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Predicting long-term toxic effects using computer models based on systems characterization of organotypic cultures

Objective

NOTOX will develop and establish a spectrum of systems biological tools including experimental and computational methods for i) organotypic human cell cultures suitable for long term toxicity testing and ii) the identification and analysis of pathways of toxicological relevance. NOTOX will initially use available human HepaRG and primary liver cells as well as mouse small intestine cultures in 3D systems to generate own experimental data to develop and validate predictive mathematical and bioinformatic models characterizing long term toxicity responses. Cellular activities will be monitored continuously by comprehensive analysis of released metabolites, peptides and proteins and by estimation of metabolic fluxes using 13C labelling techniques (fluxomics). At selected time points a part of the cells will be removed for in-depth structural (3D-optical and electron microscopy tomography), transcriptomic, epigenomic, metabolomic, proteomic and fluxomic characterizations. When applicable, cells derived from human stem cells (hESC or iPS) and available human organ simulating systems or even a multi-organ platform developed in SCREENTOX and HEMIBIO will be investigated using developed methods. Together with curated literature and genomic data these toxicological data will be organised in a toxicological database (cooperation with DETECTIVE, COSMOS and TOXBANK). Physiological data including metabolism of test compounds will be incorporated into large-scale computer models that are based on material balancing and kinetics. Various “-omics” data and 3D structural information from organotypic cultures will be integrated using correlative bioinformatic tools. These data also serve as a basis for large scale mathematical models. The overall objectives are to identify cellular and molecular signatures allowing prediction of long term toxicity, to design experimental systems for the identification of predictive endpoints and to integrate these into causal computer models.
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Coordinator

UNIVERSITAT DES SAARLANDES

Address

Campus
66123 Saarbrucken

Germany

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 1 078 931

Administrative Contact

Claudia Schacht (Ms.)

Participants (12)

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CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS

France

EU Contribution

€ 339 273

STICHTING HET NEDERLANDS KANKER INSTITUUT-ANTONI VAN LEEUWENHOEK ZIEKENHUIS

Netherlands

EU Contribution

€ 255 621,55

KAROLINSKA INSTITUTET

Sweden

EU Contribution

€ 500 525

INSILICO BIOTECHNOLOGY AG

Germany

EU Contribution

€ 330 576

INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE

France

EU Contribution

€ 382 909

DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH

Germany

EU Contribution

€ 327 343

FORSCHUNGSGESELLSCHAFT FUR ARBEITSPHYSIOLOGIE UND ARBEITSSCHUTZ E.V.

Germany

EU Contribution

€ 280 524

BIOPREDIC INTERNATIONAL SARL

France

EU Contribution

€ 168 237

WEIZMANN INSTITUTE OF SCIENCE

Israel

EU Contribution

€ 218 832

Cambridge Cell Networks Ltd

United Kingdom

EU Contribution

€ 382 762

EURICE EUROPEAN RESEARCH AND PROJECT OFFICE GMBH

Germany

EU Contribution

€ 233 576

UNIVERSITEIT MAASTRICHT

Netherlands

EU Contribution

€ 350 871,45

Project information

Grant agreement ID: 267038

Status

Closed project

  • Start date

    1 January 2011

  • End date

    31 December 2015

Funded under:

FP7-HEALTH

  • Overall budget:

    € 9 699 961,60

  • EU contribution

    € 4 849 981

Coordinated by:

UNIVERSITAT DES SAARLANDES

Germany