Translator: Leonardo Silva Reviewer: hila scherba
On September 11th, 2005,
in one step, I fundamentally changed every part of my life.
I got on an airplane out of Washington DC
and left the company that I had founded in the music industry
to shift fields, shift countries and shift continents
and move into trying to understand biology.
Now, on the one hand, what do music and biology have in common?
Well, it turns out that both of them are incredibly complex things
that get to the heart of who we are as a species.
A musical performance is a complex interplay
between a composer who figures out a piece of music,
the band that performs it,
and of course the audience that resonates with it.
It's the environment and the fundamental message within it.
We intuitively understand that, at least in music,
it's possible to understand what a composition is,
and to describe it effectively.
In music, you have notes that you can place on a staff,
and, in many respects, this is more of a mathematical construct.
It's telling you that there's some sound happening at a particular frequency,
at a particular point in time.
Fundamentally, in this perspective, music is just a series of attributes
describing what's happening at a point in time and how that changes.
So, besides notes,
you can actually look into the actual mechanics of the sound
and build mathematical models based on how humans actually listen to sound,
which, I might add, is a part of biology that we do understand
and is used to drive the mp3 players and iPods
that you all have used in your life.
But biology and sound, they're very different things.
And yet, at its heart, both of them are information.
This is DNA, which I'm sure many of you have seen before, rendered,
and it's, at the same time, a physical object and an information construct.
It's one of the most amazing molecules in the world.
Everything that's alive today exists
because this can do both physical actions and carry information.
And, thanks to all the other technologies that have been moving along,
everything from automation and the ability to both read and write DNA,
we can sit now at a laptop, design a strand of DNA, hit print,
and receive that in the post several weeks later.
So, it's fundamentally altering [how] we look at biology,
through the lens of being able to change it rather than just observe it.
Biology, of course, is more than the context
of just the information that's encoded in this DNA.
The DNA is itself read by a whole host of machines, at the nanoscale.
Here's an example.
Those base pairs in the DNA are translated into amino acids,
which is an alphabet of over 20 different molecules
that get strung together to form proteins.
These proteins themselves are then helped to fold into a three-dimensional shape,
almost like a giant jigsaw puzzle, or Legos,
and, ultimately, from that shape, it drives what it does.
Pretty pictures help. I'll explain this a little bit.
This is an enzyme that's used in central metabolism for bacteria,
and it helps turn sugars into the basic building blocks
to make more copies of itself.
Ultimately, though,
this direct relationship between the DNA into a physical object
that has function as a result of its shape
is what makes biology something that you can understand and engineer,
albeit with many caveats.
The first is: biology is fundamentally about context.
These are both examples of the same protein.
The same strand of DNA told a cell what to make,
but changing the physical context of where it is, changing its environment,
fundamentally alters its shape and its function.
And biology uses tricks like this all over the place.
It's an incredibly complex network,
where all of these enzymes, interacting with each other,
in large collections referred to as pathways,
are communicating,
are doing things like building, say, a fragrance product
that you would use in your perfume,
or building the constituents that are the food to all of us.
Biology has made almost everything that we use in our life around us,
and, as such, understanding this
is more about understanding how to, potentially, by changing DNA,
change fundamentally how these things are manufactured.
But, as I said, it isn't only about the DNA.
These are all examples of the same background string, a fungus.
We're just changing what it's fed.
Just a single part of its environment radically alters
what it does to respond to it,
and you can see different outcomes in terms of what it looks like
and, more importantly,
radically different outcomes in terms of what it actually makes.
And, if you're trying to turn biology into a technology,
into something that you can engineer with,
you need to understand why it does what it does.
So, fundamentally, these pathways that make up an organism,
it's almost like a subway map,
and you can find the ways that the information has routed through it,
and it's a function, again, of the DNA that this organism was written with.
It's how everything operates and how a bacteria sees the world,
like the E. coli, that make up most of the cells, by count, in your body.
And it gives you an understanding
that different organisms have different pathways,
they have different pieces of functionality,
different amounts of complexity to them.
