Computational modeling is an absolute requisite to gain understanding of the biological mechanisms underlying patterns observed in experimental data. However, one could say that modeling is a craftsmanship that can only be learned via intense exercising and ‘learning by doing’. In this course we offer the participants the possibility to learn and exercise the modeling process and the optimization techniques needed to fit models to data.
In the course the students will learn:
- to understand the common ground and the differences for applications of dynamic modeling in metabolic, regulatory, signaling, 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 modeling that can be solved with optimization ·
- to apply different types of numerical optimization methods: global and local search methods: steepest descent, Levenberg-Marquardt, genetic algorithms, linear programming.
- the combination of dynamic modeling and optimization to integrate experimental data in modeling, estimate model parameters and design experiments.
- to understand how numerical optimization (linear programming) works in flux balance analysis to simulate metabolic network models.
Target audienceThe course is aimed at PhD students with a background in bioinformatics, systems biology, computer science or a related field, and life sciences. Participants from the private sector are also welcome. A working knowledge of mathematics, especially differential equations, is recommendable, but we will distribute preparation material to be studied by students missing the required background. Furthermore, at the start we offer a math refresher to help those participants who are not (yet) involved in modelling on a daily basis.
Read more and register!