Programming Living Organisms Through Targeted Machine Learning

Over the past two decades, the ability to engineer increasingly complex genetic circuits, strands of DNA or RNA designed to perform logical functions inside of living organisms, 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. Yet despite our 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.

Joe Isaacson, VP of Engineering at Asimov

Joe is the VP of engineering at Asimov, a startup with the mission to program living cells with genetic circuits. We leverage techniques from synthetic biology, systems engineering and machine learning to continually improve the automation of genetic circuit design. Previous to Asimov, Joe lead machine learning teams at Quora, building recommendation systems to personalize content discovery and algorithms to optimize ad targeting. Previous to Quora, Joe lead the data science team at URX (acquired by Pinterest), a machine learning startup focused on leveraging information theory and knowledge bases to target advertisements

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