Machine Learning to Program Living Cells

Over the past two decades, the ability to engineer increasingly complex genetic circuits, sequences of DNA that encode computational operations in living cells, has advanced rapidly. Progress has resulted from several factors including faster and cheaper DNA sequencing, an improved understanding of cell biophysics and the ability to make targeted genomic modifications using CRISPR. Such advances have long ranging implications for advancing healthcare and drug discovery. Yet despite this progress, biological engineers often spend years creating a single functional design through manual trial-and-error. Drawing inspiration from machine learning and digital logic synthesis, we built a genetic circuit design automation platform, Cello. Cello has aided in the design of some of the most complex genetic circuitry to date.