The next software revolution: programming biological cells
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0:01 - 0:05The second half of the last century
was completely defined -
0:05 - 0:07by a technological revolution:
-
0:07 - 0:09the software revolution.
-
0:09 - 0:14The ability to program electrons
on a material called silicon -
0:14 - 0:17made possible technologies,
companies and industries -
0:17 - 0:21that were at one point
unimaginable to many of us, -
0:21 - 0:25but which have now fundamentally changed
the way the world works. -
0:26 - 0:28The first half of this century, though,
-
0:28 - 0:32is going to be transformed
by a new software revolution: -
0:32 - 0:35the living software revolution.
-
0:35 - 0:39And this will be powered by the ability
to program biochemistry -
0:39 - 0:41on a material called biology.
-
0:41 - 0:45And doing so will enable us to harness
the properties of biology -
0:45 - 0:48to generate new kinds of therapies,
-
0:48 - 0:50to repair damaged tissue,
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0:50 - 0:53to reprogram faulty cells
-
0:53 - 0:57or even build programmable
operating systems out of biochemistry. -
0:58 - 1:02If we can realize this --
and we do need to realize it -- -
1:02 - 1:04its impact will be so enormous
-
1:04 - 1:08that it will make the first
software revolution pale in comparison. -
1:08 - 1:12And that's because living software
would transform the entirety of medicine, -
1:12 - 1:14agriculture and energy,
-
1:14 - 1:18and these are sectors that dwarf
those dominated by IT. -
1:19 - 1:23Imagine programmable plants
that fix nitrogen more effectively -
1:23 - 1:26or resist emerging fungal pathogens,
-
1:26 - 1:29or even programming crops
to be perennial rather than annual -
1:30 - 1:32so you could double
your crop yields each year. -
1:32 - 1:34That would transform agriculture
-
1:34 - 1:38and how we'll keep our growing
and global population fed. -
1:39 - 1:41Or imagine programmable immunity,
-
1:41 - 1:45designing and harnessing molecular devices
that guide your immune system -
1:45 - 1:49to detect, eradicate
or even prevent disease. -
1:49 - 1:51This would transform medicine
-
1:51 - 1:54and how we'll keep our growing
and aging population healthy. -
1:56 - 2:00We already have many of the tools
that will make living software a reality. -
2:00 - 2:02We can precisely edit genes with CRISPR.
-
2:02 - 2:05We can rewrite the genetic code
one base at a time. -
2:05 - 2:10We can even build functioning
synthetic circuits out of DNA. -
2:10 - 2:13But figuring out how and when
to wield these tools -
2:13 - 2:15is still a process of trial and error.
-
2:15 - 2:19It needs deep expertise,
years of specialization. -
2:19 - 2:22And experimental protocols
are difficult to discover -
2:22 - 2:25and all too often, difficult to reproduce.
-
2:25 - 2:30And, you know, we have a tendency
in biology to focus a lot on the parts, -
2:30 - 2:33but we all know that something like flying
wouldn't be understood -
2:33 - 2:34by only studying feathers.
-
2:35 - 2:39So programming biology is not yet
as simple as programming your computer. -
2:39 - 2:41And then to make matters worse,
-
2:41 - 2:45living systems largely bear no resemblance
to the engineered systems -
2:45 - 2:47that you and I program every day.
-
2:48 - 2:52In contrast to engineered systems,
living systems self-generate, -
2:52 - 2:53they self-organize,
-
2:53 - 2:55they operate at molecular scales.
-
2:55 - 2:57And these molecular-level interactions
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2:57 - 3:00lead generally to robust
macro-scale output. -
3:00 - 3:03They can even self-repair.
-
3:04 - 3:07Consider, for example,
the humble household plant, -
3:07 - 3:09like that one sat
on your mantelpiece at home -
3:09 - 3:11that you keep forgetting to water.
