How should model and data management in ISBE be organised?

Data integration is an essential part of systems biology. Scientists need to combine different sources of information in order to model biological systems, and relate those models to available experimental data for validation. Adopting a common framework and standards for data management in ISBE will enable the exchange of data and models between ISBE centres and allow scientists to more easily discover and reuse these data and models for their own research. Adopting standards that are already in use in the wider life science community will additionally ensure easier exchange with external resources, such as those from ELIXIR.

In ISBE, the Data Integration Centres are particularly reliant on being able to identify data from suitable physiological conditions that can be incorporated into models. The Data Generation and Stewardship Centres can streamline this process by providing standard formats and interfaces for access, storage and exchange.

The model and data management activities in ISBE aim to:

  • Comprehensively survey the state of the art and best practices for model and data management for systems biology in Europe
  • Propose and promote a framework and best practices for model and data management for systems biology in Europe
  • Collaborate with standardisation activities for model and data management in the life sciences and information science disciplines
  • There are technical, social and educational aspects of data and model management. All must be addressed in ISBE in order to provide an infrastructure for systems biology that fits the long-term requirements of the community

Technical aspects:

  1. Which formats, identifiers, standards and ontologies should be used for ISBE?
  2. How should data and models be managed and exchanged within ISBE, and between ISBE and external resources?

Social aspects:

  1. How do we encourage compliance to the standards recommended by ISBE?
  2. How do we make annotation and standardisation easier?

Educational aspects:

  1. Educate existing Systems Biologists and new students in data and model management
  2. How do we make data and model annotation and standardisation part of the Systems Biology experiment life cycle?