The next software revolution: programming biological cells | Sara-Jane Dunn

The second half of the last century was completely defined

by a technological revolution:

the software revolution.

The ability to program electrons on a material called silicon

made possible technologies, companies and industries

that were at one point unimaginable to many of us,

but which have now fundamentally changed the way the world works.

The first half of this century, though,

is going to be transformed by a new software revolution:

the living software revolution.

And this will be powered by the ability to program biochemistry

on a material called biology.

And doing so will enable us to harness the properties of biology

to generate new kinds of therapies,

to repair damaged tissue,

to reprogram faulty cells

or even build programmable operating systems out of biochemistry.

If we can realize this -- and we do need to realize it --

its impact will be so enormous

that it will make the first software revolution pale in comparison.

And that's because living software would transform the entirety of medicine,

agriculture and energy,

and these are sectors that dwarf those dominated by IT.

Imagine programmable plants that fix nitrogen more effectively

or resist emerging fungal pathogens,

or even programming crops to be perennial rather than annual

so you could double your crop yields each year.

That would transform agriculture

and how we'll keep our growing and global population fed.

Or imagine programmable immunity,

designing and harnessing molecular devices that guide your immune system

to detect, eradicate or even prevent disease.

This would transform medicine

and how we'll keep our growing and aging population healthy.

We already have many of the tools that will make living software a reality.

We can precisely edit genes with CRISPR.

We can rewrite the genetic code one base at a time.

We can even build functioning synthetic circuits out of DNA.

But figuring out how and when to wield these tools

is still a process of trial and error.

It needs deep expertise, years of specialization.

And experimental protocols are difficult to discover

and all too often, difficult to reproduce.

And, you know, we have a tendency in biology to focus a lot on the parts,

but we all know that something like flying wouldn't be understood

by only studying feathers.

So programming biology is not yet as simple as programming your computer.

And then to make matters worse,

living systems largely bear no resemblance to the engineered systems

that you and I program every day.

In contrast to engineered systems, living systems self-generate,

they self-organize,

they operate at molecular scales.

And these molecular-level interactions

lead generally to robust macro-scale output.

They can even self-repair.

Consider, for example, the humble household plant,

like that one sat on your mantelpiece at home

that you keep forgetting to water.

Every day, despite your neglect, that plant has to wake up

and figure out how to allocate its resources.

Will it grow, photosynthesize, produce seeds, or flower?

And that's a decision that has to be made at the level of the whole organism.

But a plant doesn't have a brain to figure all of that out.

It has to make do with the cells on its leaves.

They have to respond to the environment

and make the decisions that affect the whole plant.

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 will do.

And then those programs must operate in a distributed way

across individual cells,

so that they can coordinate and that plant can grow and flourish.

If we could understand these biological programs,

if we could understand biological computation,

it would transform our ability to understand how and why

cells do what they do.

Because, if we understood these programs,

we could debug them when things go wrong.

Or we could learn from them how to design the kind of synthetic circuits

that truly exploit the computational power of biochemistry.

My passion about this idea led me to a career in research

at the interface of maths, computer science and biology.

And in my work, I focus on the concept of biology as computation.

And that means asking what do cells compute,

and how can we uncover these biological programs?

And I started to ask these questions together with some brilliant collaborators

at Microsoft Research and the University of Cambridge,

where together we wanted to understand

the biological program running inside a unique type of cell:

an embryonic stem cell.

These cells are unique because they're totally naïve.

They can become anything they want:

a brain cell, a heart cell, a bone cell, a lung cell,

any adult cell type.

This naïvety, it sets them apart,

but it also ignited the imagination of the scientific community,

who realized, if we could tap into that potential,

we would have a powerful tool for medicine.

If we could figure out how these cells make the decision

to become one cell type or another,

we might be able to harness them

to generate cells that we need to repair diseased or damaged tissue.

But realizing that vision is not without its challenges,

not least because these particular cells,

they emerge just six days after conception.

And then within a day or so, they're gone.

They have set off down the different paths

that form all the structures and organs of your adult body.

But it turns out that cell fates are a lot more plastic

than we might have imagined.

About 13 years ago, some scientists showed something truly revolutionary.

By inserting just a handful of genes into an adult cell,

like one of your skin cells,

you can transform that cell back to the naïve state.

And it's a process that's actually known as "reprogramming,"

and it allows us to imagine a kind of stem cell utopia,

the ability to take a sample of a patient's own cells,

transform them back to the naïve state

and use those cells to make whatever that patient might need,

whether it's brain cells or heart cells.

But over the last decade or so,

figuring out how to change cell fate,

it's still a process of trial and error.

Even in cases where we've uncovered successful experimental protocols,

they're still inefficient,

and we lack a fundamental understanding of how and why they work.

If you figured out how to change a stem cell into a heart cell,

that hasn't got any way of telling you how to change a stem cell

into a brain cell.

So we wanted to understand the biological program

running inside an embryonic stem cell,

and understanding the computation performed by a living system

starts with asking a devastatingly simple question:

What is it that system actually has to do?

Now, computer science actually has a set of strategies

for dealing with what it is the software and hardware are meant to do.

When you write a program, you code a piece of software,

you want that software to run correctly.

You want performance, functionality.

You want to prevent bugs.