-
3:12 - 3:15Every day, despite your neglect,
that plant has to wake up -
3:15 - 3:18and figure out how
to allocate its resources. -
3:18 - 3:22Will it grow, photosynthesize,
produce seeds, or flower? -
3:22 - 3:26And that's a decision that has to be made
at the level of the whole organism. -
3:26 - 3:29But a plant doesn't have a brain
to figure all of that out. -
3:29 - 3:32It has to make do
with the cells on its leaves. -
3:32 - 3:34They have to respond to the environment
-
3:34 - 3:37and make the decisions
that affect the whole plant. -
3:37 - 3:41So somehow there must be a program
running inside these cells, -
3:41 - 3:43a program that responds
to input signals and cues -
3:43 - 3:45and shapes what that cell will do.
-
3:46 - 3:49And then those programs must operate
in a distributed way -
3:49 - 3:50across individual cells,
-
3:50 - 3:54so that they can coordinate
and that plant can grow and flourish. -
3:56 - 3:59If we could understand
these biological programs, -
3:59 - 4:02if we could understand
biological computation, -
4:02 - 4:06it would transform our ability
to understand how and why -
4:06 - 4:08cells do what they do.
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4:08 - 4:10Because, if we understood these programs,
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4:10 - 4:12we could debug them when things go wrong.
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4:12 - 4:17Or we could learn from them how to design
the kind of synthetic circuits -
4:17 - 4:21that truly exploit
the computational power of biochemistry. -
4:22 - 4:25My passion about this idea
led me to a career in research -
4:25 - 4:29at the interface of maths,
computer science and biology. -
4:29 - 4:34And in my work, I focus on the concept
of biology as computation. -
4:34 - 4:37And that means asking
what do cells compute, -
4:38 - 4:41and how can we uncover
these biological programs? -
4:42 - 4:46And I started to ask these questions
together with some brilliant collaborators -
4:46 - 4:48at Microsoft Research
and the University of Cambridge, -
4:48 - 4:50where together we wanted to understand
-
4:50 - 4:55the biological program
running inside a unique type of cell: -
4:55 - 4:57an embryonic stem cell.
-
4:57 - 5:00These cells are unique
because they're totally naïve. -
5:00 - 5:02They can become anything they want:
-
5:03 - 5:05a brain cell, a heart cell,
a bone cell, a lung cell, -
5:05 - 5:07any adult cell type.
-
5:07 - 5:09This naïvety, it sets them apart,
-
5:09 - 5:12but it also ignited the imagination
of the scientific community, -
5:12 - 5:15who realized, if we could
tap into that potential, -
5:15 - 5:17we would have a powerful
tool for medicine. -
5:18 - 5:21If we could figure out
how these cells make the decision -
5:21 - 5:23to become one cell type or another,
-
5:23 - 5:24we might be able to harness them
-
5:24 - 5:29to generate cells that we need
to repair diseased or damaged tissue. -
5:30 - 5:33But realizing that vision
is not without its challenges, -
5:33 - 5:36not least because these particular cells,
-
5:36 - 5:38they emerge just six days
after conception. -
5:39 - 5:41And then within a day or so, they're gone.
-
5:41 - 5:43They have set off down the different paths
-
5:43 - 5:46that form all the structures
and organs of your adult body. -
5:48 - 5:51But it turns out that cell fates
are a lot more plastic -
5:51 - 5:52than we might have imagined.
-
5:52 - 5:57About 13 years ago, some scientists
showed something truly revolutionary. -
5:57 - 6:02By inserting just a handful of genes
into an adult cell, -
6:02 - 6:04like one of your skin cells,
-
6:04 - 6:08you can transform that cell
back to the naïve state. -
6:08 - 6:11And it's a process that's actually
known as "reprogramming," -
6:11 - 6:14and it allows us to imagine
a kind of stem cell utopia, -
6:14 - 6:18the ability to take a sample
of a patient's own cells, -
6:18 - 6:20transform them back to the naïve state
-
6:20 - 6:23and use those cells to make
whatever that patient might need, -
6:23 - 6:25whether it's brain cells or heart cells.
