Computational Biology

Informatics, systems analysis, and modeling

There is a wide range of computational biology research in the Biomedical Engineering Department and at OHSU generally, ranging from genomics to molecular simulation to organ-scale modeling, and beyond. Faculty bring to bear expertise in machine learning, statistics, computer science, and physical sciences. BME faculty participate in the National Cancer Institute's Genomic Data Analysis Network, consisting of the nation's top cancer research institutes that provide the first line analysis on some of the largest cohorts of cancer data on the planet. OHSU also collaborates with industry partners, such as Intel, to improve methods in cutting edge big data analysis for biological problems. Other work pursues machine-learning-based image analysis of cutting edge microscopy data generated at OHSU, physiological modeling of diabetes and blood-flow pathologies, and molecular-dynamics-based drug design.

Associated Faculty:


Young Hwan Chang
Multi-scale image analysis based on machine learning


Peter G. Jacobs
Mathematical modeling of the glucoregulatory system, drug delivery, and diagnostics

Kyle Ellrott
Large-scale integrative computing and analysis


Abhi Nellore
Large-scale analysis of publicly available genomics data


Jeremy Goecks
Computational tools and platforms for precision medicine


Sandra Rugonyi
Blood flow simulations of subject-specific cardiovascular pathologies


Laura Heiser
Integrative systems biology modeling of cancer processes


Daniel Zuckerman
Molecular through cell-scale simulations using physics-based algorithms



OHSU is also home to significant computational research outside of the BME Department. See the Computational Biology Group, the Department of Medical Informatics and Clinical Epidemiology, the Department of Computer Science & Electrical Engineering, as well as other departments which are home to computational faculty: Molecular and Medical Genetics, Radiation Medicine, and Molecular Microbiology & Immunology.