Department of Biology
Department of Biology

Computational Biology

Computational Biology Resources

Introduction

Computational biology is a field of biology broadly defined as the application of quantitative methods to biological systems. Major subfields include bioinformatics, which focuses on developing computational methods, software tools, databases to analyze and interpret large biological datasets, and mathematical biology, which emphasizes theoretical and mathematical approaches to understanding biological processes. In the era of big data, computational biology plays a critical role in uncovering patterns and mechanisms underlying living systems and generating new insights into the natural world.

A career in computational biology provides students with a powerful blend of biological domain expertise and foundational, highly transferrable data science skills that are in high demand across biotechnology, healthcare, and public health. A key distinction between computational biology and data science is that computational biologists possess domain-level expertise in biology, while data scientists may not. Nonetheless, computational biologists are competitive for both data scientist and research scientist roles in biological subfields.

Suggested Courses

To develop core competencies in computational biology, consider courses in ethics, biology, mathematics, and computing, although other fields such as data science, biomedical engineering, genomics, for example, provide many of these competencies.

If you are interested in this field, the following courses at UVA can help develop your knowledge of the field. This list should not be construed as a recommendation to take specific courses, but rather a listing of available courses relevant to this field.

Ethics

Biology

  • BIOL 3450: Biodiversity and Conservation
  • BIOL 4012: Evolution and Ecology of Infectious Diseases
  • BIOL 4018: NextGen Sequencing: MinION the Microbe Detective
  • BIOL 4020: Computational Evolutionary Biology
  • BIOL 4130: Population Ecology and Conservation Biology
  • BIOL 4200: Networks in Biology

Mathematics

  • MATH 1310 or APMA 1090: Single-variable Calculus
  • MATH 2310 or APMA 2120: Multi-variable Calculus
  • DS 3005: Mathematics for DS
  • STAT 3020: Intermediate Statistics for Biologists
  • STAT 3120: Introduction to Mathematical Statistics
  • MATH 3250 or APMA 2130: Differential equations
  • MATH 3350 or APMA 3080 or STAT 3110: Linear Algebra

Computing:

  • STAT 1601 or 1602: Introduction to Data Science
  • CS 1110, 1112, 1113, or 1120 or DS 1002 (no programming experience) or CS 1111 (programming experience): Introduction to Programming
  • DS 2003: Communicating with Data
  • CS 2100: Data Structures and Algorithms 1
  • CS 2120: Discrete Mathematics and Theory 1
  • CS 3100: Data Structures and Algorithms 2
  • CS 4102: Algorithms
  • CS 4444: Introduction to Parallel Computing
  • BME 4350: Biomedical Engineering Data Science
  • DS 4021 or CS 4774: Machine Learning

Additional resources: