Skip to main navigationSkip to main content
The University of Southampton
Courses

COMP3212 Computational Biology

Module Overview

Modern biology poses many challenging problems for the computer scientists. Rapid growth in instrumentation, and our ability to archive and distribute vast amounts of data, has significantly changed the way we attempt to understand cellular function, and the way we seek to treat complex diseases. Data from biology comes in various forms: nucleotide and amino-acid sequences, macromolecular structures, measurements from high-throughput experiments and curated literature in the form of publications and functional annotations. It is nowadays widely acknowledged that computational modelling will play a key role in extracting useful information from vast amounts of such diverse types of data. The computational challenges faced by the human genome project and Alan Turing’s contribution to morphogenesis are classic examples of such roles.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Have a clear understanding of how advanced data analysis and computational models are applied to analysing biological data
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Fundamental assumptions that drive the use of computational techniques to understand biological data
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Data analysis, Pattern recognition

Syllabus

- Introduction - Concepts in molecular biology - Computational challenges and tools in biology - Biological Sequence Analysis - Dynamic programming and sequence alignment - Probabilistic models of alignment, hidden Markov models - Stochastic context free grammars and RNA structure modelling - Analysis of high throughput data - Transcriptomic, Proteomic and Metabolomic data - Modelling by clustering and classification; inferring regulation - Systems Biology - Autoregulation - Morphogen diffusion

Learning and Teaching

TypeHours
Wider reading or practice34
Preparation for scheduled sessions10
Lecture20
Tutorial3
Completion of assessment task53
Follow-up work10
Supervised time in studio/workshop20
Total study time150

Resources & Reading list

Alon, U. (2006). An introduction to systems biology: design principles of biological circuits. 

Durbin, R., Eddy, S.E., Krogh, A. and Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids. 

Assessment

Summative

MethodPercentage contribution
Assessed Tutorials 40%
Assignment 40%
Class Test 20%

Repeat

MethodPercentage contribution
Coursework assignment(s) 100%

Referral

MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: (MATH2047 AND ELEC2204) OR (COMP2208 AND COMP2210)

Share this module Share this on Facebook Share this on Twitter Share this on Weibo
Privacy Settings