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Our research unit is working on several 3rd party projects funded by the European Union's Horizon2020 program, the German Research Foundation, the German Federal Ministry of Education and Research, and the German Federal Ministry of Economic Affairs and Energy:

CanPathPro - a platform for predictive cancer pathway modeling

CanPathPro_logoThe advancement of omics technologies as well as growing amounts of public datasets provide the basis for large-scale mechanistic modeling of biological systems. Mechanistic models help to gain a mechanistic understanding of biological processes instead of providing mere statistical correlations. Such mechanistic models can be used for in-silico experiments to, for example, predict the outcome of a pharmaceutical intervention, which is an important tool for personalized medicine as well as for identifying novel drug targets.

However, the parameterization of such large-scale models is computationally very demanding and currently not yet feasible. Furthermore, improved visualization methods are required to analyze and interpret simulation results and parameter estimates. To this end, CanPathPro aims to generate and integrate novel algorithms and protocols for the large-scale predictive modeling of cancer pathways.

Together with our project partners, we:

  • refine hybrid stochastic-deterministic global optimisation methods for maximum likelihood model parameter estimation
  • develop methods for the integrated visualisation of the underlying heterogeneous experimental data, model predictions, and statistical measures
  • integrate these tools into an easy-to-use platform for the predictive modelling of cancer signalling
  • generate hypotheses which will – in close collaboration with experimentalists – be tested in vivo to validate or refine the model

 

Funding:

European Union's Horizon2020 (grant agreement no. 686282)

Partners:

Alacris Theranostics GmbH, Agencia Estatal Consejo Superior de Investigaciones Cientificas, Biognosys AG, FINOVATIS SAS, Helmholtz Zentrum München, Leibniz-Institut für Alternsforschung - Fritz-Lipmann-institut e.V., PHENOMIN-ICS - Institut Clinique de la Souris, Simula Research Laboratory AS, Stichting Het Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis

Webpage:  

http://www.canpathpro.eu/

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Simulation-based parameter optimization and uncertainty analysis methods for reaction-diffusion-advection equations

Reaction-diffusion-advection equations are used in many fields of engineering and natural sciences to model spatio-temporal processes. As the parameters of these mathematical models are often unknown, they have to be determined from the available experimental data. Here, the first step is usually to employ optimization to determine the parameter values yielding the best match of the model prediction and the experimental data. In the second step, the uncertainty of these parameter values is analyzed to determine the predictive power of the model. In both steps constraint optimization problems have to be solved. For this reliable optimization algorithms are required which converge robustly. Available methods however fail to meet these reliability requirements for a variety of models. Accordingly, we develop a novel simulation-based optimization and profile likelihood calculation method for reaction-diffusion-advection equations, which exploiting the problem structure evaluate the simulation-based methods to several biological problems, including lateral line formation in zebrafish implement a user-friendly MATLAB toolbox

Funding:

German Research Foundation (Grant no. 629352)

Partners:

University of Klagenfurt

Webpage:  

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INCOME - Integrative collaborative modeling in systems medicine

Systems medicine aims at improving disease prevention, diagnosis and therapy by studying the human body as an integrated whole. In contrast to reductionists’ approaches, systems medicine allows to integrate different heterogeneous datasets to achieve a comprehensive understanding of the processes involved. Yet, most research projects are limited to individual pathways and models as well as datasets are rarely reused. Model reuse, extension and integration remain challenges due to improper annotation, non-standard implementation and inaccessibility of the experimental data. This in turn limits collaborative research.
This project aims at igniting and facilitating a process that brings these models and datasets (as well as their developers) closer to each other, that interlinks and connects these models as much as technically possible. To this end, we combine a tailored teaching and networking activity with two focused infrastructure activities that extend the FAIRDOMHub and its network editing and versioning functionality.

Funding:

Federal Ministry of Education and Research (grant no. 01ZX1705A)

Partners:

Heidelberger Institut für Theoretische Studien, Helmholtz Zentrum München, Technical University of Munich, University of Rostock

Webpage:  

https://www.integrative-pathway-models.de/

FitMultiCell - Integrated platform for data-driven modelling of multicellular processes

Pathological changes in tissue are currently analyzed using quantitative imaging techniques and described using computer models. So far, however, there is no solution to parameterize multicellular models with high throughput and high resolution. In this project, we will implement and validate a user-friendly, open-source software framework for modeling, simulating and parameterizing multicellular processes based on quantitative image data. This software framework can be used in biomedical research in the future. FitMultiCell will combine the strengths of the modelling and simulation tool Morpheus developed at TU Dresden (TUD) with the extended statistical inference tool pyABC developed at Helmholtz Zentrum München (HMGU). FitMultiCell will be applicable on different computer infrastructures and will use Deep Learning.

 

Funding:

Federal Ministry of Education and Research (grant no. 031L0159A)

Partners:

Universität Heidelberg, Technische Universität Dresden

Webpage:  

https://fitmulticell.gitlab.io/

 

Previous funding

 SYS-Stomach - Identification of predictive response and resistance factors to targeted therapy in gastric cancer

Gastric cancer was estimated to be the fifth most common cancer and third leading cause of death from cancer worldwide. Treatment options for gastric cancer patients include surgery, chemotherapy and radiation therapy. However, the overall survival rate remains unsatisfactory and new treatment options are urgently required. Novel drugs targeting members of a family of receptor tyrosine kinases including HER2 and epidermal growth factor receptor (EGFR) have shown mixed success in clinical trials.

In this project a systematic molecular and phenotypic analysis of a panel of gastric cancer cell lines will be performed. From these data we derive mechanistic and statistical models for use in patient stratification. In our subproject, we:
•    analyze the relation of molecular and phenotypic properties of gastric cancer cell lines
•    reconstruct the signaling pathways of cetuximab and trastuzumab
•    validate the reconstructed signaling pathways and phenotype link based upon MALDI imaging MS patient samples

 

Funding:

Federal Ministry of Education and Research (grant no. 01ZX1310B)

Partners:

Biomax Informatics AG, Helmholtz Zentrum für Infektionsforschung, Helmholtz Zentrum München, Technical University of Munich, Universitätsklinikum Leipzig

Webpage:  

http://www.biomax.com/project/sys-stomach/

 

 

 

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