They can cost you a lot.

So when a developer writes a program,

they could write down a set of specifications.

These are what your program should do.

Maybe it should compare the size of two numbers

or order numbers by increasing size.

Technology exists that allows us automatically to check

whether our specifications are satisfied,

whether that program does what it should do.

And so our idea was that in the same way,

experimental observations, things we measure in the lab,

they correspond to specifications of what the biological program should do.

So we just needed to figure out a way

to encode this new type of specification.

So let's say you've been busy in the lab and you've been measuring your genes

and you've found that if Gene A is active,

then Gene B or Gene C seems to be active.

We can write that observation down as a mathematical expression

if we can use the language of logic:

If A, then B or C.

Now, this is a very simple example, OK.

It's just to illustrate the point.

We can encode truly rich expressions

that actually capture the behavior of multiple genes or proteins over time

across multiple different experiments.

And so by translating our observations

into mathematical expression in this way,

it becomes possible to test whether or not those observations can emerge

from a program of genetic interactions.

And we developed a tool to do just this.

We were able to use this tool to encode observations

as mathematical expressions,

and then that tool would allow us to uncover the genetic program

that could explain them all.

And we then apply this approach

to uncover the genetic program running inside embryonic stem cells

to see if we could understand how to induce that naïve state.

And this tool was actually built

on a solver that's deployed routinely around the world

for conventional software verification.

So we started with a set of nearly 50 different specifications

that we generated from experimental observations of embryonic stem cells.

And by encoding these observations in this tool,

we were able to uncover the first molecular program

that could explain all of them.

Now, that's kind of a feat in and of itself, right?

Being able to reconcile all of these different observations

is not the kind of thing you can do on the back of an envelope,

even if you have a really big envelope.

Because we've got this kind of understanding,

we could go one step further.

We could use this program to predict what this cell might do

in conditions we hadn't yet tested.

We could probe the program in silico.

And so we did just that:

we generated predictions that we tested in the lab,

and we found that this program was highly predictive.

It told us how we could accelerate progress

back to the naïve state quickly and efficiently.

It told us which genes to target to do that,

which genes might even hinder that process.

We even found the program predicted the order in which genes would switch on.

So this approach really allowed us to uncover the dynamics

of what the cells are doing.

What we've developed, it's not a method that's specific to stem cell biology.

Rather, it allows us to make sense of the computation

being carried out by the cell

in the context of genetic interactions.

So really, it's just one building block.

The field urgently needs to develop new approaches

to understand biological computation more broadly

and at different levels,

from DNA right through to the flow of information between cells.

Only this kind of transformative understanding

will enable us to harness biology in ways that are predictable and reliable.

But to program biology, we will also need to develop

the kinds of tools and languages

that allow both experimentalists and computational scientists

to design biological function

and have those designs compile down to the machine code of the cell,

its biochemistry,

so that we could then build those structures.

Now, that's something akin to a living software compiler,

and I'm proud to be part of a team at Microsoft

that's working to develop one.

Though to say it's a grand challenge is kind of an understatement,

but if it's realized,

it would be the final bridge between software and wetware.

More broadly, though, programming biology is only going to be possible

if we can transform the field into being truly interdisciplinary.

It needs us to bridge the physical and the life sciences,

and scientists from each of these disciplines

need to be able to work together with common languages

and to have shared scientific questions.

In the long term, it's worth remembering that many of the giant software companies

and the technology that you and I work with every day

could hardly have been imagined

at the time we first started programming on silicon microchips.

And if we start now to think about the potential for technology

enabled by computational biology,

we'll see some of the steps that we need to take along the way

to make that a reality.

Now, there is the sobering thought that this kind of technology

could be open to misuse.

If we're willing to talk about the potential

for programming immune cells,

we should also be thinking about the potential of bacteria

engineered to evade them.

There might be people willing to do that.

Now, one reassuring thought in this

is that -- well, less so for the scientists --

is that biology is a fragile thing to work with.

So programming biology is not going to be something

you'll be doing in your garden shed.

But because we're at the outset of this,

we can move forward with our eyes wide open.

We can ask the difficult questions up front,

we can put in place the necessary safeguards

and, as part of that, we'll have to think about our ethics.

We'll have to think about putting bounds on the implementation

of biological function.

So as part of this, research in bioethics will have to be a priority.

It can't be relegated to second place

in the excitement of scientific innovation.

But the ultimate prize, the ultimate destination on this journey,

would be breakthrough applications and breakthrough industries

in areas from agriculture and medicine to energy and materials

and even computing itself.

Imagine, one day we could be powering the planet sustainably

on the ultimate green energy

if we could mimic something that plants figured out millennia ago:

how to harness the sun's energy with an efficiency that is unparalleled

by our current solar cells.

If we understood that program of quantum interactions

that allow plants to absorb sunlight so efficiently,

we might be able to translate that into building synthetic DNA circuits

that offer the material for better solar cells.

There are teams and scientists working on the fundamentals of this right now,

so perhaps if it got the right attention and the right investment,

it could be realized in 10 or 15 years.

So we are at the beginning of a technological revolution.

Understanding this ancient type of biological computation

is the critical first step.

And if we can realize this,

we would enter in the era of an operating system

that runs living software.

Thank you very much.

(Applause)