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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
PoLiMeR is funded through the EU Marie Skłodowska-Curie Innovative Training Network (ITN), which drives scientific excellence and innovation. ITNs bring together universities, research institutes, industry and clinical partners from across the world to train researchers to doctorate level.
Metabolic diseases are a burden on the European population and health care system. It is increasingly recognised that individual differences with respect to history, lifestyle, and genetic make-up affect disease
The project addresses the generation and establishment of programmed pacemaker cells for an in vitro drug testing possibility to perform predictive tests. This may lead to an improved treatment of cardiac arrhythmias or an accurate identification of potential drug molecules at a very early stage of development. Important benefits will arise in verifying the safety of a wide variety of medicines while reducing animal testing.
This project is funded by the European Social Fund (ESF) program of the
Good data and model management improves the longevity and impact of your interdisciplinary research. FAIRDOM offers software and expertise to support you in better managing your interdisciplinary life-science projects, particularly in systems and synthetic biology. If you have never heard of data and model management, or are curious about it, or you are an expert keen to exchange ideas, our user meeting is the place for you!
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Detection of pathogenic mutation in the patients with non-coronary heart diseases
Public web page: Not specified
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.
Creator: Matthias König
Contributor: Matthias König
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Not specified
Organism: Homo sapiens
Investigations: MM-PLF: Multiscale modeling for personalized li...
Modelling analyses: Galactose Modelling
Date Published: 8th Jan 2015
Journal: PLoS Pathog
PubMed ID: 25569684
Citation: PLoS Pathog. 2015 Jan 8;11(1):e1004573. doi: 10.1371/journal.ppat.1004573. eCollection 2015 Jan.