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About Research Informatics

The rapid pace of research in the biological sciences requires new thinking in the way that software is developed. Our tools are developed with an awareness of the fundamental problems of informatics currently: integrating and analyzing genomes at the scale of populations rather than individuals; exploring the dynamics of single cell measurements through mining the wealth of biological knowledge available; and enabling ad-hoc analysis and distributed collaborations through the use of good data management principles.

Research Informatics works in three areas to provide solutions.

Prediction

In systems biology, diverse types of experimental data are used to derive predictive models of biological mechanisms. Analysis of these models advances our understanding of biological function and dysfunction (i.e. disease) and can ultimately lead to the discovery of new diagnostic tools and therapeutic strategies. Next generation systems biology approaches will produce massive amounts of heterogeneous data measured over time and across different levels of regulation and hierarchy in the cell. This will require novel approaches towards the inference and analysis of these predictive models, and will drive the development of personalized medicine by using patient specific information combined with data collected from large populations.

Elucidation

Predictions from computational modeling form only the starting point for biological experiments that aim to elucidate biological function. To enable such informed experiments the researcher needs to explore the results from the computational modeling, incorporating information on instrument capabilities (e.g. is a target assayable), additional biological knowledge and the results of previous rounds of experiments. Innovations in measurement technologies or new experimental techniques will allow new biological questions to be answered. Thus, there is a need for sophisticated and highly-automated instrument workflows that enable iterative target refinement and can be rapidly adapted to new usage.

Integration

The measurement data and additional information required both for prediction and elucidation must be made available to the researcher through data integration systems. A data integration platform that is tailored to the needs of systems biology, where data types and analysis methods are continuously introduced, must be adaptable, scalable and extensible. Such a platform will allow for the capture, storage and integration of heterogeneous high throughput data and associated analysis methods in a fast, intuitive and secure manner.