-
6:27 - 6:28But over the last decade or so,
-
6:28 - 6:31figuring out how to change cell fate,
-
6:31 - 6:34it's still a process of trial and error.
-
6:34 - 6:38Even in cases where we've uncovered
successful experimental protocols, -
6:38 - 6:40they're still inefficient,
-
6:40 - 6:44and we lack a fundamental understanding
of how and why they work. -
6:45 - 6:48If you figured out how to change
a stem cell into a heart cell, -
6:48 - 6:51that hasn't got any way of telling you
how to change a stem cell -
6:51 - 6:52into a brain cell.
-
6:53 - 6:56So we wanted to understand
the biological program -
6:56 - 6:58running inside an embryonic stem cell,
-
6:58 - 7:02and understanding the computation
performed by a living system -
7:02 - 7:06starts with asking
a devastatingly simple question: -
7:06 - 7:09What is it that system actually has to do?
-
7:10 - 7:13Now, computer science actually
has a set of strategies -
7:13 - 7:17for dealing with what it is the software
and hardware are meant to do. -
7:17 - 7:19When you write a program,
you code a piece of software, -
7:19 - 7:21you want that software to run correctly.
-
7:21 - 7:23You want performance, functionality.
-
7:23 - 7:24You want to prevent bugs.
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7:24 - 7:26They can cost you a lot.
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7:26 - 7:28So when a developer writes a program,
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7:28 - 7:30they could write down
a set of specifications. -
7:30 - 7:32These are what your program should do.
-
7:32 - 7:34Maybe it should compare
the size of two numbers -
7:35 - 7:36or order numbers by increasing size.
-
7:37 - 7:42Technology exists that allows us
automatically to check -
7:42 - 7:44whether our specifications are satisfied,
-
7:44 - 7:47whether that program
does what it should do. -
7:47 - 7:50And so our idea was that in the same way,
-
7:50 - 7:53experimental observations,
things we measure in the lab, -
7:53 - 7:58they correspond to specifications
of what the biological program should do. -
7:59 - 8:01So we just needed to figure out a way
-
8:01 - 8:04to encode this new type of specification.
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8:05 - 8:08So let's say you've been busy in the lab
and you've been measuring your genes -
8:08 - 8:11and you've found that if Gene A is active,
-
8:11 - 8:14then Gene B or Gene C seems to be active.
-
8:15 - 8:18We can write that observation down
as a mathematical expression -
8:18 - 8:21if we can use the language of logic:
-
8:21 - 8:23If A, then B or C.
-
8:24 - 8:27Now, this is a very simple example, OK.
-
8:27 - 8:28It's just to illustrate the point.
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8:28 - 8:31We can encode truly rich expressions
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8:31 - 8:36that actually capture the behavior
of multiple genes or proteins over time -
8:36 - 8:38across multiple different experiments.
-
8:39 - 8:41And so by translating our observations
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8:41 - 8:43into mathematical expression in this way,
-
8:43 - 8:48it becomes possible to test whether
or not those observations can emerge -
8:48 - 8:51from a program of genetic interactions.
-
8:52 - 8:55And we developed a tool to do just this.
-
8:55 - 8:58We were able to use this tool
to encode observations -
8:58 - 8:59as mathematical expressions,
-
8:59 - 9:03and then that tool would allow us
to uncover the genetic program -
9:03 - 9:04that could explain them all.
-
9:05 - 9:08And we then apply this approach
-
9:08 - 9:12to uncover the genetic program
running inside embryonic stem cells -
9:12 - 9:16to see if we could understand
how to induce that naïve state. -
9:16 - 9:18And this tool was actually built
-
9:18 - 9:21on a solver that's deployed
routinely around the world -
9:21 - 9:23for conventional software verification.
-
9:24 - 9:27So we started with a set
of nearly 50 different specifications -
9:27 - 9:32that we generated from experimental
observations of embryonic stem cells. -
9:32 - 9:35And by encoding these
observations in this tool, -
9:35 - 9:38we were able to uncover
the first molecular program -
9:38 - 9:40that could explain all of them.
