Program Overview

Computational Biology (BIOL0029)

Key Information

• Faculty: Faculty of Life Sciences
• Teaching department: Division of Biosciences
• Credit value: 15
• Restrictions: Priority will be given to students for whom the module is compulsory, followed by other Biological Sciences and associated degrees. Natural Sciences students who have taken BIOL0006 or have equivalent knowledge of R will be considered if numbers allow.

Alternative Credit Options

There are no alternative credit options available for this module.

Description

'Computational Biology' introduces students to advanced statistics applied to the biological sciences. It builds on first-year modules (mainly Quantitative Biology and Methods in Ecology and Evolution) and introduces more advanced linear and generalized linear models, as well as approaches to model building and comparison. It also covers applications of linear models to large-scale genomic data, programming, permutation-based tests, power analysis, and multivariate statistics such as Principal Components Analysis.

In addition to providing the theoretical background of the approaches covered, the module puts much emphasis on practical implementation. Lectures are accompanied by weekly practical sessions in which students will work through analyses in the statistical software R, the standard in many areas of biology.

The module is assessed via two online quizzes (each worth 30% of the overall mark) and a Data Interpretation Project (a homework assignment, 40% of the overall mark). The quizzes focus on theoretical knowledge and the use of R. The Data Interpretation Project gives students the opportunity to independently analyze a larger dataset, interpret the results, and write up their methods and findings in publication format.

Overall, this module will provide students with analytical skills that are essential for research across disciplines of biology and other branches of science and professional activities.

Indicative Lecture Topics and Learning Objectives

• Basic programming in R
• Introduction to probability
• Regression and linear models
• Analysis of Variance
• Multiple Regression and model diagnostics
• Model comparison
• Logistic regression, Generalized Linear Models
• Statistical power and simulation
• Matrices

Module Deliveries for 2026/27 Academic Year

• Intended teaching term: Term 1
• Undergraduate (FHEQ Level 5)

Teaching and Assessment

• Mode of study: In person
• Methods of assessment:
  • 40% Coursework
  • 60% Exam (2 assessments)
• Mark scheme: Numeric Marks

Other Information

• Number of students on module in previous year: 77
• Module leader: Professor Max Reuter

Last Updated

This module description was last updated on 10th March 2026.