By adding randomness to sort-seq assays (fluorescence activated cell sorting + high-throughput sequencing) we can get precise multiplexed measurements. This solves an open problem in sort-seq assays: deterministic binning introduces systematic bias that limits precision. (2/4)
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How can we collect good enough data for machine learning driven protein design? We show that random numbers are part of the picture. Work with the David Baker lab (including
@erika_alden_d
) and MSRNE (with
@KevinKaichuang
and
@lorin_crawford
). (1/4)
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But we can get rid of this bias if we use random numbers to flip a (biased) coin to choose how to bin each cell. We show how this works with a mix of statistical theory, simulations, and wet-lab experiments.
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Pseudorandom numbers are crucial in computer science (e.g. cryptography) and statistics (e.g. randomization in trials), but rarely feature in biological assays. So it’s neat to find that they’re useful here too!
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