Recent advances in modeling languages for pathway maps and computable biological networks.

As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards.

Empowering industrial research with shared biomedical vocabularies.

The life science industries (including pharmaceuticals, agrochemicals and consumer goods) are exploring new business models for research and development that focus on external partnerships. In parallel, there is a desire to make better use of data obtained from sources such as human clinical samples to inform and support early research programmes. Success in both areas depends upon the successful integration of heterogeneous data from multiple providers and scientific domains, something that is already a major challenge within the industry.

Beyond data integration.

Pharmaceutical R&D organizations have no shortage of experimental data or annotation information. However, the sheer volume and complexity of this information results in a paralyzing inability to make effective use of it for predicting drug efficacy and safety. Data integration efforts are legion, but even in the rare instances where they succeed, they are found to be insufficient to advance programs because interpretation of query results becomes a research project in itself.