Subject Summary
This course aims to provide an applied and rigorous IB course focused on practical use of statistics and computing in modern biology, underpinned by mathematics and mathematical modelling. The course is not tied to any one single application area, but instead furnishes students with a comprehensive suite of quantitative and computational skills that will be useful at Part II, Part III and beyond (in paid employment as well as in research). Students will develop a strong background in modelling, statistics, fitting models to data, algorithms, simulation, bioinformatics, “big data” and computer programming. The students will learn Python programming from scratch.
The course will be divided into 4 blocks: (1) A foundations block covering likelihood and Bayesian methods, linear algebra, linear systems analysis and an introduction to the Python computing environment; (2) A bioinformatics block covering sequence alignment and homology detection, DNA and RNA sequence analysis, phylogeny; (3) A dynamics and models block covering dynamical systems modelling, temporal and spatial dynamics, neural networks and neuroscience models; (4) A data science block which will introduce principles of clustering and classification. The course will be taught using lectures and practical classes, and will be assessed through three mini-projects and two written papers.
Programme Specification
The Mathematical and Computational Biology course is taught by the Departments of Genetics, PDN, Pathology, Zoology, Plant Sciences, Veterinary Medicine, Bioinformatics, and Psychology.
Aims
Students will develop a strong background in modelling, statistics, fitting models to data, algorithms, simulation, bioinformatics, “big data” and computer programming. The M&CB Course will equip students with a comprehensive suite of quantitative and computational skills that will be useful at Part II, Part III and beyond.
Learning outcomes
At the end of the course students should:
- have knowledge and understanding of a range of advanced mathematical techniques and their application to biological systems;
- have an understanding of the fundamental concepts behind some mathematical techniques which can be used to understand biological systems;
- be able to implement and use common bioinformatics algorithms;
- be able to develop and analyse mathematical models;
- be able to use computer and numerical methods related to the course material;
- be able to develop their own computer programs in Python.
Teaching and learning methods
These include lectures, supervisions and practical classes.
Assessment
Assessment for this course is through:
- two unseen written papers based on the theory components of the course;
- write up of a mini project carried out in Michaelmas Term;
- write ups of two additioanl mini projects in the Lent Term (submitted at the start of Easter Term).
Courses of Preparation
Essential: Either IA Mathematics or IA Mathematical Biology.
Recommended: a mark of at least 55 is strongly recommended in either IA Mathematics or IA Mathematical Biology.
Additional Information
Further information is available on the Course Websites pages.