The 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 email ICM’s Academic Staff at alison.morrow@jhu.edu. 

Click here to listen to a Question and Answer session regarding the minor.

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

• CM Advising Form

  Please find the CM Advising Form here.

• What will I gain from a minor in CM?

  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.

• Will I have opportunities to specialize within CM?

  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.

• Who can declare the computational medicine minor?

  The minor is available to all Whiting School of Engineering (WSE), Krieger School of Arts and Sciences (KSAS), and School of Medicine (SOM) premedical undergraduates. Students should have sufficient mathematical and programming background and should plan to complete the minor prerequisites during their first two years of study in order to be prepared for the minor.

• What are the prerequisites for the minor?

  At the beginning of the minor, students will need to take the following courses. For a course to count towards the minor, a minimum grade of C- is required. (Note:  In general, courses graded as ‘S/U’ do not satisfy prerequisites, however exceptions will be made for courses taken during semesters in which all courses were graded S/U due to pandemic restrictions):

  1. Calculus I
  2. Calculus II
  3. At least one additional math or applied mathematics course (at least 3 credits)

  4. At least one of the following computer programming course (at least 3 credits):

     Gateway Computing (EN.500.112, 113 or 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)

     Intermediate Programming in Java (EN.601.107)

     Data Structures (EN.601.226)

  5. At least one of the following biological sciences course at the 200 level or higher (at least 3 credits):

  Biochemistry & Molecular Engineering (EN.580.221)

  Biochemistry (AS.020.305)

  Biochemistry I (AS.250.315/ AS.030.315)

  The Nervous System (AS.080.305)

  Biological Physics (AS.171.310)

  Protein Engineering and Biochemistry Lab (AS.250.253)

  Protein Biochemistry and Engineering Laboratory (AS.250.254)

  Genetics (AS.020.303)

     6. Intermediate Probability and Statistics: either a single course covering both (EN.553.311, EN.560.348, or EN.540.382) or a course devoted to each (EN.553.420 and EN.553.430).

     

• What are the core classes needed for the minor?

  1. Are completing the prerequisite courses, students must take both of the following required core courses for the minor and are usually completed junior or senior year:

  Course Num.	Course Name	Semester	Credits
  EN.580.431	Introduction to Computational Medicine: Imaging	1st half of Fall	2
  EN.580.433	Introduction to Computational Medicine: The Physiome	2nd half of Fall	2

   

   

   

  2. In addition to the courses listed above, one of the following is required:

  Course Num.	Course Name	Semester	Credits
  AS.110.445	Mathematical and Computational Foundations of Data Science	Spring	4
  EN.553.450	Computational Molecular Medicine	Spring	4
  EN.580.430	Systems Pharmacology & Personalized Medicine	Spring	4
  EN.580.447	Computational Stem Cell Biology	Spring	3
  EN.580.458	Computing the Transcriptome	Spring	3
  EN.580.488	Foundations of Computational Biology and Bioinformatics	Spring	4
  PH.140.628 (71) & PH.140.629 (01)	Data Science for Public Health I & II	Spring	4

• Am I required to attend ICM seminars?

  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 2021 semester, the seminar attendance requirement has been updated as follows:

  ICM seminars will continue to be presented remotely via Zoom this semester. CM minors will be expected to ‘attend’ the live seminars scheduled for the Fall 2021 semester via Zoom. For students in time zones where viewing the seminar live is not feasible, the Fall 2021 seminars can be viewed as recorded. For all Fall 2021 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.

• What are the elective requirements for the minor?

  Following satisfaction of the prerequisites, to complete the minor, an undergraduate must take at least 18 credits of CM courses. This includes the 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 alison.morrow@jhu.edu and will be reviewed by the CM Minor Curriculum Committee.

  Elective Courses  

  Significant Biology/Medicine Component (M)

  Course #	Department	Course Title	Instructor	Sem	Cr
  EN.520.473	ECE	Magnetic Resonance in Medicine*	Bottomley/Schar	N/A	3
  EN.530.676	MechE	Locomotion Dynamics & Control (formerly Locomotion Dynamics)	Cowan	S	3
  EN.540.432	ChemBE	Project in Design: Pharmacokinetics*	Donohue	F	3
  EN.540.421	ChemBE	Project in Design: Pharmacodynamics*	Donohue	S	3
  EN.580.420	BME	Build-A-Genome	Bader/Zeller	N/A	4
  EN.580.430	BME	Systems Pharmacology & Personalized Medicine* [If not core course]	Mac Gabhann	S	4
  EN.580.435	BME	Applied Bioelectrical Engineering	Hunter/Tung	S	1.5
  EN.580.446	BME	Physical Epigenetics	Feinberg/Ha	N/A	3
  EN.580.447	BME	Computational Stem Cell Biology*	Cahan	S	3
  EN.580.460	BME	Epigenetics at the Crossroads of Genes & the Environment*	Feinberg	S	1.5
  EN.580.462	BME	Representations of Choice*	Chib	S	3
  EN.580.464	BME	Advanced Data Science for Biomedical Engineering* (formerly Intro. to Data Science for BME)	Caffo	S	4
  EN.580.480	BME	Precision Care Medicine I*	Winslow/Sarma	F	4
  EN.580.481	BME	Precision Care Medicine II*	Winslow/Sarma	S	4
  EN.580.488	BME	Foundations of Computational Biology & Bioinformatics* [If not core course]	Karchin	S	4
  EN.580.492	BME	Build-A-Genome Mentor	Bader/Zeller	N/A	4
  EN.580.689	BME	Computational Personal Genomics*	Salzberg	N/A	3
  EN.601.350	CS	Genomic Data Science (formerly Introduction to Genomic Research)*	Salzberg	S	3
  EN.601.447	CS	Computational Genomics: Sequences*	Langmead	F	3
  EN.601.448	CS	Computational Genomics: Data Analysis*	Battle	N/A	3
  EN.601.750	CS	Frontiers of Sequencing Data Analysis*	Langmead	N/A	3
  AS.250.353	Biophysics	Computational Biology*	Fleming	N/A	3
  ME.600.721	Health Informatics (SOM via Interdivisional Registration)	Introduction to Precision Medicine Data Analytics*	Nagy & Vaidya	F	1.5