-
9:40 - 9:43Now, that's kind of a feat
in and of itself, right? -
9:43 - 9:46Being able to reconcile
all of these different observations -
9:46 - 9:49is not the kind of thing
you can do on the back of an envelope, -
9:49 - 9:52even if you have a really big envelope.
-
9:52 - 9:54Because we've got
this kind of understanding, -
9:54 - 9:56we could go one step further.
-
9:56 - 9:59We could use this program to predict
what this cell might do -
9:59 - 10:01in conditions we hadn't yet tested.
-
10:01 - 10:04We could probe the program in silico.
-
10:05 - 10:06And so we did just that:
-
10:06 - 10:09we generated predictions
that we tested in the lab, -
10:09 - 10:12and we found that this program
was highly predictive. -
10:12 - 10:15It told us how we could
accelerate progress -
10:15 - 10:18back to the naïve state
quickly and efficiently. -
10:18 - 10:21It told us which genes
to target to do that, -
10:21 - 10:23which genes might even
hinder that process. -
10:23 - 10:28We even found the program predicted
the order in which genes would switch on. -
10:29 - 10:32So this approach really allowed us
to uncover the dynamics -
10:32 - 10:35of what the cells are doing.
-
10:36 - 10:39What we've developed, it's not a method
that's specific to stem cell biology. -
10:39 - 10:42Rather, it allows us to make sense
of the computation -
10:42 - 10:44being carried out by the cell
-
10:44 - 10:47in the context of genetic interactions.
-
10:47 - 10:49So really, it's just one building block.
-
10:49 - 10:52The field urgently needs
to develop new approaches -
10:52 - 10:54to understand biological
computation more broadly -
10:54 - 10:56and at different levels,
-
10:56 - 11:00from DNA right through
to the flow of information between cells. -
11:00 - 11:03Only this kind of
transformative understanding -
11:03 - 11:08will enable us to harness biology
in ways that are predictable and reliable. -
11:09 - 11:12But to program biology,
we will also need to develop -
11:12 - 11:14the kinds of tools and languages
-
11:14 - 11:18that allow both experimentalists
and computational scientists -
11:18 - 11:20to design biological function
-
11:20 - 11:24and have those designs compile down
to the machine code of the cell, -
11:24 - 11:25its biochemistry,
-
11:25 - 11:27so that we could then
build those structures. -
11:27 - 11:31Now, that's something akin
to a living software compiler, -
11:31 - 11:33and I'm proud to be
part of a team at Microsoft -
11:33 - 11:35that's working to develop one.
-
11:35 - 11:39Though to say it's a grand challenge
is kind of an understatement, -
11:39 - 11:40but if it's realized,
-
11:40 - 11:44it would be the final bridge
between software and wetware. -
11:45 - 11:48More broadly, though, programming biology
is only going to be possible -
11:48 - 11:53if we can transform the field
into being truly interdisciplinary. -
11:53 - 11:56It needs us to bridge
the physical and the life sciences, -
11:56 - 11:58and scientists from
each of these disciplines -
11:58 - 12:01need to be able to work together
with common languages -
12:01 - 12:03and to have shared scientific questions.
-
12:05 - 12:09In the long term, it's worth remembering
that many of the giant software companies -
12:09 - 12:11and the technology
that you and I work with every day -
12:11 - 12:13could hardly have been imagined
-
12:13 - 12:16at the time we first started
programming on silicon microchips. -
12:16 - 12:19And if we start now to think about
the potential for technology -
12:20 - 12:22enabled by computational biology,
-
12:22 - 12:25we'll see some of the steps
that we need to take along the way -
12:25 - 12:26to make that a reality.
-
12:27 - 12:30Now, there is the sobering thought
that this kind of technology -
12:30 - 12:32could be open to misuse.
