a16z Podcast: On the Genomics of Disease, From Science to Business
October 19, 2016 13:41
00:00 / 30:02
1 plays
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!). Read more
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!). Read more
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!).
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!).
