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We're at the beginning of a new technological revolution.
It's a revolution that should allow us to program our body
to fight diseases that currently evade our defenses,
to program crops to produce more food to feed a hungry planet,
and to develop new strategies to power
our world in sustainable climate friendly ways.
And the big news, it might be possible sooner than you think.
Innovation hinges on our ability to see things differently,
breaking boundaries, and looking between the lines in
an effort to solve some of the world's toughest challenges.
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The last technological revolution, the software revolution,
was defined by our ability to encode ones and zeros on silicon.
But the next revolution won't be about ones and zeros,
It will be about our ability to encode As,
Gs, Cs, and Ts,
the building blocks of DNA.
[MUSIC]
As a scientist who studies biological computation at Microsoft,
I work at the intersection of mathematics,
computer science, and biology.
Everywhere I look, I see cells operating as little computers.
For example, consider the immune cells
that patrol your body day in day out.
These cells have to solve the problem of fighting disease and
infection without fighting against the body itself,
and that's an information processing problem.
So somehow, there must be a program running inside these cells,
a program that responds to input signals and cues,
and shapes what that cell would do,
a program of dynamic interactions at a molecular scale.
And these programs must operate in
a distributed way across your immune system,
coordinating and sharing information to protect your body.
You can think of this as living software.
If we could figure out
the biological programs that run inside cells,
like those in our immune system,
it would transform our ability to understand
how and why cells do what they do.
Because if we truly understood these biological programs,
then we could debug them when things go wrong.
And that means we could do truly wonderful things.
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For example, what if we could
program ourselves to detect, eradicate,
or even prevent disease better than we can
now or even replace damaged tissue?
That would mean living longer healthier lives.
Or what if we could program crops to resist
fungal pathogens or even to bear fruit more often?
That would enable us to feed a hungry planet.
Or what if we could figure out how to harness
quantum effects to capture
energy from the sun in truly efficient ways,
more efficient than our current solar panels?
That would be the ultimate green energy?
It's hard not to get excited about potential outcomes like this.
And while there are some common misperceptions and
genuine ethical concerns about the science here,
the reality is this,
programming biology has the potential to transform medicine,
agriculture, and energy, and
many other industries on a global scale.
Scientists have developed CRISPR technology that
allows us to precisely target and edit problem genes.
With base editing, we can rewrite DNA strands one base at a time.
We can even build functioning synthetic circuits out of DNA.
But figuring out how to use
these tools is largely driven by trial and error.
Developing robust experimental protocols is difficult,
and reproducing consistent and scalable results is a huge hurdle.
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At Microsoft, I'm part of a dedicated team called Station B,
that's working to develop an integrated system
that will allow us to program biology more effectively.
One promising area for
programming biology is the field of regenerative medicine.
Thirteen years ago, scientists
discovered something truly revolutionary,
that it's possible to take your adult cells,
like your skin cells and reprogram them
back to this embryonic stem-like state.
That was an enormous Nobel Prize winning discovery.
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Because embryonic stem cells are special.
They are the starting point of all of
the different cells in your adult body,
whether it's your heart, your lungs,
your brain, your muscles.
All of your adult cells can be traced back to this starting point.
Now, we can access this potency from your adult cells.
That means, we might be able to harness the potential of
embryonic stem cells to make
revolutionary kinds of treatments to repair damaged tissue.
What if we could help
an Alzheimer's patient to grow new brain cells?
To be able to realize this vision,
we need to figure out how to program these cells.
And that means asking first,
what do these cells compute?
And how can we go about uncovering these biological programs?
Understanding the computation performed by
a living system starts with a devastatingly simple question.
What is it that system actually has to do?
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So our idea was this,
observations of cellular behavior
specify what it is the biological program should do.
So if we can translate what we see happening in
the lab into computational and mathematical formulae,
then we can test just as though we were debugging software,
whether it's possible to explain how
those observations emerge from a program of genetic interactions.
It's raining. Okay, well, you can't program the weather.
We developed a tool to do just this,
one based on a Microsoft design solver that's used
routinely around the world for conventional software verification.
And we used it to uncover the cell's internal genetic program.
But we took it one step further.
We use this program to accurately predict how to
accelerate progress back to
that stem-like state quickly and efficiently.
We uncovered which genes were critical,
which might hinder it,
how individual genes would switch on and off over time.
This is an important breakthrough.
We developed a computer model of
this system that could successfully
tell us in advance what would
otherwise have taken years to discover.
But this is only one step towards programming biology.
We need to develop new ways to
uncover biological computation more broadly.
From how information is encoded in the DNA molecules themselves,
right through to how information flows between cells.
Ultimately, we will need to develop tools that
allow us to design biological function,
and then map those designs to
structures that we can assemble in the lab.
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So let's say that we could design a genetic circuit that would
allow a cell to respond to a new kind of input,
maybe to detect a toxin.
Then the question is how you would build that circuit.
What pieces of DNA would you need to assemble?
This is where another analogy from
conventional computing comes in useful.
High-level programming languages allow
humans to write code in a language that they understand.
Nobody actually codes in ones and zeros.
There are compilers that allow us to
translate our high-level languages
down to the machine code that
the computer can interpret to run that program.
And we need the biological equivalent.
We need a living software compiler.
And with that, we could compile down to the Gs, Cs, As,
and Ts of DNA that we would use to assemble that circuit.
Along with this, we need ways to
insert these genetic circuit into cells,
to run lab experiments reliably and automatically,
and then learn from the data that's produced.
We need an integrated system for programming biology.
In fact, this system is the next big tool that we're
working on to develop at Station B at Microsoft.
We're bringing decades of experience in
programming computational systems to help us develop it.
And in fact, we've already made tremendous strides.
We developed biological programming languages that
allow us to encode our designs for genetic circuits.
Our tools allow us to compile these designs down to the DNA code,
and then to automatically run our experiments in the lab.
The experiments around our lab robots,
then we pull the data from those experiments and
store it in a Microsoft cloud knowledge base.
As we perform new experiments,
our knowledge base is continuously updated by automated learning.
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There's still a lot of work ahead of us.
The biggest challenge remains understanding
yet to be sorted elements of biology well enough,
that we can condense the processes we want to
emulate into circuits that we can design and build.
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Now, every technology is a tool that even when used for good,
can have unintended consequences.
And the same is true here.
As I mentioned earlier,
there are important ethical concerns that must be confronted.
And we are at the start of this new software revolution.
It's going to require deep consideration,
and it's not something that we would ever relegate to
second place in our ongoing quest for scientific innovation.
We can only move forward with our eyes wide
open and in partnership with the scientific community.
Every organization in this collaboration is deeply
committed to putting the necessary safeguards in place,
and asking the difficult questions upfront.
Once we have addressed the ethical concerns,
and once we fully understand biological computation,
that everything I said earlier is possible and more.
Rebuilding organs for transplantation,
self-organizing and self-repairing materials,
chemicals and fertilizers that allow us
to leave fossil fuels in the ground.
A future made possible by the living software revolution.
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