Carnegie Mellon University

Concentration in Computational Biology

Important Note: The Concentration in Computational Biology minor is open to undergraduate students in the School of Computer Science (SCS) at Carnegie Mellon University.  If you are an undergraduate outside SCS who is interested in computational biology, we encourage you to please check out our minor in computational biology or additional major in computational biology.

The general goal of the Concentration in Computational Biology (CCB) is to provide foundational coursework in computational biology that will allow undergraduate students in the Carnegie Mellon University School of Computer Science to start building a skillset useful for understanding many of the modern technologies developed by researchers as well as companies in the biotech and biomedical arenas.

CCB consists of four core courses providing breadth in computational biology across laboratory methods, machine learning, genomics, and modeling of biological systems, as well as one elective that allows students to complete depth coursework in an area of interest, including undergraduate research.

Prerequisites

Note that not all of the prerequisites below are required to take every course in CCB (for example, 02-251 does not have any of the pre-requisites below), but these courses are required to complete all of the required coursework and should be completed early within the concentration.

15-122 Principles of Imperative Computation 10 units
15-151 Mathematical Foundations for Computer Science (21-127: Concepts of Mathematics may be taken if 15-151 is not offered) 10 units
15-210 Parallel and Sequential Data Structures and Algorithms (or 15-351: Algorithms and Advanced Data Structures) 12 units
36-218 Probability Theory for Computer Scientists (or equivalent probability/statistics course) 9 units
21-241 Matrices and Linear Transformations 10 units

Further, the following two courses are not technically required as prerequisites to the CCB courses, but they are strongly suggested prerequisites because they provide students with helpful surveys of fundamental topics in biology and computational biology.

03-121 Modern Biology 9 units
02-251 Great Ideas in Computational Biology 12 units

Learning Objectives

Students will, by way of completing this concentration:

  • model biological systems at the molecular and cellular levels using a variety of approaches;
  • generate their own high throughput molecular biology data in a laboratory setting, and apply computational techniques to analyze the data they generate;
  • transform hazy biological problems involving genomic data into well-defined computational problems, design algorithms to solve these problems, and adapt them to biological data;
  • explore additional coursework of interest in genomics, biological research automation, biological image analysis, or computational biology research.

CCB also provides students completing a computational degree other than the major in computational biology with the opportunity to make a transition toward a career in computational biology.  We have compiled information on over 250 companies working on computational biology into a unique web resource for students both inside and outside of Carnegie Mellon (http://careers.cbd.cmu.edu). These companies work on diverse topics from the automation of biological research to drug discovery to wearable medical devices to genetic diagnostics. Increasingly, when we interact with these companies, they want computationally minded candidates with as much knowledge of standard approaches in computational biology as possible.

Course Requirements

Five courses in total are required for CCB. The following four courses are required as part of a central core of coursework; they consist of three computational biology courses as well as an introductory machine learning course, which today is fundamental for even an introductory understanding of the field.

02-261¹ Quantitative Cell and Molecular Biology Laboratory 9 units
10-315 Introduction to Machine Learning 12 units
02-510 Computational Genomics 9 units
02-512 Computational Methods for Biological Modeling and Simulation 9 units

In addition to these four courses, one elective course is required. Any 02-listed (Computational Biology Department) undergraduate course of at least 9 units at the 300-level or above may satisfy this requirement; graduate courses may be applied to this category with permission. The Computational Biology Department is growing quickly, but at the time of writing, the courses that are regularly offered by the department that would satisfy this requirement are the following:

02-317 Algorithms in Nature 9 units
02-319 Genomics and Epigenetics of the Brain 9 units
02-425 Computational Methods for Proteogenomics and Metabolomics 9 units
02-450 Automation of Biological Research: Robotics and Machine Learning 9 units
02-499 Independent Study in Computational Biology variable
02-500 Undergraduate Research in Computational Biology² variable
02-514 String Algorithms  12 units
02-515 Advanced Topics in Computational Genomics 9 units
02-518 Computational Medicine 12 units

¹03-343, Experimental Techniques in Molecular Biology, may be taken if 02-261 is not offered

²03-441/03-541 may be taken if 02-500 is not offered

Double Counting

CCB follows the general SCS rule that any concentration requires at least three courses (of at least 27 units) that are not double counted with any other requirements of any major, minor, or other concentration that the student is pursuing.

Accordingly, CCB is expressly closed to majors and additional majors in computational biology.

CS and AI majors completing CCB are encouraged to double-count 10-315 as well as 02-261 as their lab science course. Suggested prerequisites 03-121 and 02-251 also count as requirements for these degrees (as a science & engineering course and domains course, respectively).

Management

The day-to-day management of CCB (including exception requests, overseeing student audits, advising, etc.) will be handled by Phillip Compeau, Assistant Department Head for Education in the Computational Biology Department. Administrative support for the concentration will be provided by Samantha Mudrinich. Curricular organization and annual review will be managed by the Computational Biology Undergraduate Review Committee.