Computational biology is an interdisciplinary field that develops and applies computational methods to understand biological systems and address societal challenges.

The computational biology minor is a cross-college minor that welcomes students from a diversity of majors. Students come together from disciplines in biology, math, computer science and engineering in interdisciplinary learning settings. The minor curriculum is intentionally designed to provide students overlap with their respective computational and biological expertise while challenging students to integrate core concepts and skills.

The minor teaches students to combine computational thinking and algorithms to tackle complex biological systems and topics like epidemiology, biotechnology, precision medicine & human health, genetics & genomics, environmental systems and scientific research. Students learn:

  • Computational biology core concepts and experimental techniques.
  • Representing and understanding biological data and patterns.
  • Biologically relevant skills in applied math, data science and statistics, and computing.
  • Modeling and predicting biological processes and dynamics.
  • Biological phenomena under uncertainty with probabilistic and statistical analyses.

Visit the Computational Biology Minor webpage for the most recent information.


A prerequisite is Calculus I or equivalent with a C- or better such as provided by MATH 1300/MATH 1310/APPM 1345/APPM 1350. Students must have a minimum cumulative GPA of 2.500 to declare this minor. Students interested in declaring should visit the Computational Biology Minor webpage and submit an interest form. A cumulative GPA of 2.000 or better is required in the courses that are used to satisfy the minor. Each individual course that is used to satisfy the minor must be passed with a C- or better.

Required Courses and Credits

The minor is divided into three course areas: Skills, BioElectives, and Data & Structure + Bioprocesses. The common pathways through the minor are: a computing or math background, which fulfills most Skills coursework, or a biology background, which fulfills BioElective coursework. Data & Structure + Bioprocesses course offerings emphasize the integration of Skills and BioElective core concepts. Completion of 8 courses from the three course areas is required for the minor; courses may not double count to satisfy multiple minor requirements. Relevant coursework can be petitioned. Course lists are maintained on the Computational Biology Minor webpage.

Mathematical Biology3-4
Choose one:
Calculating Biological Quantities
Introduction to Differential Equations with Linear Algebra
Ordinary Differential Equations
Data Science & Statistics3-4
Choose one:
Biological Data Science (recommended)
Introduction to Data Science with Probability and Statistics (recommended)
Applied Data Analysis
Biological Statistics
Intro to Data Science and Biostatistics
Introduction to Probability and Statistics
Psychological Science I: Statistics
Introduction to Data Science
Statistical Methods and Application I
Choose one, two-course sequence:
Python for Math and Data Science Applications
and Algorithms and Data Structures in Python
Computer Science 1: Starting Computing
and Computer Science 2: Data Structures
Aerospace Computing and Engineering Applications
and Computer Science 2: Data Structures
Introduction to Computational Thinking
and Programming for Information Science 2
Programming for Information Science 1
and Programming for Information Science 2
Choose one (from any biological area): 1
Principles of Biochemistry
Metabolic Pathways and Human Disease
Biochemistry of Gene Transmission, Expression and Regulation
Biomedical Engineering
Anatomy and Physiology for Biomedical Engineering
Ecology & Evolutionary Biology
Petition any upper-division EBIO course focused in biological knowledge and theory, e.g.:
Conservation Biology
Evolutionary Biology
Environmental Studies
Conservation Biology
Integrative Physiology
Human Anatomy
Human Physiology
Molecular & Cellular Biology
Molecular Biology
Cell Biology
Biology of the Cancer Cell
Infectious Disease
Structural Methods for Biological Macromolecules
The Brain - From Molecules to Behavior
Data & Structure + Bioprocesses9-10
Choose three (with at least one course from each list below) to explore biological data and modeling:
Data & Structure
Biological Networks
Bioinformatics and Genomics
Software Engineering for Scientists
Dynamic Models in Biology
Algorithms and Data Structures for Analyzing DNA
Computational and Mathematical Modeling of Infectious Diseases
Computational Neuroscience
Modeling in Mathematical Biology
Computational Genomics Lab
Phylogenetics and Comparative Biology
Computational Biology
Quantitative Genetics
Quantitative Optical Imaging
Introduction to Biophysics
Total Credit Hours24-30

Learning Outcomes

  • Effectively identify and communicate Computational Biology topics and applications to specialists and non-specialists.
  • Reframe and evaluate biological research questions in the context of computational theory and techniques.
  • Contextualize data and modeling problems based on biological principles and the scientific discovery process.
  • Collect and access biological data sources to study Computational Biology challenges.
  • Evaluate methods of biological data collection, validation, extension, and reproducibility.
  • Use and/or build computer-based systems, programs, and algorithms based in software design principles.
  • Model structured biological data and systems using computational techniques.
  • Predict and interpret biological phenomena under uncertainty with probabilistic and statistical analyses.