The practice of biomedical research has undergone dramatic changes in recent years, largely driven by new biotechnology for high-throughput data generation. These technologies include high-throughput methods for imaging, genetic sequencing, proteomics, structure determination, and numerous other tasks that now make it possible to finely characterize numerous aspects of living systems from the molecular to the organismal levels. These advances in biotechnology and the vast amounts of data they are producing have revolutionized biomedical research. They have also, however, created a pressing need for scientists capable of working in a field that is increasingly data-driven and dependent on advanced computational methods. In particular, modern biomedical research depends on a new breed of computationally and mathematically sophisticated researchers who can understand new biotechnologies, develop innovative mathematical models and computer algorithms needed to make sense of their data, and apply this knowledge to drive biological and medical advances. To do so, these researchers require a strong command of computational science, the biomedical applications on which they work, and the biological and physical sciences that inform them. The Carnegie Mellon University/University of Pittsburgh Ph.D. Program in Computational Biology (CPCB) was created to meet this need for training experts in computational biology. The program aims to prepare the future leaders of computational biology: research scientists with deep knowledge of computational theory, biological and physical sciences, and a growing body of specialized interdisciplinary knowledge at the intersection of these areas. To accomplish this, the program leverages the shared strengths of its two hosts institutions, collectively world leaders in computer science, engineering, and medical research with long track records of innovation in computational biology research and educational. The training program includes an innovative curriculum covering fundamentals of computational biology, broadly defined, and a large body of advanced elective coursework spanning four broad domains of computational biology research: bioimage informatics, cellular and systems modeling, computational genomics, and computational structural biology. Program students perform thesis research in any of numerous laboratories at the cutting edge of computational biology research. These primary components of coursework and thesis research are supplemented by numerous mechanisms to facilitate student success, promote professional development, encourage responsible conduct of research, and aid in recruiting and retaining underrepresented groups. The proposed program seeks to renew training support for a select subset of students in the broader CPCB graduate program. It will provide the most promising students with two years of research support, providing them added resources and flexibility to pursue the most innovative research directions and to aid in their development into future leaders of computational biology and biomedical research as a whole.
Biomedical research has become a data-intensive field that depends on researchers with sophisticated knowledge of both computational and biomedical sciences. By training a core of exceptionally talented students in these skills, the proposed work will help advance numerous directions in improving medical treatment that now critically depend on computational innovation, such as medical image analysis, personalized and genomic medicine, and modern drug design.
|Ragoza, Matthew; Hochuli, Joshua; Idrobo, Elisa et al. (2017) Protein-Ligand Scoring with Convolutional Neural Networks. J Chem Inf Model 57:942-957|
|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|
|Roman, Theodore; Xie, Lu; Schwartz, Russell (2017) Automated deconvolution of structured mixtures from heterogeneous tumor genomic data. PLoS Comput Biol 13:e1005815|
|Kleyman, Michael; Sefer, Emre; Nicola, Teodora et al. (2017) Selecting the most appropriate time points to profile in high-throughput studies. Elife 6:|
|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|
|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|
|Pirhadi, Somayeh; Sunseri, Jocelyn; Koes, David Ryan (2016) Open source molecular modeling. J Mol Graph Model 69:127-43|
|Roman, Theodore; Xie, Lu; Schwartz, Russell (2016) Medoidshift clustering applied to genomic bulk tumor data. BMC Genomics 17 Suppl 1:6|
|Harris, Leonard A; Hogg, Justin S; Tapia, José-Juan et al. (2016) BioNetGen 2.2: advances in rule-based modeling. Bioinformatics 32:3366-3368|
Showing the most recent 10 out of 51 publications