- Code MATH2307
- Unit Value 6 units
All activities that form part of this course will be delivered remotely in Sem 2 2020.
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
Upon successful completion, students will have the knowledge and skills to:
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)
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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WorkloadThree lectures per week and regular workshops.
Requisite and Incompatibility
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- Student Contribution Band:
- Unit value:
- 6 units
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