Statement - C . Titus Brown

The future of biology and biomedicine lies at the intersection of data gathering, hypothesis generation, and hypothesis testing. It is increasingly easy to gather large amounts of relevant data, but interpretation of that data and connection of that data to downstream hypothesis-driven experimentation is still difficult and rare. Effectively integrating large scale data analysis with “wet” biology requires the development of new perspectives and new tools, and their integration with current research programs; it also requires biology-immersed scientists who are computationally expert. My research plans focus on the opportunities at the intersection of computation and biology: in particular, I am interested in enabling and doing data-intensive biology, where I can build tools and approaches that harness large investigator-driven data sets to direct hypothesis-driven experimentation and hypothesis-free interpretation. I have spent 20 years applying computation effectively to pressing scientific problems. During this time I have worked in evolutionary modeling, data analysis for climate studies, regulatory genomics, developmental gene regulatory networks, and bioinformatics. My graduate and postdoctoral work in evolutionary developmental biology used computation to generate hypotheses in a range of biological systems, including sea urchin, chick, microbes, and marine sediment microbial communities. Moreover, in all these areas I have worked closely with experimentalists to connect hypotheses to experiment. Over the coming decade, I will continue to work on important biological problems using computation and collaboration. Current research: Data Intensive Biology