MPhil in Computational Biology

Brief outline of courses

Genome Informatics

Gos Micklem and Boris Adryan

Introduction to genome sequencing, assembly, annotation, visualisation. Computational approaches to sequence assembly; sequence alignment; automated gene finding; functional annotation (Gene Ontology, PFAM etc.); physical mapping and genome variation; sequence, gene annotation and gene variation databases.

Scientific Programming with R

Stephen Eglen

Interactive use of R. Basic data types. Writing scripts. Graphical facilities. Writing your own functions. String processing. File input/output. Vectorization. Numerics issues. Debugging. Introduction to Monte-Carlo methods. Reproducible research. Interfacing to databases. Advanced aspects.

Functional Genomics

Dr. B. Carvalho, Dr. O. Rueda, Dr. R. Stark

Introduction to microarrays: overview of technology, applications and the data generated. Pre-processing, normalisation and quality control for microarrays. Experimental design and planning issues. Analysis of differential expression (DE). Linear models for DE of complex designs. Analysis methods for Affymetrix and Illumina expression data. Probemannotation. Genomic profiling: analysis of aCGH, tiling array, SNP, CNV and ChIP-chip data. Dimension reduction techniques, clustering and classification methods. Meta and survival analysus. Resequencing technologies.

Computational Neuroscience

Stephen Eglen

Introduction to the nervous system: how neurons encode and decode information. Hodgkins-Huxley models of action potential propagation. Introduction to network-level models. Associative networks for long-term storage. Supervised learning methods. Reinforcement learning methods. Unsupervised learning methods. Applications of techniques to understanding visual system development.

Genome Sequence Analysis

Aylwyn Scally

This course will introduce hidden Markov models, their properties, implementation, and application to some important problems in bioinformatics and genomics. Topics: probabilistic models; Markov chains; inference on Markov chains; hidden Markov models; the forward-backward algorithm; inference with HMMs; the Viterbi algorithm; Baum-Welch training; sequence alignment; Markov models of sequence evolution; inference on trees; applications of HMMs in population genetics and evolutionary genomics.

Statistical Genetics

Simon Tavaré

The Wellcome Trust Case Control Consortium (WTCCC): a case study for case control design. Genetic epidemiology. Association studies. Transmission Disequilibrium Test (TDT). Coalescent theory. Linkage disequilibrium. Population structure. Generalized linear models. Statistical testing: false positive rate, false discovery rate, statistical power.

Systems Biology

Johan Paulsson

Detecting regulatory networks. Inverse engineering. Scale-free networks. Modelling frameworks: Boolean logic, deterministic rate equations and stochastic processes - analytically and computationally. Kinetic design principles, e.g. feedback loops, metabolic phase transitions, multi-stability, and order versus disorder. Systematic kinetic approaches, e.g. metabolic control analysis and biochemical systems theory. Biological mode systems , e.g. the lac operon, phages, plasmids and chemotaxis. Single cell and single molecule experiments. Synthetic Biology.

Network Biology

Florian Markowetz and Lorenz Wernisch

Introduction to Network Biology: Biological Networks. Topological properties. Network models from dynamic data; Mathematical models: relevance networks, graphical Gaussian models, Bayesian networks. Modelling gene regulatory networks. Metabolic networks. Further statistical tools: factor-analysis, independent component analysis, model selection criteria. Models of altered pathways in human cancer. Genotype-disease network models. Network analysis of gene perturbation screens: Perturbations and phenotypes, Exploratory analysis: enriched gene sets and sub-networks, Probabilistic graphical models and Bayesian networks, (Non-) linear Multiple-Input Multiple-Output models, Nested Effects Models, Genetic interaction networks.

Synthetic and Executable Biology (option)

Gos Micklem et al

This module aims to introduce students to the de novo design of biological systems using the techniques of Synthetic Biology. Synthetic Biology is introduced both in the context of designing exemplar biological systems to test our understanding of natural systems, and in that of systems design and fabrication to produce novel devices of commercial or medical utility. The design and simulation of biological modules using computational techniques is then introduced. The module ends with a one week project in which students design systems, and test their feasibility by computer simulation. Demonstrations of the models and a poster session are used for assessment.