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
Module Aims
The aim of this module is to develop an understanding of some of the computational challenges that form the basis of research in modern biology, skills associated with which are seen as important in biomedical informatics and pharmaceutical industries. You will get hands-on experience in formulating computational problems and analysing large and complex datasets to make model-based predictions about the underlying biological problems.
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
| Type | Hours |
|---|---|
| Supervised time in studio/workshop | 20 |
| Completion of assessment task | 53 |
| Wider reading or practice | 34 |
| Follow-up work | 10 |
| Tutorial | 3 |
| Lecture | 20 |
| Preparation for scheduled sessions | 10 |
| Total study time | 150 |
Resources & Reading list
Durbin, R., Eddy, S.E., Krogh, A. and Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids.
Alon, U. (2006). An introduction to systems biology: design principles of biological circuits.
Assessment
Summative
| Method | Percentage contribution |
|---|---|
| Assessed Tutorials | 40% |
| Assignment | 40% |
| Class Test | 20% |
Repeat
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Referral
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat Information
Repeat type: Internal & External
Linked modules
Pre-requisites: (MATH2047 AND ELEC2204) OR (COMP2208 AND COMP2210)