[MUSIC PLAYING]
JUAN ENRIQUEZ: -- that I changed my slides yesterday.
But as you're thinking about this stuff, and as you solve
for an x in life code.
What I really want to do over the next 12 minutes is talk
about a big X and then a little x.
And the big X, sometimes it looks like this, right?
First-point contact transistor.
And as you're asking yourself, OK, what does this little
device solve or change?
Or maybe a better question on this is, what didn't this
solve or change?
So as you're thinking of solving for x with a
transistor, you might ask the same question with this
particular diagram.
So first note on the Internet, Robert's drawing.
And again, ask yourself, what did this solve, if you were
solving for x?
And of course, that also asks, what didn't it solve?
So some of these things are just so small and turn into
something so big that it gets really interesting, and it
becomes really difficult to answer the question, how do
you solve for x in this stuff?
And that means that a few discoveries become so large
that they solve not for one x but for multiple x's.
And that's what makes this area so interesting.
The argument I'm going to make is that when you take a
diagram like this, and you begin to talk about synthetic
biology, it actually becomes something
that looks like this.
The overlap, the Venn diagram, just becomes huge and really,
unintended consequences as you go forward.
So in this context, let's take a look
at this little creature.
So this is the world's first fully programmable cell.
And some people thought that was a reasonably big deal.
In fact, some people thought that was the science discovery
of the year.
And that came out of a discussion in a bar, Landini
Brothers in Virginia.
And on about the fourth scotch, Craig Venter, Ham
Smith, Dave Kiernan and I said, wouldn't it be really
cool if we could program a cell from scratch, much as you
program a chip on a computer?
And four years and $30 million later, that
little baby's born.
And then the question becomes, OK, so how did
you make this thing?
And basically, you take four jars of chemicals.
And you have these little robot arms, and then they
assemble like LEGO blocks--
100 nucleotide base pair pieces--
and then you start playing March Madness.
So you just take this bracket and you tie it to this bracket
and you tie it to this bracket.
And you go from 1,000 base pair cassettes to 10,000 to
100,000 to a million, and you get the world's largest
organic molecule.
And then all you have to do is you have to come up with a
technology so it doesn't break when you manipulate it.
Then a technology to put it into a cell, and then a
technology for the cell to boot it.
But other than that, it was easy.
Here's the impact of this stuff.
And what's really interesting is you're getting high school
kids and college kids thinking about, how do you begin to
assemble simple, standard, interchangeable parts in
living cells?
And if you can do that, that is a big deal.
So as you're thinking about the consequences that you're
beginning to get these early menus, and you're beginning to
get off-the- shelf parts-- call it a
Radio Shack for biology--
where you can get cell death or conjugation or mortality or
odor production or sensing.
And you're simply going into the little Radio Shack and
saying, I want some of this, and I want some of this, and I
want some of this, and I want to plug it in.
And really, what it means is that biology is just about the
stage where we all started playing with things
that look like this.
So that's about where we are in synthetic
biology and cell growth.
So you can make the light.
You can make the buzzer.
You can make this.
You can make that, et cetera.
It's not really sophisticated.
You're not at the computer.
You don't have the Intel chip yet.
But boy, do these systems move fast once they start
to solve for x.
And as you're thinking of the consequences of this stuff,
here's 2007.
And by 2007 at iGEM, you're already beginning to have this
Venn diagram overlap between electronics and biology.
So you're beginning to build divide-by-two circuits and
switches and print laser construction and wave
patterns, all the stuff that you used to do in Tripoli.
The interesting thing in this system is, here is an article
that was published in October, 2011.
And basically, you're beginning to be able to do
these things and construct these things on
a predictable basis.
And again, that's a big deal, because if you move from
one-offs to predictable, simple, systems that are
modular, then you can begin to really think about building
these fabs in an interesting way.
You can take very simple components, like this Tokyo
Tech iGEM project and say, OK, what we're going to do is
we're going to engineer cells so they basically oxygenate.
We're going to do it so they don't freeze.
We're going to do it so they don't sunburn.
And that's not terribly complicated.
In genetic engineering terms, people kind of
went, yeah, so what?
Except that if you do that, and you put it in a rocket,
and you shoot it at the Martian ice caps, it lands on
the ice caps, it begins to create an oxygen atmosphere.
And what they want to do is they want to
terraform the planet.
Just mildly ambitious.
And as you're thinking about these components, there's two
really big differences between what's going on in digital
code and what's going on in life code.
The first thing is these systems were moving in
parallel for years.
So you've got Moore's law up there in white.
You've got cost of base pairs or the cost of genome or the
base pairs generated, whatever measure you want.
And they're moving along, and they're happy, and they're
toying with each other.
And then something happened in the summer of 2007.
And this thing just dropped off a cliff.
Really different system.
Really big divergence.
Let me give you an order of magnitude of this divergence.
So cost of sequencing a genome goes from about 9 million
bucks to $10,000.
That's a drop of 800 times.
Let's just put that in digital terms. Computer costs over the
same period fell by four.
So what's the impact of the stuff?
We're now generating life code about three times faster than
we can build computers to store it or build
pipes to ship it.
And there's going to be a crossover point here where
there's going to be some very, very large companies, because
we haven't had to ask and answer the question, can we
build fast enough to store it or transport it or triage it?
And that digital world and that life science world is
going to be a really interesting intersection.
Here is a second big difference.
No matter how you program a computer, you will not have
1,000 computers when you come down in the morning.
It is a big deal to have software
make its own hardware.
It means the system scales at a different speed.
That's what makes this system so interesting, right?
It's programmable, standardizable, it's moving a
hell of a lot faster than the digital revolution, and it
scales fast.