Not all living things are the same.
This is the network map that describes
how central metabolism works in just a bacteria.
And, as a point of comparison,
this is what it is compared to humans.
So, we're not going to get to one step of suddenly engineering ourselves.
Bacteria still have plenty of use,
and are more interactive where the current technology is.
But, on the other hand, we've handled complexity like this before.
The first transistor was something about this big,
and it was only in 20 years on
before you started to be able to make anything useful out of it.
And yet, if I did a similar superposition of what a microprocessor looked like
from the 1970s to the modern ones that drive your cell phones
and drive your computers,
you'd see that we've had to understand complexity
on such an order of magnitude difference already,
and achieved it,
albeit at the cost of trillions of dollars of investment.
But the fundamental ability for us to read and write DNA
to do the basic operations that allow us to perhaps change a living organism,
to help better understand how it works,
are falling far faster than we ever did in our innovations in transistors.
And these are basically the cost, per base pair, to read and write DNA,
often referred to as the Carlson Curves,
which is helping give you an understanding
of when you can suddenly talk about engineering a human,
as opposed to only a bacteria.
This is of course driven by innovations like benchtop DNA sequencing,
where, while the first genome was $ 1.5 billion to do,
now can be done by new machines like this one
for $ 1,000 in an afternoon.
And the same technologies that allow you to print rapidly at your desk
are now used to be able to print DNA.
So, the convergence that's been happening
across all the various strands of technology is making this all possible.
So, just how complex is this? You know, how do we engineer biology?
Isn't that a little hubristic?
Well, luckily, we've been doing it for millennia.
Any one of you who've ever enjoyed a beer
is taking advantage of the fact that we can engineer with biology.
But it's already handling huge amounts of complexity
in how we're doing this.
Just a few of the things that may end up changing what a beer is doing:
everything from temperatures that it's done with,
what yeast you're using, what you're brewing with.
And, very quickly, you start to realize
that it's into the dozens of different factors,
hundreds of unkowns,
and what things are going to cross-react with other things,
because biology is robust:
you pull on one part and something else moves at the same time.
So, how do you build a framework
that allows you to start to understand that complexity?
That puts a road map as you would,
the same way that you have from the semiconductor industry,
to understand when we can do these things?
Well, it turns out that the starting point is changing how we do science.
Most of you probably were taught to investigate things
by only looking at one thing at a time.
You're going to fix all of your variables and just investigate, say,
how temperature is going to change the quality of my beer.
So, you'll just set up an experiment
where maybe you take five data points and its biology.
So, there's lots of noise, and lots of things can go wrong.
And you might do it in triplicate, getting lots of data about this.
And, there is your data.
You plot the line and say, "Alright, the best beer was right there."
So, now let's take a look at how some other factor -
like what else I have put into this - is influencing it.
And I'll take what I figured was the way temperature operated,
and go and run experiments the same way I did before,
you know, the next week.
And I'll build a model out of that.
Reality, however, is that things are much more complicated.
Things cross-react. We have words for that, like "synergy."
And so, what may actually be happening
is the things that you're investigating may be pulling on each other.
So, taking advantage of modern statistics -
and the same techniques for shifting around in biology
tell us what it's doing, that are used to optimize manufacturing processes -
means that you can look at all of that complexity at the same time.
If, instead, you set up your experiment
by investigating how everything interacts with everything,
in one shot, your one experiment tells you about the entire system,
in that particular investigative context.
And, because you've set it up in a simple case with three factors,
it's a series of comparison with cubes.
So, you can end up comparing how results happen
as a function of, say, timing and temperature,
or say, timing and pH.
It gives you an enormous amount of information about what's going on,
and information is the way you can navigate complexity.
Now, another important point
is that you don't actually need to do every one of those experiments,
because you've got so much information,
you know more about what's going on,
you understand the basics of perhaps how an organism is operating.
And so, you can take advantage of that
to pare it down, to learn from what you already knew.
And so, it allows you to investigate these extremely complex problems
in a very rapid manner, in a very efficient way.