-
12:32 - 12:34If we're willing to talk
about the potential -
12:34 - 12:36for programming immune cells,
-
12:36 - 12:39we should also be thinking
about the potential of bacteria -
12:39 - 12:41engineered to evade them.
-
12:41 - 12:43There might be people willing to do that.
-
12:44 - 12:45Now, one reassuring thought in this
-
12:45 - 12:48is that -- well, less so
for the scientists -- -
12:48 - 12:51is that biology is
a fragile thing to work with. -
12:51 - 12:53So programming biology
is not going to be something -
12:53 - 12:55you'll be doing in your garden shed.
-
12:56 - 12:58But because we're at the outset of this,
-
12:58 - 13:00we can move forward
with our eyes wide open. -
13:00 - 13:03We can ask the difficult
questions up front, -
13:03 - 13:06we can put in place
the necessary safeguards -
13:06 - 13:09and, as part of that,
we'll have to think about our ethics. -
13:09 - 13:12We'll have to think about putting bounds
on the implementation -
13:12 - 13:13of biological function.
-
13:14 - 13:17So as part of this, research in bioethics
will have to be a priority. -
13:17 - 13:20It can't be relegated to second place
-
13:20 - 13:22in the excitement
of scientific innovation. -
13:23 - 13:27But the ultimate prize,
the ultimate destination on this journey, -
13:27 - 13:30would be breakthrough applications
and breakthrough industries -
13:30 - 13:34in areas from agriculture and medicine
to energy and materials -
13:34 - 13:36and even computing itself.
-
13:36 - 13:40Imagine, one day we could be powering
the planet sustainably -
13:40 - 13:42on the ultimate green energy
-
13:42 - 13:45if we could mimic something
that plants figured out millennia ago: -
13:46 - 13:49how to harness the sun's energy
with an efficiency that is unparalleled -
13:49 - 13:51by our current solar cells.
-
13:52 - 13:54If we understood that program
of quantum interactions -
13:54 - 13:58that allow plants to absorb
sunlight so efficiently, -
13:58 - 14:02we might be able to translate that
into building synthetic DNA circuits -
14:02 - 14:04that offer the material
for better solar cells. -
14:05 - 14:09There are teams and scientists working
on the fundamentals of this right now, -
14:09 - 14:12so perhaps if it got the right attention
and the right investment, -
14:12 - 14:15it could be realized in 10 or 15 years.
-
14:15 - 14:19So we are at the beginning
of a technological revolution. -
14:19 - 14:22Understanding this ancient type
of biological computation -
14:22 - 14:24is the critical first step.
-
14:24 - 14:26And if we can realize this,
-
14:26 - 14:29we would enter in the era
of an operating system -
14:29 - 14:31that runs living software.
-
14:31 - 14:32Thank you very much.
-
14:32 - 14:34(Applause)
- Title:
- The next software revolution: programming biological cells
- Speaker:
- Sara-Jane Dunn
- Description:
-
The cells in your body are like computer software: they're "programmed" to carry out specific functions at specific times. If we can better understand this process, we could unlock the ability to reprogram cells ourselves, says computational biologist Sara-Jane Dunn. In a talk from the cutting-edge of science, she explains how her team is studying embryonic stem cells to gain a new understanding of the biological programs that power life -- and develop "living software" that could transform medicine, agriculture and energy.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 14:47
Oliver Friedman edited English subtitles for The next software revolution: programming biological cells | ||
Oliver Friedman edited English subtitles for The next software revolution: programming biological cells | ||
Oliver Friedman edited English subtitles for The next software revolution: programming biological cells | ||
Oliver Friedman approved English subtitles for The next software revolution: programming biological cells | ||
Oliver Friedman edited English subtitles for The next software revolution: programming biological cells | ||
Camille Martínez accepted English subtitles for The next software revolution: programming biological cells | ||
Camille Martínez edited English subtitles for The next software revolution: programming biological cells | ||
Camille Martínez edited English subtitles for The next software revolution: programming biological cells |