Undergraduate Course: Topics in Mathematical Biology (MATH10083)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 10 (Year 4 Undergraduate)	Availability	Not available to visiting students
SCQF Credits	10	ECTS Credits	5
Summary	Integrative course in Mathematical Biology for joint Honours students in Mathematics and Biology, with a focus on current opinions and topics in evolutionary and systems biology, and a discussion of relevant mathematical modelling techniques and the challenges faced by mathematical biologists in their application. The course will consist of a number of individual modules which include a general introduction to research skills in mathematical biology and a "reading club" in which published research in mathematical biology is digested and critiqued. Additional modules may be delivered by lecturers from the School of Mathematics and the School of Biological Sciences, as well as by academic visitors to either School. Sample topics include an overview of current trends in both evolutionary and systems biology, as well as a more in-depth discussion of specific sub-areas such as quantitative genetics, epidemiology, and stochastic gene expression, and relevant mathematical techniques such as stochastic modelling, perturbation analysis, and statistical inference. Throughout, an emphasis is placed on student participation through discussion and presentation, and on twoway interaction with lecturing staff. Correspondingly, the course is assessed by coursework only. Lectures are intertwined with workshop sessions in which the lectured material is discussed in a collaborative studio environment. The "reading club" will be assessed by mandatory attendance and student presentations. Additionally, students will attend four talks in the Mathematical Biology seminar series, and subsequently submit a written synopsis as part of their coursework. The course is available only to students on the degree programme Mathematics and Biology.
Course description	A sample syllabus for this course is given as follows. Week 1 : Research skills in mathematical biology Week 2 : Mathematical Biology Seminar Week 3 : Trends in systems biology Week 4 : Mathematical Biology Seminar Week 5 : Trends in evolutionary biology Week 6 : Reading club Week 7 : Reading club Week 8 : Mathematical Biology Seminar Week 9 : Reading club Week 10 : Reading club Week 11 : Mathematical Biology Seminar Specific topics will vary by session, and will depend on the lecturing staff rota.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites	Students MUST have passed: Honours Differential Equations (MATH10066) AND Stochastic Modelling (MATH10007) AND Numerical Ordinary Differential Equations and Applications (MATH10060) Students MUST have passed: ( Evolutionary and Ecological Genetics 3 (BILG09004) AND Molecular Genetics 3 (BILG09002)) OR ( Molecular Genetics 3 (BILG09002) AND Molecular Cell Biology 3 (BILG09001))	Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information

Academic year 2022/23, Not available to visiting students (SS1)		Quota: None
Course Start	Semester 2
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info)	Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment)	Coursework 100%
Feedback	Not entered
No Exam Information

Learning Outcomes
On completion of this course, the student will be able to: Identify current opinions, topics, and challenges in mathematical biology, with a focus on evolutionary and systems biology. Understand and critically assess a selection of published research in mathematical biology. Present complex mathematical concepts to non-specialists in an accessible fashion. Discuss the state of the art in biological experimentation, data assimilation and processing, and the mathematical modelling of biological phenomena. Evaluate the relevance of both established and novel mathematical modelling techniques to emerging biological applications.