Paul Rothemund: The astonishing promise of DNA folding

72,391 views ใƒป 2008-09-04

TED


ืื ื ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ืœืžื˜ื” ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ.

ืžืชืจื’ื: Eyal Ronel ืžื‘ืงืจ: Gad Amit
00:12
So, people argue vigorously about the definition of life.
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ืื ืฉื™ื ื ื•ื˜ื™ื ืœื”ืชื•ื•ื›ื— ื‘ืœื”ื˜ ืขืœ ื”ื”ื’ื“ืจื” ืžื”ื ื—ื™ื™ื.
00:15
They ask if it should have reproduction in it, or metabolism, or evolution.
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ื”ื ืฉื•ืืœื™ื ืื ื”ื—ื™ื™ื ืชืœื•ื™ื™ื ื‘ื™ื›ื•ืœืช ืœื”ืชืจื‘ื•ืช, ืื• ื‘ื—ื™ืœื•ืฃ ื—ื•ืžืจื™ื ืื• ื‘ืื‘ื•ืœื•ืฆื™ื”.
00:20
And I don't know the answer to that, so I'm not going to tell you.
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ืื ื™ ืœื ื™ื•ื“ืข ืืช ื”ืชืฉื•ื‘ื” ืœื›ืš, ืื– ืœื ืืกืคืจ ืœื›ื.
00:22
I will say that life involves computation.
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ืื ื™ ื›ืŸ ืื•ืžืจ ืฉื—ื™ื™ื ื›ืจื•ื›ื™ื ื‘ื—ื™ืฉื•ื‘.
00:25
So this is a computer program.
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ื–ืืช ืชื•ื›ื ื™ืช ืžื—ืฉื‘.
00:27
Booted up in a cell, the program would execute,
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ื›ืฉืžืคืขื™ืœื™ื ืื•ืชื” ื‘ืชื•ืš ืชื, ื”ืชื•ื›ื ื™ืช ืชืจื•ืฅ
00:30
and it could result in this person;
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ื•ืชืคื™ืง ืืช ื”ืื™ืฉ ื”ื–ื”,
00:33
or with a small change, it could result in this person;
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ืื• ื‘ืฉื™ื ื•ื™ ืงื˜ืŸ, ืืช ื”ืื™ืฉ ื”ื–ื”
00:36
or another small change, this person;
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ืื• ื‘ืฉื™ื ื•ื™ ืงื˜ืŸ ืื—ืจ, ืืช ื”ืื™ืฉ ื”ื–ื”
00:38
or with a larger change, this dog,
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ืื• ื‘ืฉื™ื ื•ื™ ื’ื“ื•ืœ ื™ื•ืชืจ, ืืช ื”ื›ืœื‘ ื”ื–ื”
00:41
or this tree, or this whale.
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ืื• ื”ืขืฅ ื”ื–ื” ืื• ื”ืœื•ื•ื™ืชืŸ ื”ื–ื”.
00:43
So now, if you take this metaphor
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ืื ืชืงื—ื• ืืช ื”ื“ื™ืžื•ื™ ื”ื–ื” ืฉืœ
00:45
[of] genome as program seriously,
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ื’ื ื•ื ื‘ืชื•ืจ ืชื•ื›ื ื™ืช ืžื—ืฉื‘ ื‘ืจืฆื™ื ื•ืช,
00:47
you have to consider that Chris Anderson
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ืžื‘ื—ื™ื ืชื›ื ื›ืจื™ืก ืื ื“ืจืกื•ืŸ
00:49
is a computer-fabricated artifact, as is Jim Watson,
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ื”ื•ื ืคื‘ืจื•ืง ื™ืฆื™ืจ-ืžื—ืฉื‘, ื•ื›ืžื•ืชื• ื’ื ื’'ื™ื ื•ื•ื˜ืกื•ืŸ,
00:52
Craig Venter, as are all of us.
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ืงืจื™ื™ื’ ื•ื ื˜ืจ, ื•ื‘ืขืฆื ื›ื•ืœื ื•.
00:55
And in convincing yourself that this metaphor is true,
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ื•ืื ืชืฉื›ื ืขื• ืืช ืขืฆืžื›ื ืฉื”ื“ื™ืžื•ื™ ื”ื–ื” ื ื›ื•ืŸ,
00:57
there are lots of similarities between genetic programs
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ืฉื™ืฉ ืงื•ื•ื™ ื“ืžื™ื•ืŸ ืจื‘ื™ื ื‘ื™ืŸ ืชื›ื ื•ืช ื’ื ื˜ื™
00:59
and computer programs that could help to convince you.
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ืœื‘ื™ืŸ ืชื›ื ื•ืช ืžื—ืฉื‘ื™ื, ืื•ืœื™ ื–ื” ื™ืขื–ื•ืจ ืœื›ื ืœื”ืฉืชื›ื ืข.
