Projects
The goal of MSB is to provide expertise on state-of-the-art methods for the analysis of the most important high-throughput experiments and for mathematical modelling for cooperation partners. We also develop customized methods that go beyond standard analyses and pipelines. In doing so, we try to transfer findings and methodological approaches for data analysis to new fields of research. |
Analysis of Single Cell Data
In this project, we compare the performance of statistical models and tests as well as machine learning methods (e.g. clustering) for processing of single cell data, e.g. from scRNA-seq or mass cytometry. We also establish pipelines which are robust against the choice of configuration parameters.
Responsible: Eva Kohnert, Ariane Schad, Clemens Kreutz
Our collaborators: Stefan Reinker (Novartis), Florens Lohrmann, Philipp Henneke
Modelling Methods in Systems Biology
In this project, we develop and extend existing methodology for establishing mathematical models of biochemical processes in living cells. We focus on
- development of reliable and robust optimization methods for parameter estimation
- deriving a valid statistical methodology for parameter and prediction uncertainties as well as for identifiablity and observability analyses
- establishing strategies like L1 regularization for deriving small minimal models
- approximating differential equation models by functions as a prerequisite for multi-scale modelling
- evaluate deep learning approaches in the context of ODE modelling
Responsible: Lukas Refisch, Rafael Arutjunjan, Clemens Kreutz
Our collaborator: Jens Timmer, Andreas Raue (Merrimack Pharm.)
Benchmarking in Systems Biology
In this project, we evalute the performance of existing computational methods for mathematical modelling.
- We compare the performance of optimization methods which are applied for model calibration/parameter estimation
- We establish benchmark problems which can be used to assess modelling methods
- We develop and extend methodology for performing reliable benchmark studies
- We establish approaches for generation of simulation data realistically
Link: Benchmark models on github
Responsible: Lukas Refisch, Janine Egert, Clemens Kreutz
Our collaborators: Jens Timmer, Jan Hausenauer
Prediction of local COVID-19 progression
In this project, we analyze the IfSG data of the confirmed COVID-19 cases at the level of counties (Landkreise) and provide daily updated predictions for new infections for all ICUs in Germany.
Responsible: Lukas Refisch, Fabian Lorenz, Clemens Kreutz
Our collaborators: Robert Koch Institute (RKI), Federal Institute for Population Research (BIB), German Aerospace Center (DLR)
Modelling of Pattern Formation
In this project, develop spatio-temporal models of cellular signalling during morphogenesis in Drosophila (wing formation) and Xenopus (cilia and mucociliary development).
Responsible: Fabian Lorenz, Rafael Arutjunjan, Janine Egert, Clemens Kreutz
Collaborators: Georgios Pyrowolakis, Peter Walentek
COVID-19 Surveillance of Hospitalized Patients
In this project, we optimize surveillance and test strategies for patients and employees in the Oberbergkliniken based on agent-based stochastical dynamical models.
Responsible: Tim Litwin, Jens Timmer, Clemens Kreutz
Our collaborators: Matthias Müller, Andreas Wahl Kordon, Marcus Panning, Mathias Berger
Optimal Personalized Treatment of Anemia
In this project, we apply a mathematical model of EPO binding and its effect on erythropoesis to predict the optimal time- and dosage strategy for treating anemia. We consider cancer as well as patients with chronic kidney disease.
Responsible: Lukas Refisch, Clemens Kreutz
Our collaborators: Ursula Kingmüller, Jens Timmer
Identification of Differentially Methylated Regions (DMRs) from Bisulfite Sequencing (BSSEQ) Data
In this project, we compare and assess the performances of algorithms for detecting differentially methylated regions (DMRs) from bisulfite sequencing (BSSEQ) data.
Responsible: Clemens Kreutz
Our collaborators: Stefan Rensing
Optimizing adaptamer arrays
In this project, we analyze kinetic data of adaptamer binding and dissociation by mathematical models. We assess binding characteristics and develop predictive models for testing new adaptamer sequences.
Responsible: Lukas Refisch, Clemens Kreutz
Our collaborator: Günter Roth
Benchmarking Wiki
In this project, we started to develop a wiki as a repository for published benchmark studies. We intend to collect insights from literature obtained by comparing the performance of computational approaches.
The benchmarking wiki also offers the opportunity to publish (possibly small or unpublished) own results.
Everybody is welcome to contribute!
Link: Benchmark Wiki
Responsible: Clemens Kreutz
Contact/Location (klick on the image):
Location:
Stefan Meier Str. 26
79104 Freiburg