The course is designed to develop student research skills in the area of computational biology. Students will become aware of the elements of research, including appraising the literature, developing novel approaches, assessing results and drawing conclusions that they will be able to set against the current field. The module will allow students to be original in their application of knowledge to the solution of new, research-led problems.
The course will cover topics of computational cell biology and bioinformatics. Students will be introduced to computational modelling of dynamic cellular processes and some techniques from the theory of dynamical systems used for analysing these models. Emphasis will be placed on areas of great current interest such as neural systems, cell signalling, and calcium dynamics. Students will also be introduced to some key problems in bioinformatics, the models used to formally describe these problems, and algorithmic approaches used to solve them.
Students will be expected to show how the theory of machine learning serves as framework and a foundation for the efficient organisation, analysis and retrieval of large amounts of data; and to understand how the nature of the biological problems to be "solved" relate to the computational methods used to "solve" them.
- By the end of the module the student should be familiar with principles used in modelling dynamic phenomena in cells and methods that are used to analyse computational models
- By the end of the module the student should understand basic research methods in bioinformatics
- By the end of the module the student should understand the data structure (databases) used in bioinformatics and interpret the information (especially: find genes; determine their functions), understand and be aware of current research and problems relating to the area of their research project, to be able to critically evaluate the literature and identify the most important body of work
- By the end of the module the student should be aware of the range of technologies available to computer scientists in bioinformatics
- By the end of the module the student should able to carry out data mining gene and protein expression patterns and modelling cellular interactions and processes
- By the end of the module the student should have been able to develop their key numerate, literate, IT and communication skills, write a high quality report including an appraisal of the literature, methods, results, discussion and conclusion
- The nature of the biological problems to be "solved"
- The computational methods used to "solve" them
- Basic biology
- Aims of bioinformatics
- Database searching and sequence alignment
- Population and evolutionary genetics, including phylogeny reconstruction
- Finding genes in DNA sequences
- Deriving evolutionary patterns and relationships
- Predicting interactions with other cellular components
- Data mining gene and protein expression patterns
- Modelling cellular interactions and processes
- Reaction kinetics
- Biological oscillators
- Neuroinformatics (modelling brain function)
- Calcium dynamics
- Causality and Bayesian networks
- Time series analysis