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Since 2019, Matheon's application-oriented mathematical research activities are being continued in the framework of the Cluster of Excellence MATH+
www.mathplus.de
The Matheon websites will not be updated anymore.

Prof. Dr. Susanna Röblitz

Leiterin der Projekte CH5 und CH6

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

Leiterin der Arbeitsgruppe Mathematische Systembiologie

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


Forschungsschwerpunkte

Numerik hochdimensionaler Probleme
Parameteridentifizierung
Systembiologie

Projekte als Projektleiter

  • CH13

    Empirical Bayes methods for patient-specific prediction and control of pharmacological interventions

    Dr. Rainald Ehrig / Prof. Dr. Susanna Röblitz

    Projektleiter: Dr. Rainald Ehrig / Prof. Dr. Susanna Röblitz
    Projekt Mitglieder: Dr Ilja Klebanov
    Laufzeit: 01.06.2017 - 31.12.2018
    Status: beendet
    Standort: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Beschreibung

    One of the main goals of mathematical modeling related to medical applications is to obtain patient-specific parametrizations and model predictions. In clinical practice, however, the number of available measurements for single patients is usually limited due to time and cost restrictions. This hampers the process of making patient-specific predictions about the outcome of a treatment. On the other hand, data are often available for many patients, in particular if extensive clinical studies have been performed. Empirical Bayes methods can provide a solution to this controversy. Instead of applying Bayes’ rule to each measurement separately, these methods usually boil down to combining all measurements in order to construct an informative prior as a first step and then using this prior for the Bayesian inference of the individual parametrizations in a second step. We want to demonstrate the applicability and benefit of this approach on a high-dimensional model system for predicting patient-specific treatment success rates related to in vitro fertilization in reproductive medicine.

    http://www.zib.de/projects/empirical-bayes-methods-patient-specific-prediction-and-control-pharmacological-interventions
  • CH5

    Model classification under uncertainties for cellular signaling networks

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

    Projektleiter: Prof. Dr. Alexander Bockmayr / Prof. Dr. Susanna Röblitz / Prof. Dr. Heike Siebert
    Projekt Mitglieder: Stefanie Kasielke / Adam Streck
    Laufzeit: -
    Status: beendet
    Standort: Freie Universität Berlin / Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Beschreibung

    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

    Projektleiter: Prof. Dr. Susanna Röblitz / Prof. Dr. Christof Schütte
    Projekt Mitglieder: Dr Ilja Klebanov
    Laufzeit: -
    Status: beendet
    Standort: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Beschreibung

    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