Machine Learning to Program Cells
Machine Learning to Program Cells


Over the past two decades, the ability to engineer increasingly complex and precise genetic circuits has advanced rapidly. Progress has resulted from several factors: thousands of sequenced genomes (and metagenomes) from which to “mine” useful genes, faster and cheaper DNA synthesis and sequencing, an improved understanding of cell biophysics to enable simulation, the ability to make targeted genomic modifications using CRISPR, and last but not least, years of compounded genetic engineering experience distilled into guiding design principles.

Yet despite our progress so far, genetic circuit design has often been characterized by a manual and failure-prone process. Engineers often spend years creating a functional design through trial-and-error.

Drawing inspiration from the evolution of hardware automation, Asimov has built a genetic circuit design automation platform, Cello. Combining concepts from digital logic synthesis, cell biophysics, machine learning and synthetic biology, we were able to build genetic circuits with up to 10 interacting genes.


Joe is the VP of Engineering at Asimov, a startup with the mission to program living cells. We leverage techniques from synthetic biology, systems engineering and machine learning to automate the compilation of genetic circuits. Prior to Asimov, Joe lead machine learning teams at Quora and at URX (Y-combinator startup, acquired by Pinterest). In these roles, Joe helped to build recommendation systems to personalize content discovery, algorithms to optimize advertisement targeting and machine learning models for search and discovery. Prior to joining the startup world, Joe contributed to research at MIT Lincoln Laboratory and Brown University, developing numerical methods for problems in forensics, computer vision and molecular dynamics.

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