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Funding

 

The European Union's Horizon2020 program, 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

The advancement of omics technologies as well as increasing 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 mere statistical correlations. Mechanistic models can be used for in-silico experiments to 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 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 novel algorithms and protocols for the large-scale predictive modeling of cancer pathways.

Together with our project partners, we:

  • refine hybrid stochastic-deterministic global optimization methods for maximum likelihood model parameter estimation
  • developing methods for the integrated visualization of heterogeneous experimental data, model predictions, and statistical measures
  • easy-to-use platform for the predictive modeling of cancer signaling
  • 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 Institute for Aging Research - Fritz-Lipmann Institute, 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/

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. 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 been solved. For these reliable optimization algorithms are required which converge robustly. Available to meet the requirements for a variety of models. Accordingly,

Funding:

German Research Foundation (Grant no. 629352)

Partners:

University of Klagenfurt

Webpage:  

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

Gastric cancer was estimated to be the fifth most common cancer and third leading cause of death 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 tyrosine kinases family including HER2 and epidermal growth factor receptor (EGFR) have shown mixed success in clinical trials.

This project is a systematic molecular and phenotypic analysis of a panel of gastric cancer cell lines. From these data, we derive mechanistic and statistical models for use in patient stratification. In our subproject, we:

reconstruct the signaling pathway of cetuximab and trastuzumab
• reconstruct the 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 Center for Infection Research, Helmholtz Zentrum München, Technical University of Munich, University Hospital Leipzig

Webpage:  

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

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. 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 due to improper annotation, non-standard implementation and inaccessibility of the experimental data. This in turn limits collaborative research.
This project aims at understanding and facilitating a process that brings together more than one model of technology. The FAIRDOMHub and its network editing and versioning functionality.

Funding:

Federal Ministry of Education and Research (Grant No. 01ZX1705A)

Partners:

Heidelberg Institute for Theoretical Studies, Helmholtz Zentrum München, Technical University of Munich, University of Rostock

Webpage:  

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

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

Pathological changes in tissue are currently being 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 want to 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 wants to combine the strengths of modeling and simulation tool Morpheus developed at TU Dresden (TUD) with the extended statistical inference tool pyABC developed at Helmholtz Zentrum München (HMGU). FitMultiCell wants to be on different computer infrastructures and wants to use Deep Learning.

 

Funding:

Federal Ministry of Education and Research (Grant No. 031L0159A)

Partners:

University of Heidelberg, Dresden University of Technology

Webpage:  

https://fitmulticell.gitlab.io/

 

 
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