Prof. Dr. Susanna Röblitz

Head of projects CH5 and CH6

Freie Universität Berlin
Arnimallee 6
14195 Berlin
+49 (0) 30 84185156
susanna.roeblitz'at'mi.fu-berlin.de
Website

Head of reasearch group Computational Systems Biology

Zuse Institute Berlin (ZIB)
Takustr. 7
14195 Berlin
+49 (0) 30 84185156
susanna.roeblitz'at'zib.de
Website


Research focus

Numerics of high-dimensional problems
Parameter identification
Systems biology

Projects as a project leader

  • CH5

    Model classification under uncertainties for cellular signaling networks

    Prof. Dr. Alexander Bockmayr / Prof. Dr. Susanna Röblitz / Prof. Dr. Heike Siebert

    Project heads: Prof. Dr. Alexander Bockmayr / Prof. Dr. Susanna Röblitz / Prof. Dr. Heike Siebert
    Project members: Stefanie Kasielke / Adam Streck
    Duration: -
    Status: running
    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
  • CH6

    Uncertainty quantification for Bayesian inverse problems with applications to systems biology

    Prof. Dr. Susanna Röblitz / Prof. Dr. Christof Schütte

    Project heads: Prof. Dr. Susanna Röblitz / Prof. Dr. Christof Schütte
    Project members: Dr Ilja Klebanov
    Duration: -
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    In biotechnology, systems biology, or reaction engineering one is faced with large systems of ordinary differential equations (ODE) that are used to describe the kinetics of the reaction network of interest. These ODE models contain a large number of mostly unknown kinetic parameters that one needs to infer from usually sparse and noisy experimental data. Typically, inverse problems like classical parameter identification are associated with ill-posed behaviour. However, Bayesian approaches can be used to recover joint parameter distributions and allow for the quantification of uncertainty and risk in a way demanded by the applications. In this project, we want to overcome the computational limitations of classical Markov-chain Monte-Carlo methods by developing new algorithmic approaches to Bayesian inverse problems using, e.g., sparse approximation results or empirical Bayes methods. The methods will directly be applied to large-scale networks in systems biology.

    http://www.zib.de/projects/UQ-systems-biology