Prof. Dr. Susanna Röblitz

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