Date
June 26 – 30 2017, in Wageningen,
Registration is not open!! enrollment form
Venue
Wageningen UR, FORUM building (room C0211, practicals in room PC0629)
Route & travel information
Tutors
Jaap Molenaar (WUR), Natal van Riel (TUE), Hans Stigter (WUR), Aalt-Jan van Dijk (WUR), Fianne Sips (TUE)
Study load
3 ECTS points
Description
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 are introduced via a great variety of examples taken from diverse practices. The emphasis is on providing an introduction into modelling approaches rather than an in-depth treatment of a few techniques. So, the course provides a broad overview of Modelling in the Life Sciences and is as such useful for a broad audience. The course is a mixture of theory sessions and computer practicals. During the practicals Matlab is 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 (Jaap Molenaar), introduction Matlab (Hans Stigter), overview of modelling techniques (Natal van Riel)
- Tuesday: parameter estimation (Hans Stigter), case study flowering time (Aalt-Jan van Dijk)
- Wednesday: network inference (Jaap Molenaar, Aalt-Jan van Dijk), case study nitrate control (Hans Stigter)
- Thursday: uncertainty analysis, sensitivity analysis, case study metabolism (Natal van Riel, Fianne Sips)
- Friday: case study glucose and fatty acids (Fianne Sips), chemotaxis and signaling (Jaap Molenaar), assignment preparation
Programme
Location: room C0211 (theory) and room PC6.29 (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
Target Audience
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 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.
Learning Objectives
The students will be provided with a theoretical basis, methods and computational hands-on experience on 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.
More information
Below please find a list with hotels in Wageningen. For more information about logistics you can contact Ms Dinie Verbeek (Wageningen UR, Biometris), tel. +31 (0)317 480798, email: dinie.verbeek@wur.nl
Hotel list:
Hof van Wageningen – Hotel & Congrescentrum
Lawickse Allee 9
6701 AN Wageningen
Tel: +31 (0) 317 490 133 | Fax: +31 (0) 317 426 243 | Email: info@hofvanwageningen.nl
Rijn IJssel Vakschool Wageningen
Hotel de Nieuwe Wereld
Marijkeweg 5
6709 PE Wageningen
Tel. +31 (0) 26-3129604 Afdeling reserveringen
Hotel Nol in ‘t Bosch
Hartenseweg 60
6704 PA Wageningen
Tel: +31 (0)317 319101 | Fax: +31 (0)317 313611 | Email: info@nolintbosch.nl
ReeHorst
Bennekomseweg 24
6717 LM EDE (Gld)
Telefoon +31 (0)318-750300
