| Semester 2 Credit Value: | 10 |
|---|---|
To introduce stochastic modelling of biological processes, computational methods for sequence analysis and modelling of genetic and biochemical networks.
Original Summary:
Statistics is becoming increasingly important in bioinformatics as biological research moves into an era of predictive biology. This module builds on the stats skills acquired in MAS8401, introducing more advanced statistical techniques such as Hidden Markov models and stochastic modelling of biological pathways, that are used frequently used in modern bioinformatics.
Introduction to probability modelling: independence, Markov chains, hidden Markov models. Estimation: maximum likelihood, confidence intervals. Algorithms: localised and global decoding; Baum-Welch. Implementation in R.
Introduction to biological modelling; representations of biochemical networks; chemical kinetics; stochastic kinetics; stochastic simulation algorithms.
| Category | Activity | Number | Length | Student Hours | Academic Staff Contact Hours | Comment |
|---|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 21 | 1:00 | 21:00 | 21:00 | N/A |
| Scheduled Learning And Teaching Activities | Small group teaching | 10 | 1:00 | 10:00 | 10:00 | Seminars |
| Scheduled Learning And Teaching Activities | Small group teaching | 10 | 1:00 | 10:00 | 10:00 | Tutorials |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 24 | 0:00 | 0:00 | 24:00 | Office Hours: A member of staff is available in their office to answer queries on the module |
| Guided Independent Study | Independent study | 59 | 1:00 | 59:00 | 0:00 | N/A |
| Total | 100:00 | 65:00 |
Lectures and directed reading will be used to deliver the technical material. Seminar discussions will underpin this learning experience. Practical computer sessions will be used to emphasise the simulation and analysis components.
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Other | 2 | M | 100 |
There will be two equally weighted components. One will assess understanding of Markov chains and HMM-based sequence analysis and the other to assess skills in the simulation and modelling of biological processes and networks.