This chart shows collaborative projects between working groups at the Simons Foundation in 2014. Some of these efforts are formal grants programs (“Programs”), but many are ongoing collaborations between scientists on staff (“Projects”). SCDA and the Informatics group often assist other foundation groups in the planning, storage, analysis and dissemination of large datasets.
At one year, SCDA now comprises six research groups focusing on computing, software development, algorithms, systems biology, neuroscience and genomics. “What unifies the application areas is that they’re all parts of biomedical and biophysical research, where there are opportunities for discovery that cannot be achieved by classical techniques,” Greengard explains. “It’s not just about doing a better experiment. It’s about combining experiment with theory, simulation and data analysis to learn things that are beyond the capacity of a single measurement.”
Part of Dmitri Chklovskii’s neuroscience group, for example, is developing techniques for reconstructing the three-dimensional architecture of neural networks from data generated by high-resolution electron microscopy — a computational task that is currently intractable, according to Greengard. Members of Olga Troyanskaya’s team conduct meta-analyses of genomic data from different organs (and even different species) to better predict gene expressions associated with specific cell types or specific diseases and developmental disorders, including autism. The systems biology group, led by Richard Bonneau, aims in part to understand the regulatory controls of the immune system and the interaction between the human genome and the ‘microbiome’ — the microorganisms that live on and in our bodies and outnumber our own cells by an order of magnitude.
According to Greengard, each of these scientific endeavors requires new analytic approaches, and the development of methods that make sense of the data (‘data science’) is becoming a discipline in its own right. SCDA requires leaders who have both a deep understanding of the underlying science and the ability to create the mathematical and computational frameworks that will permit scientists to ask previously inaccessible research questions. Going forward, each group at SCDA will typically work closely with external experimental collaborators to analyze their results, which will also drive the center’s thinking about new questions to investigate. Greengard intends to be selective about what problems SCDA takes on, because each one could easily consume all of the center’s scientific and technical resources. “The reason SCDA exists,” he says, “is to develop new ways of thinking about big data in biology and to develop tools that will enhance the productivity of individual researchers.”
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