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The second half of the last century
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was completely defined
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,
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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
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wouldn't be understood
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 that sat
on your mantlepiece 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
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in a distributed way
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
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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 the 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've 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 for dealing
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with what it is that 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
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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
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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
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can emerge 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 ?? 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'd 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 ??.
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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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Now, what we've developed,
it's not a method
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that's specific to stem cell biology.
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Rather, it allows us to make sense
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of the computation
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
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on the implementation
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,
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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
if we could mimic something
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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,
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)