JHU offers undergraduate minor in computational medicine

The Johns Hopkins Institute for Computational Medicine (ICM), a BME affiliated institution, has launched the nation’s first undergraduate minor in the emerging field of computational medicine.

The minor course of study exposes students to the fundamentals of computational medicine — a discipline devoted to the development of quantitative approaches to understanding the mechanisms, diagnosis, and treatment of human disease.

The ICM core faculty of 19 researchers, many of whom hold primary appointments in the Department of Biomedical Engineering, will act as advisors to students. Courses will guide students through recent advances in modeling and computing technologies that have opened the door to new possibilities for identifying, analyzing, and treating diseases.

The program, which is open to any Johns Hopkins undergraduate, is expected to attract students interested in computer science, biomedical engineering, electrical and computer engineering, applied mathematics and statistics, biology, neuroscience, biophysics, and public health, as well as those interested in medical school.

While there are no formal tracks or specializations within the minor, students will be exposed to key areas that include:

• Computational Physiological Medicine: A field that develops integrative, mechanistic models of biological systems in disease and applies insights gained from these models to the development of improved diagnostics and therapies.
• Computational Molecular Medicine: A field that harnesses massive datasets from high-throughput assays, such as next-generation sequencing systems, to construct statistical models for identifying the drivers of disease.
• Computational Anatomical Medicine: A field that uses medical imaging and computational methods to analyze the variation in structure and function of human organs in health and disease to assist in the diagnosis and prognosis of complex diseases.
• Computational Healthcare: An emerging field devoted to statistical modeling and analysis of large healthcare datasets to improve patient health.