Required in stage
Optional in stage
First term
Second term
Both terms
Not organised
Next year
Alternating years
External
Prerequisites
Language of instruction
Duration
Identical coursesTheoretical Modelling in Biology (B-KUL-G0G41A)
Wenseleers Tom (coordinator) | Wenseleers Tom | van den Berg Piet 
This course is taught this academic year, but not next year.Aims
The students will acquire expertise in the development of theoretical models in biological research, with emphasis on applications in ecology and evolutionary biology. The students will develop sufficient expertise to make sound decisions with respect to the modeling approach to be taken. Students will be thought how to use modelling tools such as Mathematica, so that they can develop and implement their own theoretical models. Expertise will be obtained in both numerical and analytical techniques. Through relatively simple examples, the students will acquire a feeling for the possibilities and limitations of different modeling approaches.
Previous knowledge
The students have a basic knowledge in ecology and evolutionary biology.
Is included in these courses of study
Activities
1.7 ects. Theoretical Modelling in Biology (B-KUL-G0G41a)
Content
Basic concepts
- Modelling approaches in theoretical biology: analytical models, numerical techniques, simulation
- Different kinds of analytical and numerical models: static vs. dynamic, continuous vs. discrete, optimisation vs. explicit genetic models, individual- and agent-based models
I. Case studies analytical models
- Setting up the basic question: examples from life history evolution (optimal clutch size), behavioural ecology (ESS concept) and population ecology (coexistence of two species)
- Difference and differential equation models; applications from population ecology (population growth, competitive interactions between two species)
- Game theory and inclusive fitness: evolutionarily stable strategies; applications in the areas of social evolution on the evolution of mating strategies
- Explicit genetic models; applications from evolutionary genetics and behavioural ecology
II. Case studies numerical techniques
- Individual- and agent-based models: applications from population-ecology and on self-organising behaviour
III. Beschrijvende ecologische modellen
- Populationdynamic models with interactions between more than two species
- Modelling of food webs
- Spatially explicit models
Exercises
Working out examples using Mathematica
1.3 ects. Theoretical Modelling in Biology: Exercises (B-KUL-G0G42a)
Content
Basic concepts
- Modelling approaches in theoretical biology: analytical models, numerical techniques, simulation
- Different kinds of analytical and numerical models: static vs. dynamic, continuous vs. discrete, optimisation vs. explicit genetic models, individual- and agent-based models
I. Case studies analytical models
- Setting up the basic question: examples from life history evolution (optimal clutch size), behavioural ecology (ESS concept) and population ecology (coexistence of two species)
- Difference and differential equation models; applications from population ecology (population growth, competitive interactions between two species)
- Game theory and inclusive fitness: evolutionarily stable strategies; applications in the areas of social evolution on the evolution of mating strategies
- Explicit genetic models; applications from evolutionary genetics and behavioural ecology
II. Case studies numerical techniques
- Individual- and agent-based models: applications from population-ecology and on self-organising behaviour
III. Beschrijvende ecologische modellen
- Populationdynamic models with interactions between more than two species
- Modelling of food webs
- Spatially explicit models
Exercises
Working out examples using Mathematica
Evaluation
Evaluation: Theoretical Modelling in Biology (B-KUL-G2G41a)
Explanation
Evaluation type: report
Explanation: Evaluation is based on an individual theoretical modelling project, whereby a model from the literature is validated using Mathematica and further extended by incorporating one or more additional parameters. A student passes if the score is at least 10/20.