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The second half of the last century
was completely defined

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by a technological revolution:

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the software revolution.

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The ability to program electrons
on a material called silicon

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made possible technologies,
companies and industries

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that were at one point
unimaginable to many of us,

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

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The first half of this century, though,

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is going to be transformed
by a new software revolution:

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the living software revolution.

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And this will be powered by the ability
to program biochemistry

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on a material called biology.

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

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to generate new kinds of therapies,

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to repair damaged tissue,

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to reprogram faulty cells

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or even build programmable
operating systems out of biochemistry.

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

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its impact will be so enormous

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

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

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agriculture and energy,

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and these are sectors that dwarf
those dominated by IT.

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Imagine programmable plants
that fix nitrogen more effectively

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or resist emerging fungal pathogens,

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or even programming crops
to be perennial rather than annual

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so you could double
your crop yields each year.

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That would transform agriculture

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

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Or imagine programmable immunity,

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designing and harnessing molecular devices
that guide your immune system

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to detect, eradicate
or even prevent disease.

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This would transform medicine

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

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

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We can precisely edit genes with CRISPR.

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

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We can even build functioning
synthetic circuits out of DNA.

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But figuring out how and when
to wield these tools

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is still a process of trial and error.

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It needs deep expertise,
years of specialization.

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And experimental protocols
are difficult to discover

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and all too often, difficult to reproduce.

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

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

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by only studying feathers.

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

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And then to make matters worse,

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living systems largely bear no resemblance
to the engineered systems

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that you and I program every day.

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

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they self-organize,

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they operate at molecular scales.

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And these molecular-level interactions

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lead generally to robust
macro-scale output.

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They can even self-repair.

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Consider, for example,
the humble household plant,

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like that one sat
on your mantelpiece at home

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that you keep forgetting to water.

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

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and figure out how
to allocate its resources.

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

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

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

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

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They have to respond to the environment

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and make the decisions
that affect the whole plant.

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So somehow there must be a program
running inside these cells,

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a program that responds
to input signals and cues

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and shapes what that cell will do.

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And then those programs must operate
in a distributed way

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across individual cells,

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

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If we could understand
these biological programs,

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if we could understand
biological computation,

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it would transform our ability
to understand how and why

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cells do what they do.

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Because, if we understood these programs,

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we could debug them when things go wrong.

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

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that truly exploit
the computational power of biochemistry.

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

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at the interface of maths,
computer science and biology.

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

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And that means asking
what do cells compute,

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and how can we uncover
these biological programs?

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

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at Microsoft Research
and the University of Cambridge,

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where together we wanted to understand

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the biological program
running inside a unique type of cell:

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an embryonic stem cell.

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These cells are unique
because they're totally naïve.

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They can become anything they want:

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

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any adult cell type.

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This naïvety, it sets them apart,

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but it also ignited the imagination
of the scientific community,

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who realized, if we could
tap into that potential,

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we would have a powerful
tool for medicine.

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If we could figure out
how these cells make the decision

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to become one cell type or another,

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we might be able to harness them

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

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But realizing that vision
is not without its challenges,

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not least because these particular cells,

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they emerge just six days
after conception.

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

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They have set off down the different paths

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

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

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than we might have imagined.

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

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

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like one of your skin cells,

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you can transform that cell
back to the naïve state.

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

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

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

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transform them back to the naïve state

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

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whether it's brain cells or heart cells.

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But over the last decade or so,

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figuring out how to change cell fate,

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it's still a process of trial and error.

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

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they're still inefficient,

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

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

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

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into a brain cell.

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So we wanted to understand
the biological program

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running inside an embryonic stem cell,

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and understanding the computation
performed by a living system

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starts with asking
a devastatingly simple question:

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What is it that system actually has to do?

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Now, computer science actually
has a set of strategies

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

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

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you want that software to run correctly.

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You want performance, functionality.

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You want to prevent bugs.

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They can cost you a lot.

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So when a developer writes a program,

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they could write down
a set of specifications.

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These are what your program should do.

