Abstract:

Biology is characterized by a diversity of phenotypes and functions that outpace the limited molecular diversity encoded in genomes. How this is achieved is a fundamental and enduring mystery of the cell. It is clear that networks defining how biological molecules interact can drive diverse and complex phenotypes, but the intermolecular interactions or edges of these networks are highly multidimensional and difficult to measure at scale. The predictive power of models depends not only on the identities of components and their edges, but on complex properties of those edges ranging from intermolecular specificities, quantitative binding affinities, rate constants for enzymatic reactions, the influence of post-translational modifications, cross-talk between components, and the spatiotemporal organization and dynamics of network components and activities. This talk will describe the development of scalable synthetic biology platforms to comprehensively map these edge properties of protein networks, applied to the human epigenome. We will also describe how these principles need to be considered and engineered in completely abiotic applications that leverage biomolecules; specifically, we will describe the engineering of extremely dense digital information storage and computing systems with DNA as the information medium. We will also touch on how these principles translate upward in spatial scale to biomedical applications in neurological disorders such as Angelman Syndrome and in the engineering of human cerebral organoid models of the developing brain.