Subject Summary: Part IB Mathematical and Computational Biology
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 5 blocks: An introductory block covering data visualisation, stochastic models, introduction to the computing environment; (2) A bioinformatics block covering sequence alignment and homology detection, DNA and RNA sequence analysis, phylogeny; (3) A foundations block covering likelihood and Bayesian methods, linear algebra, linear systems analysis; (4) A dynamics and models block covering dynamical systems modelling, temporal and spatial dynamics, neural networks and neuroscience models; (5) A data science block which will introduce principles of clustering and classification. The course will be taught using lectures (traditional and computer-based) and practical classes, and will be assessed through mini-projects, practical examinations and a written paper.
Programme Specification: Part IB Mathematical and Computational Biology
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 traditional lectures, computational lectures, supervisions and practical classes.
Assessment
Assessment for this course is through:
- one unseen written paper of three hours based on the theory components of the course;
- one unseen computational examination of three hours, focusing on implementation/ practical use of the computational methods and techniques;
- write up of a introductory mini project carried out in Michaelmas Term.
- write ups of two mini projects in the Lent Term.
Courses of Preparation
Essential: Either IA Mathematics or IA Mathematical Biology
Recommended: a mark of at least 65 is strongly recommended in either IA Mathematics, IA Mathematical Biology, or IB Mathematics (for Physical Sciences students choosing this course).
Additional Information
Further information is available on the Course Websites pages.