Once we sequenced the human genome, we'd know the cause of -- and therefore be able to help cure -- all diseases... Or so we thought. Turns out, 20,000 genes (and counting) didn't really explain why disease occurred. Sure, some could be explained by mutations in a single genome, but most, like cancer, are too damn complex. And while the focused, singular approach to understanding disease did yield some useful therapeutics, it's now reached its limits. It hasn't helped much on the diagnostics (and early detection) front, either.
That's where a systems approach to bio comes in, drawing on machine learning techniques as well as a sort of "Moore's Law" for genomics that's driving costs down, and fast. We're now focusing on the 99% of the genome that hasn't really been understood yet in terms of how they affect the human body and disease. But what will it take for such an approach to succeed? For one thing, it involves building an applications layer on top of the sequencing layer -- so can we borrow lessons from how the computing industry (from chips to apps) evolved here? What are some of the constraints unique to the healthcare system?
In this episode of the a16z Podcast, Freenome CEO and co-founder Gabriel Otte and a16z bio fund partners Vijay Pande and Malinka Walaliyadde (in conversation with Sonal Chokshi) talk all things genomics and disease from science to business, also covering recent news like Illumina to what's next beyond human genomics to future trends. Including what the ultimate, Elysium-like magical diagnostic machine is (hint: the magical is mundane!).
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