Computational Medicine Focus Area Curriculum Requirements












Computational Medicine (CM) is an emerging discipline devoted to the development of quantitative approaches for understanding the mechanisms, diagnosis and treatment of human disease through applications of mathematics, engineering and computational science. The core approach of CM is to develop computational models of the molecular biology, physiology, and anatomy of disease, and apply these models to improve patient care. CM approaches can provide insight into and across many areas of biology, including genetics, genomics, molecular networks, cellular and tissue physiology, organ systems, and whole body pharmacology. At the Institute for Computational Medicine (ICM), the “birthplace” of CM, research focuses on four key areas: Computational Molecular Medicine, Computational Physiological Medicine, Computational Anatomy, and Computational Healthcare.

The BME MSE Focus Area in Computational Medicine is a course-based degree program comprising two BME core competency courses, three CM focus area courses, three CM focus area electives, and at least two electives outside the program. Students are also required to complete the Distinguished Seminar Series in Computational Medicine. Required CM courses and focus area electives are taught by core faculty of the Institute for Computational Medicine.

Students in the Computational Medicine focus must complete two of the following courses:
  • Introduction to Computational Medicine (EN.580.631)
  • Choose one:
    • Foundations of Computational Biology & Bioinformatics (EN.580.688)
    • Introduction to Data Science for Biomedical Engineering (EN.580.664)
    • Systems Pharmacology and Personalized Medicine (EN.580.640)
CM students will complete additional electives selected from the following list (choose at least three of these courses):
  • Advanced Topics in Genomic Data Analysis (EN.601.751)
  • Advanced Topics in Pharmacokinetics and Pharmacodynamics (EN.540.639)
  • Computational Genomics: Data Analysis (EN.601.648)
  • Computational Genomics: Sequences (EN.601.647)
  • Computational Stem Cell Biology (EN.580.647)
  • Data Mining (EN.553.636)
  • Introduction to Data Science for Biomedical Engineers (EN.580.664)
  • Introduction to Probability (EN.553.620)
  • Introduction to Stochastic Processes (EN.553.426/626)
  • Machine Learning (EN.601.675)
  • Machine Learning: Data to Models (EN.601.676)
  • Machine Learning: Deep Learning (EN.601.682)
  • Mathematics of Deep Learning (EN.580.745)
  • Models of the Neuron (EN.580.639)
  • Modeling, Simulation, and Monte Carlo (EN.553.664)
  • Neuro Data Design (EN.580.697/698)
  • Practical Ethics for Future Leaders (EN 580.496)
  • Precision Care Design I (EN.580.670)
  • Precision Care Design II (EN.580.671)
  • Principles of Complex Networked Systems (EN.520.622)
  • Systems Pharmacology & Personalized Medicine (EN.580.640)
Students will select additional graduate level science, technology, engineering, or math courses with the consent of their advisor to complete the total of 30 credits required for graduation.