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PSC, Pitt, Carnegie Mellon Launch Effort to Improve Multi-scale Modeling of Biological Systems
NIH Awards $9.3 Million to Establish Biomedical Technology Research Center
The University of Pittsburgh School of Medicine, Carnegie Mellon University (CMU) and the Pittsburgh Supercomputing Center (PSC) have been awarded a five-year, $9.3 million grant from the National Institutes of Health (NIH) to establish the Biomedical Technology Research Center (BTRC) that will develop computational tools for modeling and simulating biological systems from the tissue level down to the molecular level.
By filling in the missing pieces between modeling efforts at disparate scales of structural biology, cell modeling and large-scale image analysis, this new collaborative initiative seeks to identify the molecular and cellular mechanisms that control neurotransmission and signaling events, which in turn could lead to the development of novel treatments for nervous system disorders.
“With these tools, our goal is to better understand and appreciate the impact of defective proteins and interactions at the cellular level, and their effects on the central nervous system behavior,” said Ivet Bahar, Ph.D., professor and John K. Vries Chair of the Department of Computational and Systems Biology at the Pitt School of Medicine. “We hope to bridge the gaps between molecular-, cellular- and tissue-level information to build integrated models of cell signaling and regulation.”
Bahar is the principal investigator for the award, titled “High Performance Computing for Multi-scale Modeling of Biological Systems,” from the NIH’s National Institute of General Medical Sciences. Robert F. Murphy, Ph.D., director of the Lane Center for Computational Biology in Carnegie Mellon’s School of Computer Science, will lead CMU’s participation. The Pittsburgh Supercomputing Center’s long-established National Resource for Biomedical Supercomputing (NRBSC), headed by Markus Dittrich, Ph.D., was seminal to and is the third major partner in the new BTRC. The collaboration also includes the Salk Institute for Biological Studies in La Jolla, Calif.
“We have imagined this new center as a Pittsburgh center, joining the two universities, the University of Pittsburgh and Carnegie Mellon, with PSC strengths in training and biomedical supercomputing,” said Dr. Murphy, the Ray and Stephanie Lane Professor of Computational Biology and professor of biological sciences, biomedical engineering and machine learning. “We now have an opportunity to combine that work with work in the Lane Center on image-derived modeling of cellular organization and machine learning for structural biology to go beyond what we’ve done before.”
Dr. Dittrich said the collaboration opens many opportunities for his National Resource for Biomedical Supercomputing (NRBSC). “As core members in the new BTRC we continue our work in cellular modeling, structural biology, and large-scale volumetric image analysis and welcome the synergy of working with the outstanding computational biology programs at the University of Pittsburgh and Carnegie Mellon,” he said.
As part of the NIH grant, a supplementary award of $1.1 million provides two years of additional support for the Anton supercomputer, which the NRBSC has made available to U.S. biomedical scientists since 2010. The special-purpose computing system from D.E. Shaw Research has achieved exceptional results in the simulation of proteins and other biomolecules.
Dr. Bahar’s team will tailor computational models for five biomedical research projects including neurotransmitter signaling, immune cell regulation and neuronal circuit reconstruction that are under way at Pitt, Caltech, Allen Brain Institute (Seattle), and UT Southwestern Medical Center.
“Until now, experimental scientists have been collecting data that are not testable by computational methods, while the computational scientists have been building models and making predictions that can’t be verified experimentally,” Dr. Bahar noted. “We aim to bridge this communication gap, too, so that we can solve relevant problems computationally while generating new hypotheses that can be tested in the lab.”