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: 2018-08-30
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2019
150 credits including 75 credits in biology, 30 credits in chemistry, 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 2019
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