01:02
But one, to me, that's most compelling
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ืงื• ื“ืžื™ื•ืŸ ืฉืžื“ื”ื™ื ืื•ืชื™ ื‘ืžื™ื•ื—ื“
01:04
is the peculiar sensitivity to small changes
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ื”ื•ื ื”ืจื’ื™ืฉื•ืช ื™ื•ืฆืืช ื”ื“ื•ืคืŸ ืœืฉื™ื ื•ื™ื™ื ืงืœื™ื
01:07
that can make large changes in biological development -- the output.
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ืฉื’ื•ืจืžืช ืœืฉื™ื ื•ื™ื™ื ื’ื“ื•ืœื™ื ื‘ื”ืชืคืชื—ื•ืช ืฉืœ ื”ืคืœื˜ ื”ื‘ื™ื•ืœื•ื’ื™:
01:10
A small mutation can take a two-wing fly
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ืžื•ื˜ืฆื™ื” ืงื˜ื ื” ื™ื›ื•ืœื” ืœืงื—ืช ื–ื‘ื•ื‘ ืขื ืฉืชื™ ื›ื ืคื™ื™ื
01:12
and make it a four-wing fly.
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ื•ืœื”ืคื•ืš ืื•ืชื• ืœื–ื‘ื•ื‘ ืขื ืืจื‘ืข ื›ื ืคื™ื™ื;
01:13
Or it could take a fly and put legs where its antennae should be.
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ืื• ืœืงื—ืช ื–ื‘ื•ื‘ ื•ืœืฉื™ื ืจื’ืœื™ื™ื ื‘ืžืงื•ื ื”ืžื—ื•ืฉื™ื ืฉืœื•;
01:17
Or if you're familiar with "The Princess Bride,"
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ืื•, ืื ืืชื ืžื›ื™ืจื™ื ืืช "ื”ื ืกื™ื›ื” ื”ืงืกื•ืžื”",
01:19
it could create a six-fingered man.
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ืœื™ืฆื•ืจ ืื“ื ืขื ืฉืฉ ืืฆื‘ืขื•ืช.
01:21
Now, a hallmark of computer programs
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ื”ืชื”ื™ืœื” ื”ื’ื“ื•ืœื” ืฉืœ ืชื•ื›ื ื•ืช ื”ืžื—ืฉื‘
01:23
is just this kind of sensitivity to small changes.
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ื”ื™ื ืื•ืชื” ืจื’ื™ืฉื•ืช ืœืฉื™ื ื•ื™ื™ื ืงืœื™ื.
01:26
If your bank account's one dollar, and you flip a single bit,
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ืื ื™ืฉ ืœื›ื ื‘ื—ืฉื‘ื•ืŸ ื”ื‘ื ืง ื“ื•ืœืจ ืื—ื“ ื•ืชื”ืคื›ื• ืกื™ื‘ื™ืช ืื—ืช,
01:28
you could end up with a thousand dollars.
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ืชื•ื›ืœื• ืœืงื‘ืœ ืืœืฃ ื“ื•ืœืจ.
01:30
So these small changes are things that I think
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ืื– ื”ืฉื™ื ื•ื™ื™ื ื”ืงืœื™ื ื”ืืœื” ื”ื ื“ื‘ืจื™ื ืฉืœื“ืขืชื™
01:33
that -- they indicate to us that a complicated computation
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ืžืฆื‘ื™ืขื™ื ืขืœ ื›ืš ืฉืงื™ื™ื ื—ื™ืฉื•ื‘ ืžื•ืจื›ื‘
01:35
in development is underlying these amplified, large changes.
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ืฉื’ื•ืจื ืœืฉื™ื ื•ื™ื™ื ื”ืืœื” ืœื’ื“ื•ืœ ืœืžืžื“ื™ื”ื.
01:39
So now, all of this indicates that there are molecular programs underlying biology,
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ื›ืœ ื–ื” ืžืจืื” ืฉื™ืฉ ืชื•ื›ื ื•ืช ืžื•ืœืงื•ืœืจื™ื•ืช ื‘ื‘ืกื™ืก ื”ื‘ื™ื•ืœื•ื’ื™ื”.
01:45
and it shows the power of molecular programs -- biology does.
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ื”ื‘ื™ื•ืœื•ื’ื™ื” ืžืจืื” ืื™ื–ื” ื›ื•ื— ื™ืฉ ืœืชื•ื›ื ื•ืช ืžื•ืœืงื•ืœืจื™ื•ืช.
01:49
And what I want to do is write molecular programs,
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ืื ื™ ืจื•ืฆื” ืœื›ืชื•ื‘ ืชื•ื›ื ื•ืช ืžื•ืœืงื•ืœืจื™ื•ืช
01:51
potentially to build technology.
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ืฉื™ืฉ ืœื”ืŸ ื™ื›ื•ืœืช ืœื‘ื ื•ืช ื˜ื›ื ื•ืœื•ื’ื™ื”.
