This proposal seeks support for an interdisciplinary graduate training program in Computational Biology that has been built under a grant from HHMI in Phase I of the HHMI-NIBIB Interfaces Initiative. The primary goal of that support has been the careful development and evaluation of new curriculum and procedures for the program. This goal has been thoroughly met, and the program has grown to include over 40 students. It is a truly joint program between Carnegie Mellon University (CMU) and the University of Pittsburgh (Pitt), with a single point of admissions for students and equal representation of the two universities in all committees and administration. It has brought together the world-class strengths of the two universities in computer science and biomedical research, and has led to new collaborative research efforts and jointly supervised students. A guiding principle of the CMU-Pitt PhD Program in Computational Biology (CPCB) is that all students receive deep training in both computational and natural sciences, and, as appropriate for future educators and researchers in the growing field of computational biology, that they be fully versed in its principles and paradigms. The program has been highly successful at recruiting outstanding U.S. students, and current trainees in the program have already begun to make contributions to the field. An outstanding group of over 40 training faculty from diverse departments and schools at the two universities provide numerous opportunities for cutting-edge thesis projects in well-funded research groups. The program has the unqualified support of the administrations of both universities, and builds on the significant infrastructure and faculty investments that have been made in the establishment of the Department of Computational Biology at Pitt and the Lane Center for Computational Biology at CMU. A professionally-managed and thorough plan for evaluation of all aspects of the program on an ongoing basis has been created, and the evaluation efforts during Phase I have led to the identification and solution of problems encountered during that period. Support is requested for ten training slots. While the requested funds will support only a modest fraction of the training grant eligible students enrolled at a given time, it will enable outstanding students who are dedicated to interdisciplinary biological research to receive unique training that will place them among the leaders of this field.

Public Health Relevance

The funds will contribute to public health by meeting a pressing need throughout the biomedical research field for researchers capable of doing innovative work at the interface of computational and biological sciences. In addition to training the specific supported students, the program will aid in the development of novel teaching materials and practices of benefit to other training programs in computational biology.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Special Emphasis Panel (ZEB1-OSR-E (J1))
Program Officer
Baird, Richard A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pittsburgh
Schools of Medicine
United States
Zip Code
Wingert, Bentley M; Oerlemans, Rick; Camacho, Carlos J (2018) Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges. J Comput Aided Mol Des 32:287-297
Wingert, Bentley M; Camacho, Carlos J (2018) Improving small molecule virtual screening strategies for the next generation of therapeutics. Curr Opin Chem Biol 44:87-92
Thomas, Marcus; Schwartz, Russell (2018) A method for efficient Bayesian optimization of self-assembly systems from scattering data. BMC Syst Biol 12:65
Meyer, Wynn K; Jamison, Jerrica; Richter, Rebecca et al. (2018) Ancient convergent losses of Paraoxonase 1 yield potential risks for modern marine mammals. Science 361:591-594
Spagnolo, Daniel M; Al-Kofahi, Yousef; Zhu, Peihong et al. (2017) Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images. Cancer Res 77:e71-e74
Ragoza, Matthew; Hochuli, Joshua; Idrobo, Elisa et al. (2017) Protein-Ligand Scoring with Convolutional Neural Networks. J Chem Inf Model 57:942-957
Roman, Theodore; Xie, Lu; Schwartz, Russell (2017) Automated deconvolution of structured mixtures from heterogeneous tumor genomic data. PLoS Comput Biol 13:e1005815
Johnson, Gregory R; Kangas, Joshua D; Dovzhenko, Alexander et al. (2017) A method for characterizing phenotypic changes in highly variable cell populations and its application to high content screening of Arabidopsis thaliana protoplasts. Cytometry A 91:326-335
Thomas, Marcus; Schwartz, Russell (2017) Quantitative computational models of molecular self-assembly in systems biology. Phys Biol 14:035003
Gretzmeier, Christine; Eiselein, Sven; Johnson, Gregory R et al. (2017) Degradation of protein translation machinery by amino acid starvation-induced macroautophagy. Autophagy 13:1064-1075

Showing the most recent 10 out of 55 publications