Abstract:

Modeling of biological systems is a highly interdisciplinary and
iterative task that requires researchers from different disciplines to
collaborate interactively. Many of the tasks that have to be carried out
during modeling are not directly backed up by traditional standard
approaches in the participating disciplines. For example, building
dynamic models from measurement data is a standard procedure in
engineering, using system identification techniques. A large toolbox of
system theoretic results and computational tools is available. However,
these can not directly be applied to the modeling of biological systems.
The main reason lies in different desired levels of abstraction of the
resulting models. In engineering applications the focus is mostly on the
prediction of a systems input/output behavior, while the focus in models
of biological systems lies on the interactions within the system.

An often employed approach to modeling is to generate a model by
selecting a network structure, determining biochemically reasonable rate
expressions, populating the parameters with values found in literature
and/or through experiments, followed by parameter optimization.

In this tutorial we want to focus on computational techniques for
data-based modeling. We will consider both input/output and
physiologically based modeling.

The tutorial will to a large extent allow the participants to get
hands-on experience.