Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation

A New Biology for a New Century Obstacles to an Exponential Increase in Synthetic Biology Productivity Machine Learning's Predictive Capabilities Machine Learning Needs Automation To Be Truly Effective Predictive Synthetic Biology Will Dramatically Impact Biology and Inspire Computer Science Biology has changed radically in the past two decades, transitioning from a descriptive science into a design science. The discovery of DNA as the repository of genetic information, and of recombinant DNA as an effective way to modify it, has first led into the development of genetic engineering and later the field of synthetic biology. Synthetic biology(1) goes beyond the historical practice of a biological research based on describing and cataloguing (e.g., Linnaean taxonomic classification or phylogenetic tree development), and aims to design biological systems to a given specification (e.g., production of a given amount of a medical drug or targeted invasion of a specific type of cancer cell). This transition into an industrialized synthetic biology is expected to affect most human activities, from improving human health, to producing renewable biofuels to combat climate change.(2) Some examples commercially available now include synthetic leather and spider silk, renewable biodiesel that propels the Rio de Janeiro public bus system, vegan burgers with meat taste, and sustainable skin-rejuvenating cosmetics.