Undergraduate Minor

Picture1The Institute for Computational Medicine is proud to offer an Undergraduate Minor in Computational Medicine, the first educational program in CM, reflecting Johns Hopkins University’s leadership in this field. Like the ICM itself, the Undergraduate Minor in Computational Medicine is integrative and multidisciplinary. The 17 ICM Core Faculty who serve as advisors to the Undergraduate Concentration in Computational Medicine hold primary and joint appointments in multiple Johns Hopkins University departments and schools including Biomedical Engineering, Computer Science, Electrical and Computer Engineering, Mechanical Engineering, Applied Mathematics and Statistics (WSE); Neurosurgery, Emergency Medicine, Medicine, and the Divisions of Cardiology and Health Sciences Informatics (SOM); and Health Policy and Management (BSPH).

Undergraduates who are interested in learning about the Minor in Computational Medicine are encouraged to attend the ICM’s annual Computational Medicine Night.

Undergraduates who would like to begin the minor declaration process should complete the CM Minor Prospective Students Form.

Click here to view a PDF of a sample curricula by major.

  • With a minor in CM, students will have a solid grounding in the development and application of computational methods in multiple key areas of medicine. Specifically, they will understand how mathematical models can be constructed from biophysical laws or experimental data, and how predictions from these models facilitate diagnosis and treatment of a disease. Graduating students will be conversant with a wide variety of statistical, deterministic and stochastic modeling methods. They will be able to develop a model and to write code to implement it; they will be able to analyze and visualize the resulting data from the simulations. These skills are essential to the advancement of modern medicine, and are prized both in academic research and industrial research. The courses and research opportunities available in the CM minor will place students at the forefront of the application of mathematics, computing and engineering to human health. Whether you go on to medical school, graduate research, or biomedical industries, the comprehensive quantitative training and exposure to cutting edge CM techniques will give you a competitive advantage for working in the medicine of tomorrow – which will be data-driven, predictive, personalized and preventative.

  • Yes. The minor will provide both foundational training and opportunities for specialization in Computational Medicine. Students can select electives from an approved list that match their interests. We also provide examples of curricula in some key subareas of Computational Medicine, including:

    Computational Physiological Medicine develops mechanistic models of biological systems in disease, and applies the insights gained from these models to develop improved diagnostics and therapies. Therapies could be diverse drugs, electrical stimulation, mechanical support devices and more.

    Computational Molecular Medicine harnesses the enormous amount of disease-relevant data produced by next-generation sequencing, microarray and proteomic experiments of large patient cohorts, using statistical models to identify the drivers of disease and the susceptible links in disease networks.

    Computational Anatomical Medicine uses medical imaging to analyze the variation in structure of human organs in health and disease. Such image analysis has been integrated into clinical workflows to assist in the diagnosis and prognosis of complex diseases.

    Computational Healthcare is an emerging field devoted to understanding populations of patients and their interaction with all aspects of the healthcare process.

    Techniques for and applications in each of these four key subareas will be introduced in the required core courses, so that students will be exposed to the breadth of Computational Medicine, and will be able to identify preferred areas of interest.

  • Before attempting the minor, undergraduates will have taken the following courses. For a course to count towards the minor, a minimum grade of C- is required (courses graded as ‘S/U’ do not satisfy prerequisites):

    1. Calculus I
    2. Calculus II
    3. Probability and Statistics: either a single course covering both (e.g. 553.310 or 553.311), or a course devoted to each (e.g. 553.420 and 553.430) – this may be taken concurrent with Introduction to Computational Medicine (see below).
    4. At least one additional course math or applied mathematics (at least 3 credits)
    5. At least one biological sciences course at the 200 level or higher (at least 3 credits)
    6. At least one of the following computer programming course (at least 3 credits):

     

    Introduction to Scientific Computing for BME in Python, Matlab, and R (EN.580.200)

    Intermediate Programming (EN.601.120)

    Introduction to Programming in Java (EN.601.107)

     

  • The required core courses for the minor are Introduction to Computational Medicine I (EN.580.431) and one of the following:

    • Computational Molecular Medicine (EN.553.450) or
    • Foundations of Computational Biology and Bioinformatics II (EN.580.488).

