Syllabus for Modelling in Biology
Modellering i biologi
A revised version of the syllabus is available.
- 5 credits
- Course code: 1BG383
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computational Science A1N
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2009-03-12
- Established by:
- Revised: 2021-10-19
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2022
150 credits including 75 credits biology, and Mathematics and Statistics, 10 credits. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Responsible department: Biology Education Centre
The aim of the course is to give students with a background in biology basic skills in building and analysing mathematical models of biological systems. On completion of the course, the student should be able to:
- outline the principles behind modelling - why mathematical models?
- perform the modelling cycle - (i) translate a biological question into a mathematical model, (ii) analyse the model and (iii) interpret the results
- choose the appropriate modelling framework for different biological questions - quantitative vs qualitative models - deterministic vs stochastic models
- analyse models formulated in terms of differential and difference equations: equilibria and their stability, basic numerical methods
- understand, analyse and apply classic models in ecology and evolution: density-dependent population growth, models of species interactions and structured population models, evolutionary models of allele frequency change and invasion analysis
- critically interpret scientific papers that are based on mathematical models
- The modelling cycle: (i) translating a biological question into a mathematical model, (ii) mathematical analysis of the model, and (iii) interpreting the mathematical results in terms of biology
- Standard models in ecology: models for the dynamics of unstructured and structured populations, models of competition and predation
- Standard models in evolution: one- and two-locus models, quantitative genetics and the breeders' equation, invasion analysis, the stochastic Wright-Fisher and Moran models for allele frequency change
- Stability analysis of linear and non-linear models in one and two variables, phase-plane analysis, elementary vector and matrix algebra, eigenvalues and eigenvectors, elementary probability theory.
Lectures, home-assignments and exercise classes.
Home-assignments and active participation during the tutorials.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
Applies from: Autumn 2022
Some titles may be available electronically through the University library.
Otto, Sarah P.;
A biologist's guide to mathematical modeling in ecology and evolution
Princeton, N.J.: Princeton University Press, cop. 2007