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 ICM Core Faculty who serve as advisors to the Undergraduate Minor 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 math or applied mathematics course (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):
    Gateway Computing (EN.500.112, EN.500.113 or EN.500 114)
    Computation & Programming for Materials Science & Engineering (EN.510.202)
    Scientific Computing with Python (EN.553.383)
    Scientific Computing: Linear Algebra (EN.553.385)
    Scientific Computing: Differential Equations (EN.553.386)
    Introduction to Scientific Computing for BME in Python, Matlab & R (EN.580.200)
    Intermediate Programming (EN.601.120)
    Introductory Programming in Java (EN.601.107)

       

  • The following are required core courses for the minor and are usually completed junior or senior year:

    1. Introduction to Computational Medicine: Imaging (EN.580.431) and
    2. Introduction to Computational Medicine: The Physiome (EN.580.433) and 
    3. One of the following:
      • Advanced Data Science for Biomedical Engineering (EN.580.464) 
      • Computational Molecular Medicine (EN.553.450) 
      • Computational Genomics: Data Analysis (EN.601.448) 
      • Foundations of Computational Biology and Bioinformatics II (EN.580.488)
      • Systems Pharmacology & Personalized Medicine (EN.580.430)

    EN.580.431 and EN.580.433 cover computational anatomy and physiology and will be jointly taught by ICM faculty from multiple departments.

    EN.580.464 covers introductory R, data cleaning, reproducible research, basic statistical inference, machine learning, and artificial intelligence.  EN.553.450 covers measuring associations, testing multiple hypotheses, and learning predictors, Markov chains and graphical models.  EN.601.448 covers computational analysis of genomic data with a focus on statistical methods and machine learning. EN.580.488 introduces probabilistic modeling and information theory applied to biological sequence analysis and EN.580.430 focuses on the applications of pharmacokinetics and pharmacodynamics to simulating the effects of various drugs across a heterogeneous population of diseased individuals. 

  • 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.

    For the Fall 2020 semester, the seminar attendance requirement has been updated as follows:

    ICM seminars will continue for the Fall 2020 semester but will be presented remotely via Zoom. CM minors will be expected to ‘attend’ the live seminars scheduled for the Fall 2020 semester via Zoom. For students in time zones where viewing the seminar live is not feasible, the Fall 2020 seminars can be viewed as recorded. For all Fall 2020 seminars viewed, students must complete a seminar attendance form to receive credit towards the 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 Alecia Flynn (aflynn12@jhu.edu) and will be reviewed by the CM Minor Curriculum Committee.

    Elective Courses  

    Significant Biology/Medicine Component (M)

    Course #DepartmentCourse TitleInstructorSemCr
    EN.520.473ECEMagnetic Resonance in Medicine*Bottomley/ScharN/A3
    EN.530.676MechELocomotion Dynamics & Control (formerly Locomotion Dynamics)CowanS3
    EN.540.400ChemBEProject in Design: Pharmacokinetics*DonohueF3
    EN.540.421ChemBEProject in Design: Pharmacodynamics*DonohueS3
    EN.580.420BMEBuild-A-GenomeBader/ZellerN/A4
    EN.580.430BMESystems Pharmacology & Personalized Medicine* [If not core course]Mac GabhannS4
    EN.580.435BMEApplied Bioelectrical EngineeringHunter/TungS1.5
    EN.580.446BMEPhysical EpigeneticsFeinberg/HaN/A3
    EN.580.447BMEComputational Stem Cell Biology*CahanS3
    EN.580.460BMEEpigenetics at the Crossroads of Genes & the Environment*FeinbergS1.5
    EN.580.462BMERepresentations of Choice*ChibS3
    EN.580.464BMEAdvanced Data Science for Biomedical Engineering* (formerly Intro. to Data Science for BME)CaffoS4
    EN.580.480BMEPrecision Care Medicine I*Winslow/SarmaF4
    EN.580.481BMEPrecision Care Medicine II*Winslow/SarmaS4
    EN.580.488BMEFoundations of Computational Biology & Bioinformatics* [If not core course]KarchinS4
    EN.580.492BMEBuild-A-Genome MentorBader/ZellerN/A4
    EN.580.689BMEComputational Personal Genomics*SalzbergN/A3
    EN.580.694BMEStatistical Connectomics*VogelsteinN/A3
    EN.601.350CSGenomic Data Science (formerly Introduction to Genomic Research)*SalzbergS3
    EN.601.447CSComputational Genomics: Sequences*LangmeadF3
    EN.601.448CSComputational Genomics: Data Analysis*BattleN/A3
    EN.601.750CSFrontiers of Sequencing Data Analysis*LangmeadN/A3
    AS.250.353BiophysicsComputational Biology*FlemingN/A3