01:53
And there are a lot of people doing this,
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ืื ืฉื™ื ืจื‘ื™ื,
01:54
a lot of synthetic biologists doing this, like Craig Venter.
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ื”ืจื‘ื” ื‘ื™ื•ืœื•ื’ื™ื ืกื™ื ืชื˜ื™ื™ื ื“ื•ื’ืžืช ืงืจื™ื™ื’ ื•ื ื˜ืจ
01:57
And they concentrate on using cells.
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ืžืฉืชืžืฉื™ื ื‘ืชืื™ื.
01:59
They're cell-oriented.
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ื”ื ืžื•ื ื—ื™-ืชืื™ื.
02:01
So my friends, molecular programmers, and I
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ืœื—ื‘ืจื™ื™ ื”ืชื•ื›ื ื™ืชื ื™ื ื”ืžื•ืœืงื•ืœืจื™ื™ื ื•ืœื™
02:03
have a sort of biomolecule-centric approach.
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ื™ืฉ ื’ื™ืฉื” ื‘ื™ื•-ืžื•ืœืงื•ืœื•-ืฆื ื˜ืจื™ืช.
02:05
We're interested in using DNA, RNA and protein,
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ืื ื—ื ื• ืžืฉืชืžืฉื™ื ื‘-DNA, ื‘-RNA ื•ื‘ื—ืœื‘ื•ื ื™ื
02:08
and building new languages for building things from the bottom up,
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ื›ื“ื™ ืœื‘ื ื•ืช ืฉืคื•ืช ื—ื“ืฉื•ืช ืœื‘ื ื™ื™ืช ื“ื‘ืจื™ื ื—ื“ืฉื™ื ืžื”ื™ืกื•ื“
02:11
using biomolecules,
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ื‘ืขื–ืจืช ื‘ื™ื•-ืžื•ืœืงื•ืœื•ืช,
02:12
potentially having nothing to do with biology.
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ืฉืœื›ืื•ืจื” ืื™ืŸ ืœื”ืŸ ืฉื•ื ืงืฉืจ ืœื‘ื™ื•ืœื•ื’ื™ื”.
02:15
So, these are all the machines in a cell.
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ืืœื” ื›ืœ ื”ืžื›ื•ื ื•ืช ืฉื™ืฉ ื‘ืชื•ืš ื”ืชื.
02:19
There's a camera.
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ื™ืฉ ืžืฆืœืžื”
02:21
There's the solar panels of the cell,
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ื•ื™ืฉ ืžืฉื˜ื—ื™ื ืกื•ืœืืจื™ื™ื ืฉืœ ื”ืชื,
02:22
some switches that turn your genes on and off,
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ื›ืžื” ืžืคืกืงื™ื ืฉืžื“ืœื™ืงื™ื ื•ืžื›ื‘ื™ื ืืช ื”ื’ื ื™ื,
02:24
the girders of the cell, motors that move your muscles.
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ืงื•ืจื•ืช ืชืžื™ื›ื” ืฉืœ ื”ืชื, ืžื ื•ืขื™ื ืฉืžื–ื™ื–ื™ื ืืช ื”ืฉืจื™ืจื™ื.
02:27
My little group of molecular programmers
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ืงื‘ื•ืฆืช ื”ืชื•ื›ื ื™ืชื ื™ื ื”ืžื•ืœืงื•ืœืจื™ื™ื ื”ืงื˜ื ื” ืฉืœื™
02:29
are trying to refashion all of these parts from DNA.
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ืžื ืกื” ืœืขืฆื‘ ืžื—ื“ืฉ ืืช ื›ืœ ื”ื—ืœืงื™ื ื”ืืœื” ืž-DNA.
02:33
We're not DNA zealots, but DNA is the cheapest,
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ืื ื—ื ื• ืœื ืงื ืื™ DNA, ื”ื•ื ืคืฉื•ื˜ ื”ื–ื•ืœ
02:35
easiest to understand and easy to program material to do this.
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ื•ื”ืคืฉื•ื˜ ื‘ื™ื•ืชืจ ืœื”ื‘ื ื”, ื•ืงืœ ืœืชื›ื ืช ื—ื•ืžืจื™ื ืœืขืฉื•ืช ื–ืืช.
02:38
And as other things become easier to use --
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ื•ื›ืืฉืจ ื“ื‘ืจื™ื ืื—ืจื™ื ื™ื”ืคื›ื• ืงืœื™ื ื™ื•ืชืจ ืœืฉื™ืžื•ืฉ -
02:40
maybe protein -- we'll work with those.
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ืื•ืœื™ ื—ืœื‘ื•ื ื™ื, ืื ื—ื ื• ื ืขื‘ื•ื“ ืืชื.
02:43
If we succeed, what will molecular programming look like?
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ืื ื ืฆืœื™ื—, ืื™ืš ื™ื™ืจืื” ืชื›ื ื•ืช ืžื•ืœืงื•ืœืจื™?
