 111:1631-1640

Showing the most recent 10 out of 119 publications