Because, when you're talking about something like growing a plant,
where it takes perhaps several years to do a single experiment,
you need to ensure you learned everything you could in that one run.
And the tools that are enabling us to analyze this -
these experiments can only really be explained using software -
are also tied into the rest of the tool set,
the automation that allows us to run these experiments
without humans being involved.
The design tools: what does the design CAD tool
that allows you to build a living thing look like?
Those are being invented right now.
And it's technologies like microfluidics,
where now you can grow bacteria inside little picodroplets,
millions of them at a time,
and individually trace their phase.
So, you're investigating
the mega dimensional space of biological context in parallel,
and you can actually embrace the complexity that is in biology.
And, of course, high-throughput lab automation means
that you can do these incredibly intricate experiments,
that, to be frank, would drive a human insane.
And it also means that software itself can drive that.
Scaled-down models, everything from bioreactors that can be run in parallel
to investigate how the environment is influencing a microorganism
through two entirely parallel and automated production infrastructures,
now are driving how we make the drugs that we use to treat our illnesses,
and many of the food additives or fragrances
that we use in products every day.
One of the most naive short-term applications
is just to make chemicals
that are already used in the world around us,
which - well, behind the scenes,
it's the fabrics that you wear, it's the dyes in the clothes,
it is the things that make your food taste good.
What's really going to be interesting
is when we can apply it to problems that we haven't been able to solve.
This is the mosquito, and it's actually the most deadly organism on the planet.
Every year, from malaria and dengue fever,
it ends up killing more people than almost any other disorder out there.
And yet, the way we treat it
is by going around and spraying pesticides, broad-spectrum,
that are killing many other organisms and are known carcinogens.
In comparison, applying synthetic biology,
there's a UK company, Oxitec, that has developed sterile mosquitoes.
And so, by releasing them,
the mosquitoes are able to take an entire generation out of the population cycle,
because they don't have any viable offspring.
There's no issue of accidentally killing other organisms,
there's no issue of pesticides leaching into the environment.
It simply deals with the problem directly.
And it's already working in the real world,
with a 96% knock-down rate in Brazil.
Or you can start talking about
how you can build organisms that coexist within us.
Another UK company, Prokaryon,
has developed a mechanism for delivering vaccines orally
by taking vaccinella,
which is a bacteria now designed to coexist with you,
and giving it the ability to present the shape of something
that our immune system needs to learn to defend against.
So, by swallowing a simple pill,
without necessarily having to have a cold chain,
you have the ability to have an organism coexisting in you,
providing immunity against diseases.
And, in their case, they're leading this with travelers' diarrhea,
which sadly still kills more people than any other disorder on this planet.
Or, thinking even bigger,
there's reasons why we don't tour abattoirs.
The meat industry, as well as things like leather goods
and other processed components from animals,
isn't something that most people really like to look at.
On the other hand, what if you could make leather right out of a fermenter?
What if you could have a really good steak
and it was just meat that was coming out of a vat?
That is already happening today with companies like Modern Meadow,
taking advantage of genetic engineering here and synthetic biology
to change fundamentally what you think of "coming from biology."
One of the best forces out there to help understand all of this, though,
has been one of the international communities called iGEM.
That's the International Genetically Engineered Machines competition,
originally founded out of MIT, but now a non-profit,
which helps every year organize a competition,
at now the high school, undergraduate and postgraduate level,
to envision what you can do with biology.
And, over the course of a summer, people are doing amazing things.
Last year, in 2012,
a team from University College London
built a bacteria that was able to digest plastic
as a solution to plastic pollution in the ocean,
and it worked.
Now, this was a team of undergraduates taking advantage of the new tools,
the new way of thinking that's coming with this discipline,
over the course of three months,
talking about how to solve Grand Challenges.
Now, understanding the full context of that,
what happens when you release this into the world?
Do all your pipes suddenly get digested?
It means you don't actually see this in the world yet,
and understanding the full consequence of these actions
is only going to come from all of us envisioning what can be done
when biology becomes a technology.
Thank you.
(Applause)