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Maybe it should compare
the size of two numbers

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or order numbers by increasing size.

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Technology exists that allows us
automatically to check

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whether our specifications are satisfied,

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whether that program
does what it should do.

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And so our idea was that in the same way,

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experimental observations,
things we measure in the lab,

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

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So we just needed to figure out a way

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to encode this new type of specification.

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

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

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then Gene B or Gene C seems to be active.

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We can write that observation down
as a mathematical expression

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if we can use the language of logic:

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If A, then B or C.

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Now, this is a very simple example, OK.

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It's just to illustrate the point.

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We can encode truly rich expressions

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

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across multiple different experiments.

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And so by translating our observations

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into mathematical expression in this way,

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

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from a program of genetic interactions.

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And we developed a tool to do just this.

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We were able to use this tool
to encode observations

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as mathematical expressions,

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

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that could explain them all.

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And we then apply this approach

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to uncover the genetic program
running inside embryonic stem cells

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

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And this tool was actually built

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on a solver that's deployed
routinely around the world

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for conventional software verification.

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So we started with a set
of nearly 50 different specifications

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that we generated from experimental
observations of embryonic stem cells.

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And by encoding these
observations in this tool,

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we were able to uncover
the first molecular program

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that could explain all of them.

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

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Being able to reconcile
all of these different observations

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

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even if you have a really big envelope.

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Because we've got
this kind of understanding,

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we could go one step further.

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

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in conditions we hadn't yet tested.

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We could probe the program in silico.

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And so we did just that:

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we generated predictions
that we tested in the lab,

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and we found that this program
was highly predictive.

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It told us how we could
accelerate progress

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back to the naïve state
quickly and efficiently.

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It told us which genes
to target to do that,

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which genes might even
hinder that process.

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

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So this approach really allowed us
to uncover the dynamics

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of what the cells are doing.

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

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Rather, it allows us to make sense
of the computation

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being carried out by the cell

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in the context of genetic interactions.

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So really, it's just one building block.

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The field urgently needs
to develop new approaches

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to understand biological
computation more broadly

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and at different levels,

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

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Only this kind of
transformative understanding

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

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But to program biology,
we will also need to develop

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the kinds of tools and languages

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that allow both experimentalists
and computational scientists

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to design biological function

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

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its biochemistry,

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so that we could then
build those structures.

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

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

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that's working to develop one.

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

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but if it's realized,

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it would be the final bridge
between software and wetware.

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

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if we can transform the field
into being truly interdisciplinary.

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

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and scientists from
each of these disciplines

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need to be able to work together
with common languages

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and to have shared scientific questions.

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

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and the technology
that you and I work with every day

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could hardly have been imagined

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at the time we first started
programming on silicon microchips.

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

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enabled by computational biology,

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

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to make that a reality.

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

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could be open to misuse.

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If we're willing to talk
about the potential

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for programming immune cells,

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we should also be thinking
about the potential of bacteria

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engineered to evade them.

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There might be people willing to do that.

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Now, one reassuring thought in this

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

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is that biology is
a fragile thing to work with.

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So programming biology
is not going to be something

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you'll be doing in your garden shed.

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But because we're at the outset of this,

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we can move forward
with our eyes wide open.

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We can ask the difficult
questions up front,

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we can put in place
the necessary safeguards

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

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

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of biological function.

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

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It can't be relegated to second place

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in the excitement
of scientific innovation.

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

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would be breakthrough applications
and breakthrough industries

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in areas from agriculture and medicine
to energy and materials

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and even computing itself.

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Imagine, one day we could be powering
the planet sustainably

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on the ultimate green energy

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

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

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by our current solar cells.

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If we understood that program
of quantum interactions

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that allow plants to absorb
sunlight so efficiently,

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

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that offer the material
for better solar cells.

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

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

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it could be realized in 10 or 15 years.

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So we are at the beginning
of a technological revolution.

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Understanding this ancient type
of biological computation

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is the critical first step.

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And if we can realize this,

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we would enter in the era
of an operating system

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that runs living software.

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Thank you very much.

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(Applause)