02:45
You're going to sit in front of your computer.
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ืืชื ืชืฉื‘ื• ืœืคื ื™ ื”ืžื—ืฉื‘ ืฉืœื›ื
02:47
You're going to design something like a cell phone,
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ื•ืชืขืฆื‘ื• ืžืฉื”ื• ื›ืžื• ื˜ืœืคื•ืŸ ืกืœื•ืœืจื™,
02:49
and in a high-level language, you'll describe that cell phone.
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ื•ื‘ืขื–ืจืช ืฉืคื” ืขื™ืœื™ืช ืืชื ืชืชืืจื• ืืช ื”ื˜ืœืคื•ืŸ ื”ื–ื”.
02:51
Then you're going to have a compiler
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ื•ืื– ื™ื”ื™ื” ืœื›ื ืงื•ืžืคื™ื™ืœืจ (ืžื”ื“ืจ)
02:53
that's going to take that description
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ืฉื™ื™ืงื— ืืช ื”ืชื™ืื•ืจ ื”ื–ื”
02:54
and it's going to turn it into actual molecules
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ื•ื™ื”ืคื•ืš ืื•ืชื• ืœืžื•ืœืงื•ืœื•ืช ืืžื™ืชื™ื•ืช
02:56
that can be sent to a synthesizer
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ืฉืืคืฉืจ ืœืฉืœื•ื— ืœืกื™ื ืชื™ืกื™ื™ื–ืจ
02:58
and that synthesizer will pack those molecules into a seed.
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ืฉื™ืืจื•ื– ืืช ื”ืžื•ืœืงื•ืœื•ืช ื‘ืชื•ืš ื’ืจืขื™ืŸ.
03:01
And what happens if you water and feed that seed appropriately,
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ื•ืื ืชืฉืงื• ื•ืชืื›ื™ืœื• ืืช ื”ื’ืจืขื™ืŸ ื›ืžื• ืฉืฆืจื™ืš,
03:04
is it will do a developmental computation,
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ื”ื•ื ื™ื‘ืฆืข ื—ื™ืฉื•ื‘ ื”ืชืคืชื—ื•ืชื™,
03:06
a molecular computation, and it'll build an electronic computer.
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ื—ื™ืฉื•ื‘ ืžื•ืœืงื•ืœืจื™, ื•ื”ื•ื ื™ื‘ื ื” ืžื—ืฉื‘ ืืœืงื˜ืจื•ื ื™.
03:09
And if I haven't revealed my prejudices already,
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ื•ืื ืœื ื—ืฉืคืชื™ ื›ื‘ืจ ืืช ื“ืขื•ืชื™ื™ ื”ืงื“ื•ืžื•ืช,
03:12
I think that life has been about molecular computers
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ืื ื™ ื—ื•ืฉื‘ ืฉื”ื—ื™ื™ื ื”ื ืžื—ืฉื‘ื™ื ืžื•ืœืงื•ืœืจื™ื™ื
03:14
building electrochemical computers,
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ืฉื‘ื•ื ื™ื ืžื—ืฉื‘ื™ื ืืœืงื˜ืจื•-ื›ื™ืžื™ื™ื
03:16
building electronic computers,
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ืฉื‘ื•ื ื™ื ืžื—ืฉื‘ื™ื ืืœืงื˜ืจื•ื ื™ื™ื
03:18
which together with electrochemical computers
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ืฉื™ื—ื“ ืขื ื”ืžื—ืฉื‘ื™ื ื”ืืœืงื˜ืจื•-ื›ื™ืžื™ื™ื,
03:20
will build new molecular computers,
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ื™ื‘ื ื• ืžื—ืฉื‘ื™ื ืžื•ืœืงื•ืœืจื™ื™ื ื—ื“ืฉื™ื
03:22
which will build new electronic computers, and so forth.
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ืฉื™ื‘ื ื• ืžื—ืฉื‘ื™ื ืืœืงื˜ืจื•ื ื™ื™ื ื—ื“ืฉื™ื ื•ื›ืŸ ื”ืœืื”.
03:25
And if you buy all of this,
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ื•ืื ืืชื ืžืกื›ื™ืžื™ื ืขื ื›ืœ ื–ื”,
03:26
and you think life is about computation, as I do,
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ื•ืืชื ื—ื•ืฉื‘ื™ื ื›ืžื•ื ื™ ืฉื”ื—ื™ื™ื ื”ื ืขื ื™ื™ืŸ ืฉืœ ื—ื™ืฉื•ื‘,
03:28
then you look at big questions through the eyes of a computer scientist.
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ืืชื ืชืกืชื›ืœื• ืขืœ ืฉืืœื•ืช ื’ื“ื•ืœื•ืช ื‘ืขื™ื ื™ื™ื ืฉืœ ืžื“ืขื ื™ ืžื—ืฉื‘.
03:31
So one big question is, how does a baby know when to stop growing?
