In this course the participants learn how to describe the dynamic behavior of biological systems, and how to use optimization techniques to solve a variety of problems in bioinformatics and systems biology. Concepts of modelling in terms of differential equations are introduced via a great variety of case studies taken from diverse practices. This course also provides you with the main ideas underlying optimization, and to get you acquainted with the optimization techniques that are nowadays available and widely used. Examples of optimization problems in life sciences will be presented and discussed.
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 offers a math refresher to help those participants who are not (yet) involved in modelling on a daily basis. The course is completed with assignments in the form of practical exercises as homework afterwards.
The students will be provided with a theoretical basis, a variety of methods and a computational hands-on experience to handle differential equation modelling and numerical optimization.
In the course the students will learn:
- To understand the common ground and the differences for applications of dynamic modeling in metabolic, regulatory, signaling, population and multi-scale biological processes
- How to set-up a dynamic model to represent biological networks using different interaction mechanisms
- To implement, simulate and analyze dynamic network models
- To understand the wide variety of problems in bioinformatics & systems biology that can be solved with optimization
- To apply different types of numerical optimization methods
- The combination of dynamic modeling and optimization to integrate experimental data in modelling, estimate model parameters and design experiments.
PhDs and Postdocs interested in the fundamental modeling concepts and techniques of Systems Biology and Optimization. Basic knowledge of mathematics is assumed, Programming experience and knowledge of Matlab is preferable but not required.