The course begins with a detailed discussion of sequence alignment algorithms that are critical for assessing the relatedness of DNA, RNA and amino acid sequences. We then proceed to studying Markov chains and hidden Markov models as important examples of biological models for such sequences. The main algorithms and several applications will be explained. Finally, a broad range of examples of applications of mathematics in biology, both at the molecular and macroscopic level, will be given. These may include current research being done at the ANU. The course is accompanied by computer lab sessions where we explore, in particular, major biological databases and sequence similarity search.

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.
2. Understand and apply basic probabilistic concepts such as probability spaces, conditional probability, Markov chains, and stationary distributions.
3. Understand the main principles of mathematical modelling in biology.

Assignment 1: 25% Assignment 2: 25% Take home exam: 35% Home work: 15%