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ืฉืืœื” ื’ื“ื•ืœื” ืื—ืช ื›ื–ื• ื”ื™ื: ืื™ืš ืชื™ื ื•ืง ื™ื•ื“ืข ืžืชื™ ืœื”ืคืกื™ืง ืœื’ื“ื•ืœ?
03:35
And for molecular programming,
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ื•ืขื‘ื•ืจ ืชื•ื›ื ื™ืชืŸ ืžื•ืœืงื•ืœืจื™,
03:37
the question is how does your cell phone know when to stop growing?
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ื”ืฉืืœื” ื”ื™ื ืื™ืš ื”ื˜ืœืคื•ืŸ ื”ืกืœื•ืœืจื™ ื™ื•ื“ืข ืžืชื™ ืœื”ืคืกื™ืง ืœื’ื“ื•ืœ?
03:39
(Laughter)
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(ืฆื—ื•ืง)
03:40
Or how does a computer program know when to stop running?
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ืื• ืื™ืš ืชื•ื›ื ืช ืžื—ืฉื‘ ื™ื•ื“ืขืช ืžืชื™ ืœื”ืคืกื™ืง ืœืจื•ืฅ?
03:43
Or more to the point, how do you know if a program will ever stop?
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ืื• ืœื™ืชืจ ื“ื™ื•ืง, ืื™ืš ื™ื•ื“ืขื™ื ืื ื”ืชื•ื›ื ื” ืื™ ืคืขื ืชืขืฆื•ืจ?
03:46
There are other questions like this, too.
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ื™ืฉ ืขื•ื“ ืฉืืœื•ืช ืžื”ืกื•ื’ ื”ื–ื”.
03:48
One of them is Craig Venter's question.
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ืื—ืช ื”ืฉืืœื•ืช ื”ืืœื” ื”ื™ื ืฉืœ ืงืจื™ื™ื’ ื•ื ื˜ืจ.
03:50
Turns out I think he's actually a computer scientist.
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ืžืกืชื‘ืจ ืฉื”ื•ื ืžืžืฉ ืื™ืฉ ืžื“ืขื™ ื”ืžื—ืฉื‘.
03:52
He asked, how big is the minimal genome
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ื”ื•ื ืฉืืœ ืžื” ื”ื’ื•ื“ืœ ื”ืžื™ื ื™ืžืœื™ ืฉืœ ื’ื ื•ื
03:55
that will give me a functioning microorganism?
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ืฉื™ืฆืœื™ื— ืœื”ืคื™ืง ืžื™ืงืจื•-ืื•ืจื’ื ื™ื–ื ืžืชืคืงื“?
03:57
How few genes can I use?
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ืžื” ืžื™ื ื™ืžื•ื ื”ื’ื ื™ื ื”ืืคืฉืจื™?
03:59
This is exactly analogous to the question,
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ืฉื–ื• ืื ืœื•ื’ื™ื” ืžื•ืฉืœืžืช ืœืฉืืœื”,
04:01
what's the smallest program I can write
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ืžื” ื”ืชื•ื›ื ื” ื”ืงื˜ื ื” ื‘ื™ื•ืชืจ
04:02
that will act exactly like Microsoft Word?
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ืฉืชืชื ื”ื’ ื‘ื“ื™ื•ืง ื›ืžื• ืžื™ืงืจื•ืกื•ืคื˜ ื•ื•ืจื“?
04:04
(Laughter)
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(ืฆื—ื•ืง)
04:05
And just as he's writing, you know, bacteria that will be smaller,
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ื›ืžื• ืฉื”ื•ื ื›ื•ืชื‘ ืžื•ื“ืœื™ื ืฉืœ ื—ื™ื™ื“ืงื™ื ืฉื™ื”ื™ื• ืงื˜ื ื™ื ื™ื•ืชืจ,
04:09
he's writing genomes that will work,
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ื”ื•ื ื›ื•ืชื‘ ื’ื ื•ืžื™ื ืžืชืคืงื“ื™ื,
04:10
we could write smaller programs
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ืื ื—ื ื• ื ื•ื›ืœ ืœื‘ื ื•ืช ืชื•ื›ื ื•ืช ืงื˜ื ื•ืช ื™ื•ืชืจ
04:12
that would do what Microsoft Word does.
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ืฉื™ืชื ื”ื’ื• ื›ืžื• ืžื™ืงืจื•ืกื•ืคื˜ ื•ื•ืจื“.
04:14
But for molecular programming, our question is,
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ืื‘ืœ ื‘ืชื›ื ื•ืช ืžื•ืœืงื•ืœืจื™, ื”ืฉืืœื” ื”ื™ื
04:16
how many molecules do we need to put in that seed to get a cell phone?
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ื›ืžื” ืžื•ืœืงื•ืœื•ืช ืฆืจื™ืš ืœืฉื™ื ื‘ื’ืจืขื™ืŸ ื›ื“ื™ ืœื™ืฆื•ืจ ื˜ืœืคื•ืŸ ืกืœื•ืœืจื™?
