MultiPath

A generic multi-layer model for integrating multiple types of pathway knowledge

Aims
Currently the integration of prior knowledge about biological pathways as well as re-tracing the use of specific pathway knowledge in scientific publications is in-efficient and cumbersome. This junior group aims at facilitating the integration of pathway knowledge and boosting reproducible research in clinical research in general and in systems medicine in particular. This research project aims at easing data integration via a new generic multi-layer pathway modeling framework. The central idea is the definition of a multi-layer pathway modeling framework which offers a generic and extendable format for integrating multiple pathway types and further knowledge sources influencing these pathways. Its outstanding feature is the inclusion of procedures allowing automatic pathway transformations and their documentation. Within this proposal two highly innovative applications are planned within the clinic:

  • First, improving clinical research by facilitating prior knowledge integration for hypotheses generation and analyses of high-throughput cancer cohort data.

  • Second, improving clinical practice by enhancing the university clinic's tumor board via the integration of pathway data, high-throughput and mutation patient data, as well as drug-target information, into patient-specific multi-layer models.
The generic model is an n-layer graph with nodes arranged within layers and nodes connected via inter- and intra-layer edges. The multi-layer models will include the initial pathway data and all performed operations and transformations, increasing reproducibility in the field of network biology and network medicine.


Work plan

The central aim of this junior group is the development of a new generic multi-layer pathway modeling framework. This includes the theoretical model definition as well as the implementation for general use. Apart from an independent implementation, support for these multi-layer models will be added to state-of-the-art software tools via plug-ins and packages, in order to increase interoperability and to enable an easy adaption of our approach for fellow researchers. Available pathway resources will be compiled into an online catalog of models. Furthermore, specific models will be evaluated via functional analyses in cancer cohort data sets of colorectal carcinomas (DFG clinical research group KFO179) and metastatic tumors (BMBF e:Bio MetastaSys) together with our clinical cooperation partners.


Keywords: Network Medicine, Network Biology