     

    Significant Computational Component (C)

    Course #DepartmentCourse TitleInstructorSemCr
    EN.520.353ECEControl SystemsMallada GarciaS3
    EN.520.432ECEMedical Imaging SystemsBellF3
    EN.520.433ECEMedical Imaging AnalysisJerry PrinceS3
    EN.520.473ECEMagnetic Resonance in Medicine*Bottomley/ScharN/A3
    EN.540.400ChemBEProject in Design: Pharmacokenetics*DonohueF3
    EN.540.409ChemBEDynamic Modeling & ControlStaffF4
    EN.540.414ChemBEComputational Protein Structure Prediction & DesignGrayN/A3
    EN.540.421ChemBEProject in Design: Pharmacodynamics*DonohueS3
    EN.540.638ChemBEAdvanced Topics in Pharmacokinetics and Pharmacodynamics IDonohueF3
    EN.553.386AMSScientific Computing: Differential EquationsEyinkS4
    EN.553.436AMSIntroduction to Data Science (formerly Data Mining)BudavariF4
    EN.553.492AMSMathematical BiologyAthreyaS3
    EN.580.430BMESystems Pharmacology & Personalized Medicine* [If not core course]Mac GabhannS4
    EN.580.445BMENetworksSarmaN/A3
    EN.580.447BMEComputational Stem Cell Biology*CahanS3
    EN.580.460BMEEpigenetics at the Crossroads of Genes & the Environment*FeinbergS1.5
    EN.580.462BMERepresentations of Choice*ChibS3
    EN.580.464BMEAdvanced Data Science for Biomedical Engineering* (formerly Intro. to Data Science for BME)CaffoS4
    EN.580.468BMEThe Art of Data ScienceVogelsteinN/A3
    EN.580.480BMEPrecision Care Medicine I*Winslow/SarmaF4
    EN.580.481BMEPrecision Care Medicine II*Winslow/SarmaS4
    EN.580.488BMEFoundations of Computational Biology & Bioinformatics* [If not core course]KarchinS3
    EN.580.491BMELearning, Estimation, and Control (formerly Learning Theory)ShadmehrS3
    EN.580.689BMEComputational Personal GenomicsSalzbergN/A3
    EN.580.694BMEStatistical ConnectomicsVogelsteinN/A3
    EN.601.323CSData-Intensive ComputingBurnsN/A3
    EN.601.350CSGenomic Data Science (formerly Introduction to Genomic Research)*SalzbergS3
    EN.601.447CSComputational Genomics: Sequences*LangmeadF3
    EN.601.448CSComputational Genomics: Data Analysis*BattleN/A3
    EN.601.455CSComputer Integrated Surgery 1TaylorF4
    EN.601.461CSComputer VisionHagarF3
    EN.601.475CSMachine LearningGraffS3
    EN.601.476CSMachine Learning: Data to ModelsSariaN/A3
    EN.601.482CSMachine Learning: Deep LearningHagerS3
    EN.601.723CSAdvanced Topics in Data-Intensive ComputingBurnsF3
    EN.601.750CSFrontiers of Sequencing Data Analysis*LangmeadN/A3
    AS.050.375 (formerly EN.601.485)CSProbabilistic Models of the Visual CortexYuilleF3
    AS.250.353BiophysicsComputational Biology*FlemingF3

     

    Other Electives

    Course #DepartmentCourse TitleInstructorSemCr
    EN.520.315ECEIntroduction to Bio-Inspired Processing of Audio-Visual Signals (formerly Intro. to Information Processing of Sensory Signals)HermanskyF3
    EN.520.621ECEIntroduction to Nonlinear SystemsInglesiasN/A3
    EN.530.343MechEDesign & Analysis of Dynamical SystemsKimS3
    EN.530.616 (formerly EN.520.601)MechEIntroduction to Linear Systems TheoryWhitcombS3
    EN.553.391AMSDynamical SystemsEiynikF4
    EN.553.420AMSIntroduction to Probability [if not prereq.]WiermanS4
    EN.553.426AMSIntroduction to Stochastic ProcessesFillS4
    EN.553.430AMSIntroduction to Statistics [if not prereq.]AthreyaF4


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

    Legend: F = Fall , S = Spring, N/A = no longer offered, Cr = number of credits

    Refer to the Course Schedule and Course Catalog for availability of the courses listed above.

Specific questions regarding the minor can be directed to Alecia Flynn, Academic Coordinator for ICM.

JHU - Institute for Computational Medicine