04:20
What's the smallest number we can get away with?
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ืžื” ื”ืžืกืคืจ ื”ืงื˜ืŸ ื‘ื™ื•ืชืจ ืฉื™ืกืคื™ืง ืœื ื•?
04:22
Now, these are big questions in computer science.
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ืืœื” ืฉืืœื•ืช ื’ื“ื•ืœื•ืช ื‘ืžื“ืขื™ ื”ืžื—ืฉื‘.
04:24
These are all complexity questions,
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ื›ืœ ืืœื” ื”ืŸ ืฉืืœื•ืช ืกื™ื‘ื•ื›ื™ื•ืช
04:26
and computer science tells us that these are very hard questions.
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ื•ื‘ืžื“ืขื™ ื”ืžื—ืฉื‘ ืืœื” ืฉืืœื•ืช ืžืื•ื“ ืงืฉื•ืช.
04:28
Almost -- many of them are impossible.
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ื•ื›ืžืขื˜ ืœื›ื•ืœื ืื™ืŸ ืคื™ืชืจื•ืŸ.
04:30
But for some tasks, we can start to answer them.
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ืื‘ืœ ืœืฉืืœื•ืช ืžืกื•ื™ืžื•ืช ื™ืฉ ืœื ื• ื”ืชื—ืœื” ืฉืœ ืชืฉื•ื‘ื”.
04:33
So, I'm going to start asking those questions
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ืื ื™ ืืฉืืœ ืขื›ืฉื™ื• ื›ืžื” ืžื”ืฉืืœื•ืช
04:34
for the DNA structures I'm going to talk about next.
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ืขื‘ื•ืจ ืžื‘ื ื™ ื”-DNA ืฉืขืœื™ื”ื ืื“ื‘ืจ ื‘ื”ืžืฉืš.
04:37
So, this is normal DNA, what you think of as normal DNA.
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ื–ื” DNA ืกื˜ื ื“ืจื˜ื™, ื›ืžื• ืฉืืชื ืžื›ื™ืจื™ื.
04:40
It's double-stranded, it's a double helix,
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ื™ืฉ ืœื• ืฉื ื™ ื’ื“ื™ืœื™ื, ื–ื” ืกืœื™ืœ ื›ืคื•ืœ,
04:42
has the As, Ts, Cs and Gs that pair to hold the strands together.
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ื–ื•ื’ื•ืช ืฉืœ C ,T ,A ื•-G ืฉืžื—ื–ื™ืงื™ื ืืช ื”ื’ื“ื™ืœื™ื ื‘ื™ื—ื“
04:45
And I'm going to draw it like this sometimes,
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ืœืคืขืžื™ื ืื ื™ ืืฆื™ื™ืจ ืื•ืชื• ื›ื›ื”
04:47
just so I don't scare you.
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ืื– ืืœ ืชื™ื‘ื”ืœื•.
04:49
We want to look at individual strands and not think about the double helix.
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ืื ื—ื ื• ื ืกืชื›ืœ ืขืœ ื”ื’ื“ื™ืœื™ื ื‘ื ืคืจื“ ื•ืœื ื ื—ืฉื•ื‘ ืขืœ ื”ืกืœื™ืœ ื”ื›ืคื•ืœ.
04:52
When we synthesize it, it comes single-stranded,
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ื‘ืชื”ืœื™ืš ื”ืกื™ื ืชื–ื” ืžืงื‘ืœื™ื ื’ื“ื™ืœ ืื—ื“,
04:55
so we can take the blue strand in one tube
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ืื– ืืคืฉืจ ืœืฉื™ื ื’ื“ื™ืœ ื›ื—ื•ืœ ื‘ืžื‘ื—ื ื” ืื—ืช
04:58
and make an orange strand in the other tube,
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ื•ื’ื“ื™ืœ ื›ืชื•ื ื‘ืžื‘ื—ื ื” ื”ืฉื ื™ื™ื”
05:00
and they're floppy when they're single-stranded.
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ื•ื”ื ืžืกืชืœืกืœื™ื ืœื”ื ื‘ื ืคืจื“.
05:02
You mix them together and they make a rigid double helix.
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ืชืขืจื‘ื‘ื• ืื•ืชื ื™ื—ื“ ื•ืชืงื‘ืœื• ืกืœื™ืœ ื›ืคื•ืœ ื™ืฆื™ื‘.
05:05
Now for the last 25 years,
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ื‘ืžื”ืœืš 25 ื”ืฉื ื™ื ื”ืื—ืจื•ื ื•ืช
05:07
Ned Seeman and a bunch of his descendants
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ื ื“ ืกื™ืžืืŸ ื•ื›ืžื” ืžืžืฉื™ื›ื™ื•
05:09
have worked very hard and made beautiful three-dimensional structures
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ืขื‘ื“ื• ืงืฉื” ืžืื•ื“ ื•ื™ืฆืจื• ืžื‘ื ื™ื ืชืœืช ืžืžื“ื™ื™ื ื™ืคื”ืคื™ื™ื
05:12
using this kind of reaction of DNA strands coming together.