    EN.580.431 covers computational anatomy and physiology and will be jointly taught by ICM faculty from multiple departments.

    EN.553.450 focuses on molecular medicine and computational healthcare while EN.580.488 introduces probabilistic modeling and information theory applied to biological sequence analysis. Both courses require background in probability theory and statistics.

    Core courses may not be taken concurrently.

  • Distinguished Seminar Series

    In addition to the elective requirements, students with a declared Computational Medicine minor are REQUIRED to attend no less than 6 ICM Distinguished Seminars in person prior to graduation. Documentation of seminar attendance is two-fold: (1) Students must sign-in at every seminar attended and (2) students must complete the online Seminar Attendance Form. Please note that undergraduates do not need to register for the Distinguished Seminar Series in Computational Medicine course (EN.580.736/7) but do need to attend six ICM seminars and document their attendance to graduate with a Computational Medicine minor.

    More information on seminar speakers, dates, and topics can be found here.

  • Following satisfaction of the prerequisites, to complete the minor, an undergraduate must take at least 18 credits of CM courses. This includes two one-semester core courses plus approved elective courses selected from those listed below. The following restrictions apply to elective courses:

    1. No more than 3 of the 18 elective credits can consist of independent research in computational medicine or approved CM-related research. The Senior Design Project Course (EN.580.580/581) may count toward independent research, provided that the research falls within the field of computational medicine, as decided by the advisor. Eligibility of independent research as “M”, “C”, “MC”, or neither is at the advisor’s discretion.
    2. All 18 credits will all be at 300-level or above.
    3. At least 1 non-core course must be outside the student’s home department
    4. At least 2 non-core courses must have a substantial biology or medicine component, as identified in the list below with an “M” designation.
    5. At least 1 non-core course must have a significant component of “applied programming” (distinct from a course on computer language or on programming such as Intermediate Computer Programming in Computer Science) to satisfy the computational component, as identified in the list of electives with an “C” designation.
    6. All courses must be passed at a C- level or above.
    7. A class may not be counted as both a prerequisite and an elective.
    8. Students may suggest elective courses to be added to the list by completing the “Class Approval Request Form”. Requests should be made to Dr. Joshua Vogelstein and will be reviewed by the CM Minor Curriculum Committee.

    Elective Courses  

    Note: This list is not comprehensive. Contact Dr. Joshua Vogelstein with suggested additions.

    Significant Biology/Medicine Component (M)

