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. The course is accompanied by computer lab sessions to gain practice in the language and environment for statistical and mathematical computing and graphics, R

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

1. Understand basic models for the evolution and comparison of biological sequences.

2. Understand and apply basic probabilistic concepts such as conditional probability, Markov chains, 

and hidden Markov models.

3. Understand the main principles of mathematical modelling in biology.

Assessment will be based on:

• Three assignments (20% each; LO 1-4)
• Final exam (40%; LO 1-4)

36 lectures and ten tutorials

12 units of Group A courses in Mathematics, including MATH1014 or MATH1116.