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ื‘ืขื–ืจืช ืชื’ื•ื‘ื” ืฉืœ ื’ื“ื™ืœื™ DNA ืฉืžืชื—ื‘ืจื™ื ื–ื” ืœื–ื”.
05:15
But a lot of their approaches, though elegant, take a long time.
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ื”ื’ื™ืฉื” ืฉืœื”ื ืืœื’ื ื˜ื™ืช ืื‘ืœ ืžืื•ื“ ืืจื•ื›ื”,
05:18
They can take a couple of years, or it can be difficult to design.
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ืœื•ืงื—ืช ื›ืžื” ืฉื ื™ื ืื• ื›ืจื•ื›ื” ื‘ืชื›ื ื•ืŸ ืžืกื•ื‘ืš.
05:21
So I came up with a new method a couple of years ago
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ืื ื™ ื”ืžืฆืืชื™ ืฉื™ื˜ื” ื—ื“ืฉื” ืœืคื ื™ ื›ืžื” ืฉื ื™ื:
05:24
I call DNA origami
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ืฉื ืงืจืืช ืื•ืจื™ื’ืžื™ DNA.
05:25
that's so easy you could do it at home in your kitchen
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ื–ื” ื›ืœ ื›ืš ืคืฉื•ื˜ ืฉื’ื ืืชื ื™ื›ื•ืœื™ื ืœืขืฉื•ืช ืืช ื–ื” ื‘ื‘ื™ืช ื‘ืžื˜ื‘ื—
05:27
and design the stuff on a laptop.
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ื•ืœืชื›ื ืŸ ืืช ื”ืขืกืง ืขืœ ืžื—ืฉื‘ ื ื™ื™ื“.
05:29
But to do it, you need a long, single strand of DNA,
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ืื‘ืœ ื›ื“ื™ ืœืขืฉื•ืช ืืช ื–ื”, ืชืฆื˜ืจื›ื• ื’ื“ื™ืœ ืืจื•ืš ืฉืœ DNA
05:32
which is technically very difficult to get.
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ืฉื˜ื›ื ื™ืช ืงืฉื” ืžืื•ื“ ืœื”ืฉื™ื’.
05:34
So, you can go to a natural source.
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ืืชื ื™ื›ื•ืœื™ื ืœื’ืฉืช ืœืžืงื•ืจ ื˜ื‘ืขื™,
05:36
You can look in this computer-fabricated artifact,
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ืœื—ืคืฉ ืืฆืœ ื”ื™ืฆื•ืจ ื”ืžืžื•ื—ืฉื‘ ื‘ืฆื•ืจื” ืžืœืื›ื•ืชื™ืช ื”ื–ื”
05:38
and he's got a double-stranded genome -- that's no good.
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ื•ื™ืฉ ืœื• ื’ื ื•ื ืขื ื’ื“ื™ืœ ื›ืคื•ืœ ืฉืœื ืžืชืื™ื ืœื ื•.
05:40
You look in his intestines. There are billions of bacteria.
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ื ืกืชื›ืœ ื‘ืงืจื‘ื™ื™ื ืฉืœื•. ื™ืฉ ืฉื ืžื™ืœื™ืืจื“ื™ ื—ื™ื™ื“ืงื™ื.
05:43
They're no good either.
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ื’ื ื”ื ืœื ืžืชืื™ืžื™ื ืœื ื•.
05:45
Double strand again, but inside them, they're infected with a virus
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ื™ืฉ ืœื”ื ื’ื“ื™ืœ ื›ืคื•ืœ, ืื‘ืœ ื‘ืคื ื™ื ื”ื ื ื’ื•ืขื™ื ื‘ื•ื•ื™ืจื•ืก
05:47
that has a nice, long, single-stranded genome
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ื•ืœื• ื™ืฉ ื’ื ื•ื ื—ื‘ื™ื‘, ืืจื•ืš ื•ื—ื“-ื’ื“ื™ืœื™
05:50
that we can fold like a piece of paper.
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ืฉืืคืฉืจ ืœืงืคืœ ื›ืžื• ื ื™ื™ืจ,
05:52
And here's how we do it.
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ื•ื›ื›ื” ืขื•ืฉื™ื ืืช ื–ื”.
05:53
This is part of that genome.
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ื–ื” ื—ืœืง ืžื”ื’ื ื•ื ื”ื”ื•ื.
05:54
We add a bunch of short, synthetic DNAs that I call staples.
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ื ื•ืกื™ืฃ ื–ื ื‘ื•ืช DNA ืกื™ื ืชื˜ื™ื™ื ืงืฆืจื™ื, "ืžื”ื“ืงื™ื".
