Project heads:
Prof. Dr. Alexander Bockmayr
/
Prof. Dr. Susanna Röblitz
/
Prof. Dr. Heike Siebert
Project members:
Stefanie Kasielke
/
Adam Streck
Duration: -
Status:
completed
Located at:
Freie Universität Berlin
/ Konrad-Zuse-Zentrum für Informationstechnik Berlin
Description
Mathematical modelling in biological and medical applications is almost
always faced with the problem of incomplete and noisy data. Rather than
adding unsupported assumptions to obtain a unique model, a different
approach generates a pool of models in agreement with
all available observations. Analysis and classification of such models
allow linking the constraints imposed by the data to essential model
characteristics and showcase different implementations of key mechanisms.
Within the project, we aim at combining the advantages of logical and
continuous modeling to arrive at a comprehensive system analysis under
data uncertainty. Model classification will integrate qualitative
aspects such as characteristics of the network topology with
more quantitative information extracted from clustering of joint
parameter distributions derived from Bayesian approaches. The theory
development is accompanied by and tested in application to oncogenic
signaling networks.
http://www.mi.fu-berlin.de/en/math/groups/dibimath/projects/A-CH5/index.html
