The course concerns applications of mathematical, statistical and computational methods to problems in molecular biology. Relevant biological material will be explained as the course progresses, and several lectures will be given by leading biologists and medical researchers.

This course covers:

• The three standard ways of constructing the genome map (genetic mapping, physical mapping and DNA sequencing), concentrating on DNA sequencing.
• Shotgun sequencing method will be covered including fragment assembly, discussion of existing DNA technologies and an introduction to molecular biology databases.
• Search problems and sequence alignment algorithms will be considered, as well as their implementation in software such as BLAST and FAST.
• Extension of the similarity search algorithms to predicting RNA secondary structures and protein secondary structures and folds.
• Other methods to deal with predicting protein folds will be described from amongst the following:
• Molecular modelling
• Side-chain packing
• Lattice models
• Probabilistic framework
• Hidden Markov models and neural networks, including the use of these ideas in gene and exon finding
• Phylogenetic analysis and distance-based methods will be discussed.

Note: Graduate students attend joint classes with undergraduates but will be assessed separately.

On satisfying the requirements of this course, students will have the knowledge and skills to:

1. Understand basic models for the evolution of biological sequences and protein folding.
2. Understand and apply the essential methods for phylogenetic reconstruction.
3. Apply phylogenetic reconstruction software to the main databases of biological sequences.
4. Understand and apply basic probabilistic concepts such as probability spaces, conditional probability, Markov chains, and stationary distributions.

Assessment will be based on:

• Three assignments (worth 10% each; LO 1-4)
• Final examination (70%; LO 1-4)