June 26 – 30 2017, in Wageningen.
Wageningen UR, FORUM building (room P0635, practicals in room PC0602)
Route & travel information
Jaap Molenaar (WUR), Natal van Riel (TU/e), Hans Stigter (WUR), Aalt-Jan van Dijk (WUR)
3 ECTS points
In this Quantitative and Predictive Modelling course the participants learn how to describe the dynamic behaviour of biological systems and to integrate experimental data. Concepts of modelling in terms of differential equations are introduced via a great variety of case studies taken from diverse practices. The course offers a math refresher to help those participants who are not (yet) involved in modelling on a daily basis. The emphasis is on providing an introduction into modelling approaches rather than an in-depth treatment of a few techniques and aims as such at a broad audience. The course is a mixture of theory sessions and computer practicals. During the practicals most of the time Matlab will be used. Participants not acquainted with Matlab will get an introduction. The course has to be completed with assignments in the form of practical exercises as homework afterwards.
The structure of the course is as follows:
- Monday: refresh of Math tools and overview of Modelling techniques (Jaap Molenaar), introduction Matlab (Hans Stigter)
- Tuesday: Network inference, case study flowering time (Aalt-Jan van Dijk)
- Wednesday: Parameter estimation, Optimal Experimental Design, case study toxicity measurement (Hans Stigter)
- Thursday: Uncertainty analysis, Sensitivity Analysis, case study Metabolism (Natal van Riel)
- Friday: case studies network reconstruction in Tomato and Chemotaxis and Signaling (Jaap Molenaar), assignment preparation
Location: room P0635 (theory) and room PC0602 (practical sessions) in the Forum building
Daily programme schedule:
- 09:00 – 12:30 Session A: theory
- 13:30 – 15:30 Session B: practical session
- 16:00 – 17:30 Session C: theory
This course is primarily targeted at academic researchers such as PhD students and Postdocs in Life Sciences, Bioinformatics, Systems Biology or Biomedical Engineering. Participants from the private sector are also welcome.
Participants are expected to have some experience in modelling with differential equations or to have followed the Introductory courses E-course modelling and E-course calculus, and Discovering Systems Biology Principles or Applications for Systems Biology and Bioinformatics in the Medical Sciences. The course will start with a session to refresh the basic elements of modelling with differential equations.
The students will be provided with a theoretical basis, a variety of methods and a computational hands-on experience to handle differential equation modelling, parameter estimation and uncertainty analysis.
In the course the students will learn:
- How to set-up a dynamic model to represent biological networks using different interaction mechanisms
- To implement, simulate and analyse dynamic network models in different software tools
- To understand the common ground and the differences for applications in metabolic, regulatory, signalling, population and multi-scale biological processes
- To integrate experimental data in modelling, estimate model parameters and assess the accuracy of parameter estimates and model predictions.