Phillip Compeau, Concentration Director
Location: GHC 7403

Tara Seman, Academic Coordinator
Location: GHC 7721

Computational Biology Concentration

This concentration is available to SCS students only.

The general goal of the Computational Biology Concentration 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.

This concentration 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.

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.

This concentration 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.

Curriculum

Prerequisites

Note that not all of the prerequisites below are required to take every course in this concentration (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 this concentration.

		Units
02-120	Programming for Scientists	12
or 15-112	Fundamentals of Programming and Computer Science
15-122	Principles of Imperative Computation	12
15-151	Mathematical Foundations for Computer Science	12
15-210	Parallel and Sequential Data Structures and Algorithms	12
21-241	Matrices and Linear Transformations	11
36-218	Probability Theory for Computer Scientists	9

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

02-251	Great Ideas in Computational Biology	12
02-180 & 02-181	Great Ideas in Computational Biology I and Great Ideas in Computational Biology II	10
03-151	Honors Modern Biology	10
or 03-121	Modern Biology

Course Requirements

Five courses in total are required for this concentration. 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.

		Units
02-261	Quantitative Cell and Molecular Biology Laboratory (03-343, Experimental Techniques in Molecular Biology, may be taken if 02-261 is not offered)	Var.
02-510	Computational Genomics	12
02-512	Computational Methods for Biological Modeling and Simulation	9
10-301	Introduction to Machine Learning	12
or 07-280	Artificial Intelligence and Machine Learning I

Double Counting

At most two courses can double count with all program requirements for majors, minors and other concentrations being pursued by the student. Courses used as free electives for a major are not considered double counted.

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

CS and AI majors completing this concentration are encouraged to double-count 10-315 as well as 02-261 as their lab science course. Suggested prerequisites 03-151 and 02-251 also count as requirements for these degrees (as a Science & Engineering course and CS Domains course, respectively).

Advising and Management

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

SCS students interested in this concentration should set up an appointment with Phillip Compeau for a brief interview.