Projects

MESI-STRAT: Systems Medicine of Metabolic-Signaling Networks -A New Concept for Breast Cancer Patient Stratification. Breast cancer is a complex disease with high prevalence in the European Union and world-wide. 75%-80 of the patients have estrogen receptor-positive (ER)-positive tumors and are treated with endocrine therapies. Endocrine therapies, which block ER-driven tumor growth, show high efficacy. Yet, a significant proportion of the patients will eventually relapse with metastatic breast ...

IMOMESIC - Integrating Modelling of Metabolism and Signalling towards an Application in Liver Cancer

One of the most challenging questions in cancer research is currently the interconnection of metabolism and signalling. An understanding of mechanisms that facilitate the physiological shift towards a proliferative metabolism in cancer cells is considered a major upcoming topic in oncology and is a key activity for future drug development. Due to the complexity of interrelations, a systems biology ...

We will contribute to the LiSyM Research Network an open source, freely available and reproducible multiscale model of the human liver from single cell metabolism to whole liver function. The model will be available in existing standards of systems biology, provide standardized interfaces for data integration and be fully annotated to available biological, medical and computational ontologies. All data, models and source code will be shared within the LiSyM Research Network and made available to ...

The workshop focuses on the publication, curation, retrieval, and usage of kinetic data from the reaction kinetics database SABIO-RK and on the use of data in modeling. There will be experience reports from scientists who successfully used experimental data to formulate or verify biological hypotheses with the computer, and you will experience how experimental data can be used with computational models.

The COLOSYS project aims to develop a deeper understanding of colon cancer networks and convert them into computer models with which it will be better to predict response to treatment. The combination of computational, experimental and clinical testing will provide understanding of drug resistance mechanisms, and allow personalised treatment of colon cancer.

tba.