Access Track 1:
Genotype-to-Phenotype analysis based on models and experimental data
This Access Track targets life scientists, who aim to relate genotype to phenotype. It will empower users from different scientific domains to address main research topics such as systems and personalized medicine, cancer, neurodegenerative and inflammatory diseases, precision biotechnology and modern nonrecombinant agriculture with high impact for translational research in health and biotech industries.
One of the greatest challenges in biomedical research is to predict disease risk and treatment outcomes for individuals, taking advantage of increasing genomic information. Personalized medicine aims to facilitate predictions about phenotypes of individuals (e.g. susceptibility to diseases or therapies), based on the analysis of complete genome sequences, functional genomic data, clinical assays and lifestyle parameters. Mathematical models of genotype-to-phenotype relations are generated by systems biologists to integrate genetic with molecular, physiological and imaging data. These models need to be available to researchers in biomedical areas handling biological samples or in vivo models, large data sets, screens, images and physiological data. Researchers in precision biotechnology fields (white, red, green and blue) can also benefit from the same type of model provision.
You should choose Access Track 1 in your application, if you need e.g. integrated access to samples, imaging, data analysis and integration expertise, multi-level modelling or subsequent validation of data and predictions with in vivo models. The five research infrastructures BBMRI-ERIC, ELIXIR, INFRAFRONTIER, Euro-BioImaging and ISBE are developing innovation pipelines, which can support you with your research project aiming at predicting specific aspects of phenotypes from individual information.
In case of questions please contact the Access Track leader Sonja Hansen.
Example of an ongoing pilot project:
Terminate-NB: From Cancer DiagnOMICS to Precision Medicine: Model Neuroblastoma
"We are very satisfied with our participation in this CORBEL pilot that will enable to develop further our current project and we recommend European scientists to similarly take this opportunity to work with research infrastructures. Highly interested in services available in other research infrastructures such as EU-OPENSCREEN (see Access Track 2), we appreciate the flexibility offered in this Open Call."
Scientific interest: Our central research goal is to identify and understand core signalling networks controlling hallmarks of cancer in highly aggressive neuroblastoma cells and their microenvironment which lead to treatment failure and relapse. Terminate-NB will enhance, complement and hone molecular NB classification by integrating proteomics and metabolomics with existing and newly generated genomics and transcriptomics data using sophisticated bioinformatics approaches to conduct cross-platform analysis of OMICS datasets and cross-species analysis from human, mouse and zebra fish data aiming to make the clinical phenotypes of residual and metastatic disease predictable. Functional evaluation of complementary OMICS datasets will lead to improved individualized treatment strategies via the precise molecular risk stratification and identification of key-signalling elements as promising drug targets.
Work plan: We are working with ISBE partners at the MDC in Berlin highly involved in our in silico integrOMICS approach based on solid bioinformatics and computational modelling. In particular they are assessing prognostic and diagnostic features of high-dimensional genomics data for NB and analyzing gene regulatory impacts of (epi)genetic alterations in NB. We are also collaborating with the German Infrafrontier node at the Helmholtz Zentrum München and planning systemic phenotyping of several mouse mutants we have generated to validate previously identified targets. In addition we are considering using BBMRI-ERIC services to support the biobanking of newly collected NB patient samples.