Modelling and optimization for bioinformatics & systems biology

Course description

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 classical optimization, such as steepest descent methods, Levenberg-Marquardt approach, genetic algorithms, global and local search methods,  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 we will work with the easy-to-use package PPlane for phaseplane analysis and most of the time with Matlab. Participants not acquainted with Matlab will get the opportunity to follow an introduction module. 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.

Learning Objectives

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 apply different types of numerical optimization methods;
  • The combination of dynamic modeling and optimization to estimate model parameters and design experiments;
  • Design of experiments to minimize the uncertainty in parameter estimates.

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