    Course # Department Course Title  Instructor Sem Cr
    EN.520.432 ECE Medical Imaging Systems Prince F 3
    EN.520.473 ECE Magnetic Resonance in Medicine* Bottomley/Schar S 3
    EN.530.676 MechE Locomotion in Mech. & Bio. Systems Cowan S 3
    EN.540.400 ChemBE Project in Design: Pharmacokinetics* Donohue F 3
    EN.540.421 ChemBE Project in Design: Pharmacodynamics* Donohue S 3
    EN.580.430 BME Systems Pharmacology & Personalized Medicine* Mac Gabhann S 3
    EN.580.460 BME Theory of Cancer* Popel S 3
    EN.580.462 BME Representations of Choice* Chib S 3
    EN.580.480 BME Precision Care Medicine* Winslow/Sarma F/S 3
    EN.580.488 BME Foundations of Computational Biology & Bioinformatics II* Karchin S 3
    EN.580.626 BME Computational Models of the Cardiac Myocyte* Winslow S 3
    EN.580.689 BME Computational Personal Genomics* Salzberg S 3
    EN.580.694 BME Statistical Connectomics* Vogelstein S 3
    EN.580.446 BME Physical Epigenetics Feinberg/Ha S 3
    EN.580.420 BME Build-A-Genome Bader/Zeller F 4
    EN.580.492 BME Build-A-Genome Mentor Bader/Zeller F 4
    EN.601.448 CS Computational Genomics: Data Analysis* Battle S 3
    EN.601.447 CS Computational Genomics: Sequences* Langmead F 3
    EN.601.350 CS Introduction to Genomic Research* Salzberg S 3
    EN.601.750 CS Frontiers of Sequencing Data Analysis* Langmead S 3
    AS.250.353 Biophysics Computational Biology* Fleming F 3
    Significant Computational Component (C)
    Course # Department Course Title Instructor Sem Cr
    EN.540.409 ChemBE Dynamic Modeling & Control Goffin F 4
    EN.520.473 ECE Magnetic Resonance in Medicine* Bottomley/Schar S 3
    EN.540.400 ChemBE Project in Design: Pharmacokinetics* Donohue F 3
    EN.540.421 ChemBE Project in Design: Pharmacodynamics* Donohue S 3
    EN.540.638 ChemBE Advanced Topics in Pharmacokinetics and Pharmacodynamics I Donohue F 3
    EN.553.386 AMS Scientific Computing: Differential Equations Eyink S 4
    EN.553.492 AMS Mathematical Biology Athreya S 3
    EN.553.436 AMS Data Mining Budavari F 4
    EN.580.445 BME Networks Sarma F 3
    EN.580.468 BME The Art of Data Science Vogelstein S 3
    EN.580.480 BME Precision Care Medicine* Winslow/Sarma F/S 3
    EN.580.491 BME Learning Theory Shadmehr S 3
    EN.580.430 BME Systems Pharmacology & Personalized Medicine* Mac Gabhann S 3
    EN.580.460 BME Theory of Cancer* Popel S 3
    EN.580.462 BME Representations of Choice* Chib S 3
    EN.580.488 BME Foundations of Computational Biology & Bioinformatics II* Karchin S 3
    EN.580.682 BME Computational Models of the Cardiac Myocyte* Winslow S 3
    EN.580.689 BME Computational Personal Genomics* Salzberg S 3
    EN.580.694 BME Statistical Connectomics* Vogelstein S 3
    EN.601.448 CS Computational Genomics: Data Analysis* Battle S 3
    EN.601.447 CS Computational Genomics: Sequences* Langmead F 3
    EN.601.350 CS Introduction to Genomic Research* Salzberg S 3
    EN.601.750 CS Frontiers of Sequencing Data Analysis* Langmead S 3
    EN.601.323 CS Data-Intensive Computing Burns F 3
    EN.601.445 CS Computer Integrated Surgery 1 Taylor F 4
    EN.601.461 CS Computer Vision Reiter F 3
    EN.601.475 CS Machine Learning Staff S 3
    EN.601.476 CS Machine Learning: Data to Models Saria S 3
    EN.601.482 CS Machine Learning: Deep Learning Hager S 3
    EN.601.485 CS Probabilistic Models of the Visual Cortex Yuille F 3
    EN.601.723 CS Advanced Topics in Data-Intensive Computing Burns F 3
    AS.250.353 Biophysics Computational Biology* Fleming F 3
    Other Electives
    Course # Department Course Title Instructor Sem Cr
    EN.520.315 ECE Introduction to Information Processing of Sensory Signals Hermansky F 3
    EN.520.601 ECE Introduction to Linear Systems Theory Inglesias S 3
    EN.520.621 ECE Introduction to Nonlinear Systems Inglesias S 3
    EN.530.343 MechE Design & Analysis of Dynamical Systems Cowan S 3
    EN.553.391 AMS Dynamical Systems Athavale F 4
    EN.553.420 AMS Introduction to Probability [if not prereq.] Torcaso S 4
    EN.553.426 AMS Introduction to Stochastic Processes Wierman S 4
    EN.553.430 AMS Introduction to Statistics [if not prereq.] Athreya/Naiman F 4

    *May be used to satisfy “C” or “M” requirement but not both.

    Legend: F = Fall / S = Spring, Cr = number of credits

Specific questions regarding the minor can be directed to Dr. Joshua Vogelstein, Director of Undergraduate Studies for the CM minor.

JHU - Institute for Computational Medicine