The scientific challenge for this project is to accelerate discovery and exploration of the synthetic biology design space. In particular, many parts used in synthetic biology come from or are initially tested in a simple bacteria, E. coli, but many potential applications in energy, agriculture, materials, and health require either different bacteria or higher level organisms (yeast for example). Currently, researchers use a trial-and-error approach because they cannot find reliable information about prior experiments with a given part of interest. This process simply cannot scale. Therefore, to achieve scale, a wide range of data must be harnessed to allow confidence to be determined about the likelihood of success. The quantity of data and the exponential increase in the publications generated by this field is creating a tipping point, but this data is not readily accessible to practitioners. To address this challenge, our multidisciplinary team of biological engineers, machine learning experts, data scientists, library scientists, and social scientists will build a knowledge system integrating disparate data and publication repositories in order to deliver effective and efficient access to collectively available information; doing so will enable expedited, knowledge-based synthetic biology design research.

This project was founded by National Science Foundation Grants No. 1939892