   

  Significant Computational Component (C)

  Course #	Department	Course Title	Instructor	Sem	Cr
  EN.520.353	ECE	Control Systems	Mallada Garcia	S	3
  EN.520.432	ECE	Medical Imaging Systems	Bell	F	3
  EN.520.433	ECE	Medical Imaging Analysis	Jerry Prince	S	3
  EN.520.473	ECE	Magnetic Resonance in Medicine*	Bottomley/Schar	N/A	3
  EN.540.400	ChemBE	Project in Design: Pharmacokenetics*	Donohue	F	3
  EN.540.409	ChemBE	Dynamic Modeling & Control	Staff	F	4
  EN.540.414	ChemBE	Computational Protein Structure Prediction & Design	Gray	N/A	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.553.361	AMS	Introduction to Optimization	Fishkind	F/S	4
  EN.553.386	AMS	Scientific Computing: Differential Equations	Eyink	S	4
  EN.553.436	AMS	Introduction to Data Science (formerly Data Mining)	Budavari	F	4
  EN.553.492	AMS	Mathematical Biology	Athreya	S	3
  EN.580.430	BME	Systems Pharmacology & Personalized Medicine* [If not core course]	Mac Gabhann	S	4
  EN.580.437	BME	Neuro Data Design I	Vogelstein	F	4
  EN.580.438	BME	Neuro Data Design II	Vogelstein	S	4
  EN.580.447	BME	Computational Stem Cell Biology*	Cahan	S	3
  EN.580.460	BME	Epigenetics at the Crossroads of Genes & the Environment*	Feinberg	S	1.5
  EN.580.462	BME	Representations of Choice*	Chib	S	3
  EN.580.464	BME	Advanced Data Science for Biomedical Engineering* (formerly Intro. to Data Science for BME)	Caffo	S	4
  EN.580.480	BME	Precision Care Medicine I*	Winslow/Sarma	F	4
  EN.580.481	BME	Precision Care Medicine II*	Winslow/Sarma	S	4
  EN.580.488	BME	Foundations of Computational Biology & Bioinformatics* [If not core course]	Karchin	S	3
  EN.580.491	BME	Learning, Estimation, and Control (formerly Learning Theory)	Shadmehr	S	3
  EN.580.689	BME	Computational Personal Genomics	Salzberg	N/A	3
  EN.601.323	CS	Data-Intensive Computing	Burns	N/A	3
  EN.601.350	CS	Genomic Data Science (formerly Introduction to Genomic Research)*	Salzberg	S	3
  EN.601.447	CS	Computational Genomics: Sequences*	Langmead	F	3
  EN.601.448	CS	Computational Genomics: Data Analysis*	Battle	N/A	3
  EN.601.455	CS	Computer Integrated Surgery 1	Taylor	F	4
  EN.601.461	CS	Computer Vision	Hagar	F	3
  EN.601.475	CS	Machine Learning	Graff	S	3
  EN.601.476	CS	Machine Learning: Data to Models	Saria	N/A	3
  EN.601.482	CS	Machine Learning: Deep Learning	Hager	S	3
  EN.601.723	CS	Advanced Topics in Data-Intensive Computing	Burns	F	3
  EN.601.750	CS	Frontiers of Sequencing Data Analysis*	Langmead	N/A	3
  AS.050.375 (formerly EN.601.485)	CS	Probabilistic Models of the Visual Cortex	Yuille	F	3
  AS.250.302	Biophysics	Modeling the Living Cell	M. Johnson	S	4
  AS.250.353	Biophysics	Computational Biology*	Fleming	F	3
  ME.600.721	Health Informatics (SOM via Interdivisional Registration )	Introduction to Precision Medicine Data Analytics*	Nagy & Vaidya	F	1.5
  PH.340.677	Epidemiology (BSPH)	Infectious Disease Dynamics: Theoretical and Computational Approaches	Lessler & Wesolowski	S	1.5

   

  Other Electives (M/C)*

  Course #	Department	Course Title	Instructor	Sem	Cr
  EN.520.315	ECE	Introduction to Bio-Inspired Processing of Audio-Visual Signals (formerly Intro. to Information Processing of Sensory Signals)	Hermansky	F	3
  EN.520.621	ECE	Introduction to Nonlinear Systems	Inglesias	N/A	3
  EN.530.343	MechE	Design & Analysis of Dynamical Systems	Kim	S	3
  EN.530.616 (formerly EN.520.601)	MechE	Introduction to Linear Systems Theory	Whitcomb	S	3
  EN.553.391	AMS	Dynamical Systems	Eiynik	F	4
  EN.553.420	AMS	Introduction to Probability [if not prereq.]	Wierman	S	4
  EN.553.426	AMS	Introduction to Stochastic Processes	Fill	S	4
  EN.553.430	AMS	Introduction to Statistics [if not prereq.]	Athreya	F	4
  EN.580.439	BME	Models of the Neuron	Zhang	F, S	4
  EN.530.410	ME	Biomechanics of the Cell	Sun	S	3

  
  *May be used to satisfy “M” or “C” 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 alison.morrow@jhu.edu.