INCOME: Integrative Collaborative Modelling in Systems Medicine

By bringing the community of modelling groups together and contributing to the improvement of the annotation of existing models and the accessibility of related datasets, we will pave the way for the development of a modular, integrative model of cellular processes.

Systems medicine aims at improving disease prevention, diagnosis and therapy by studying the human body as an integrated whole. In contrast to reductionists’ approaches, systems medicine allows to integrate different heterogeneous datasets to achieve a comprehensive understanding of the processes involved. Yet, most research projects are limited to individual pathways and models as well as datasets are rarely reused. Model reuse, extension and integration remain challenges due to improper annotation, non-standard implementation and inaccessibility of the experimental data. This in turn limits collaborative research. e:Med is a funding initiative in which many models have been created in a wide variety of projects. The present collaboration project aims at igniting and facilitating a process that brings
these models and datasets (as well as their developers) closer to each other, that interlinks and connects these models as much as technically possible. To this end, we combine a tailored teaching and networking activity with two focused infrastructure activities that extend the FAIRDOMHub and its network editing and versioning functionality.

The key to the success of this project is the coordinated contribution of many e:Med partners to the networking events and the subsequent work. Early on, we will help members to collect e:Med models and datasets in the FAIRDOMHub. Then we will facilitate collaboration between the members in uniting these models, aiming at an integrative model. While the aim is an integrative model with a high degree of modularity, there is a wide variety of possible useful outcomes, ranging from better-annotated better-reusable models to groups of multi-pathway models and associated data.