Aims

To introduce stochastic modelling of biological processes, computational methods for sequence analysis and modelling of genetic and biochemical networks.

Module 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.

Outline Of Syllabus

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.

Teaching Rationale And Relationship

Lectures and directed reading will be used to deliver the technical material. Practical computer sessions will be used to emphasise the simulation and analysis components.

Assessment Rationale And Relationship

There will be two equally-weighted components. One will assess understanding of Markov chains and implementation of HMM-based sequence analysis algorithms. The other will assess skills in the simulation and modelling of biological processes and networks.