So you can go to a little startup like Exxon Mobile and
say, what would you like me to program?
For some reason, they chose fuels.
Imagine that.
So we started programming in San Diego algae to make fuels.
And then we started scaling, and this is what our little
greenhouse looks like.
And now we want to become modest little farmers.
We want to build a farm that eventually looks like this.
Now, the interesting thing is, if you can program a cell on a
simple, standardizable basis, you can make fuels, or you can
make textiles, or you can make petrochemical derivatives, or
you can go out to Novartis and make a vaccine and take care
of the problems of contagion in about two weeks
manufacturing a vaccine for everyone in the United States
from sequence.
And as you think of the consequences of this stuff,
how we make stuff, where we make stuff, is going to
absolutely fundamentally change to the point where
DuPont today is getting 40% of its earnings from
life science products.
This is not just little startups in the People's
Republic of Cambridge.
So that's the big X. Now let's talk for one second about a
second, smaller x, which is traffic jams. Back in the '80s
when the Beltway used to move, there was this one place where
the Beltway backed up every single day.
It drove the traffic engineers absolutely crazy.
Why in the hell is it backing up right here?
And they looked, and they studied, and everything else.
And then they got a letter that said, there's this one
idiot who gets on the same time, same place, left lane
55, every day.
Letter gets sent to the Washington Post. Two days
later, another letter appears.
Hi, my name's John Nestor.
I'm the person who does that.
It's easier for me to drive on the left.
I don't have to change.
And the law says 55.
Anybody else wants to speed, that's their
problem, not mine.
That so outraged people that you had a verb in Washington
called Nestoring.
Guess where John Nestor worked?
He was in charge of renal and cardiac drugs.
And the letter said, the drug has to be safe or
it can't get approved.
It wasn't approved.
It turns out that John Nestor had an unassailable record of
protecting the public from harmful drugs, among other
things because over a couple of decades, he never approved
a single drug.
And it turns out he may have killed a lot of
people because of that.
And as you think about the consequences of that, that is
medicine's missing measure.
Because we think about, boy, this might harm somebody, but
there's something on the other side.
Here's what's happened to new drugs that you get per billion
dollars spent.
That's your little trend line.
In 1950 to 2010, huge technology advance, huge
databases, combinatorial chemistry, all this stuff, and
your output is just dropping through the floor.
Here's an order of magnitude.
This is like Moore's law in reverse.
And, by the way, this little x has a few consequences because
not only does it hurt personalized medicine, not
only does it hurt rolling out a lot of the stuff that we've
heard and will hear in this conference but it may break
the United States.
Because if there's one area that is absolutely driving the
deficit batshit, it's health care and life science and
regulatory costs.
And as you're thinking about this stuff, what it's done is
it's shifted pharma's focus from discovery to mergers and
marketing to the point where pharma's now spending about
twice as much on these ads that you see on the Super Bowl
as it is on R&D. And that's a real problem, because it makes
science moot.
As you're thinking about these systems, when a drug can jump
at $100 million, you get drugs for malaria, you get drugs for
TB, you get drugs for this, you get drugs for that.
You start playing Olympic at $300 million by '87, a bunch
of drugs don't come to market.
Forget about approval.
They simply don't get tested.
And today, at about $1.3 billion, not a lot
of people make that.
And we kill a lot of people because those drugs don't get
investigated, because they don't come to market.
But we don't measure that stuff.
Of course, some drugs do make the hurdle rate occasionally.
But they're fewer and fewer, and it doesn't work with
personalized medicine.
Here's a really interesting idea.
There is not a single instance of a congressional committee
asking why something was not done or approved.
So guess what message all these folks are getting?
Guess what happens if you don't measure?
Guess what happens if you have missing measurements as your
system, the digital system, begins to collide with the
life science system when you try and produce stuff?
So maybe the question we should be asking is, at some
point, by not acting, by being too careful, by making it too
expensive, maybe we're killing more people than the
regulations are saving.
And we may be doing that by an order or two of magnitude.
There's got to be a crossover point here, right?
I'm not saying eliminate all government.
I'm not saying eliminate all regulators.
I'm simply saying, measure the cost of not acting.
And as you're doing that, and you ask, what's
the crossover point?
We don't know.
We have no idea, because we don't measure that stuff.
So here's my moonshot.
Small one.
I want all of you to crowdsource medicine's missing
measurement.
What I want you to do is I want you to understand and
help me understand and measure the risk of
acting and not acting.
And very specifically, let's explicitly measure, what is
the cost of not acting, of not creating a medicine, of taking
12 years instead of 4 to bring a medicine to market in deaths
and disability, life years, et cetera?
And let's explicitly measure the costs of acting slowly so
that there is a pressure on university IRBs, oh my god,
I'm going to be embarrassed.
Let's take another year.
You're going to kill 4,000 people or 5,000 people if you
take another year.
Let's just make that explicit as you measure your
reputation.
Here's my desire.
Let's use all you math geniuses out there, and let's
use everybody in the world, to begin to create a relatively
simple Black-Scholes-like equation that begins to
measure for technology, and let's start with pharma, but
for other technologies, the risk-reward and regulatory
costs and put them on one sheet so that when something
comes in before Congress, there's a pressure on the
other side to act.
Thank you very much.
MALE SPEAKER: Let us define x.
x is a solution, a solution to a seemingly insurmountable
problem, like climate change or cancer, one
that affects the world.
But what if we redefine x as a challenge, an opportunity for
radical thinking, a chance to light up the world with
breakthrough ideas and cutting-edge technology, the
stuff of science fiction that just might fly after all.
Solving for x requires wonder and imagination and a vision
to build seemingly impossible solutions to the world's
biggest problems. Solve for x.
Moonshot thinking.