We actually think about this a lot and we believe that these processes need to learn from each other. We build feedback and feed forward loops between dry lab and experimental work - essentially we think iteration is most important. We do up to 2.2 millions experiments in our wet lab each week to feed machine learning predictions and those predictions feed back into the wet lab experiment design. We do all of this in service of decoding biology and delivering therapeutics to patients.

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EDIT: Removed a typo.