05:57
Each one has a left half that binds the long strand in one place,
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ื‘ื›ืœ ืžื”ื“ืง ื”ื—ืฆื™ ื”ืฉืžืืœื™ ืžื—ื‘ืจ ืืช ื”ื’ื“ื™ืœ ื”ืืจื•ืš ื‘ื ืงื•ื“ื” ืื—ืช
06:01
and a right half that binds it in a different place,
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ื•ื”ื—ืฆื™ ื”ื™ืžื ื™ ืžืชื—ื‘ืจ ื‘ื ืงื•ื“ื” ืื—ืจืช
06:04
and brings the long strand together like this.
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ื•ืงื•ืฉืจ ืืช ื”ื’ื“ื™ืœ ื”ืืจื•ืš ื‘ืฆื•ืจื” ื›ื–ื•.
06:07
The net action of many of these on that long strand
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ืกืš ื›ืœ ื”ืคืขื•ืœื•ืช ืฉืœ ื”ืžื”ื“ืงื™ื ืขืœ ื”ื’ื“ื™ืœ ื”ืืจื•ืš
06:09
is to fold it into something like a rectangle.
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ืžืงืคืœื•ืช ืื•ืชื• ืœืฆื•ืจื” ื“ืžื•ื™ื™ืช ืžืœื‘ืŸ.
06:11
Now, we can't actually take a movie of this process,
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ืื™ืŸ ืœื ื• ื“ืจืš ืœื”ืžื—ื™ืฉ ืืช ื”ืชื”ืœื™ืš ื‘ืกืจื˜,
06:13
but Shawn Douglas at Harvard
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ืื‘ืœ ืฉื•ืŸ ื“ื’ืœืืก ืžื”ืจื•ื•ืืจื“
06:15
has made a nice visualization for us
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ื”ื›ื™ืŸ ื”ื“ืžื™ื” ื ื—ืžื“ื” ื‘ืฉื‘ื™ืœื ื•
06:17
that begins with a long strand and has some short strands in it.
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ืฉืžืชื—ื™ืœื” ื‘ื’ื“ื™ืœ ืืจื•ืš ืขื ื›ืžื” ื’ื“ื™ืœื™ื ืงืฆืจื™ื ื‘ืชื•ื›ื•.
06:21
And what happens is that we mix these strands together.
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ืื ื—ื ื• ืžืขืจื‘ื‘ื™ื ืืช ื”ื’ื“ื™ืœื™ื ื”ืืœื” ื™ื—ื“,
06:25
We heat them up, we add a little bit of salt,
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ืžื•ืกื™ืคื™ื ื˜ื™ืค-ื˜ื™ืคื” ืฉืœ ืžืœื—
06:27
we heat them up to almost boiling and cool them down,
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ื•ืžื—ืžืžื™ื ืื•ืชื ื›ืžืขื˜ ืœืจืชื™ื—ื” ื•ืื– ืžืงืจืจื™ื,
06:29
and as we cool them down,
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ื•ืชื•ืš ื›ื“ื™ ื”ืงื™ืจื•ืจ
06:30
the short strands bind the long strands
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ื”ื’ื“ื™ืœื™ื ื”ืงืฆืจื™ื ืงื•ืฉืจื™ื ืืช ื”ืืจื•ื›ื™ื
06:32
and start to form structure.
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ื•ืžืชื—ื™ืœื™ื ืœื™ืฆื•ืจ ืžื‘ื ื”.
06:34
And you can see a little bit of double helix forming there.
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ืืชื ื™ื›ื•ืœื™ื ืœืจืื•ืช ืงืฆืช ืกืœื™ืœ ื›ืคื•ืœ ืฉื ื•ืฆืจ ื›ืืŸ.
06:38
When you look at DNA origami,
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ืื ืชืกืชื›ืœื• ืขืœ ืื•ืจื™ื’ืžื™ DNA
06:40
you can see that what it really is,
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ืชื‘ื™ื ื• ืžืžื” ื”ื•ื ื‘ืขืฆื ืขืฉื•ื™,
06:43
even though you think it's complicated,
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ืœืžืจื•ืช ืฉื”ื•ื ื ืจืื” ืžืกื•ื‘ืš,
06:44
is a bunch of double helices that are parallel to each other,
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ื–ื” ื‘ืขืฆื ืื•ืกืฃ ืฉืœ ืกืœื™ืœื™ื ื›ืคื•ืœื™ื ืžืงื‘ื™ืœื™ื ื–ื” ืœื–ื”
06:47
and they're held together
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ืฉืžื•ื—ื–ืงื™ื ื™ื—ื“ ื‘ืžืงื•ืžื•ืช ืฉื‘ื”ื
06:49
by places where short strands go along one helix
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ื’ื“ื™ืœื™ื ืงืฆืจื™ื ืžืชื—ื™ืœื™ื ื‘ืกืœื™ืœ ืื—ื“
06:51
and then jump to another one.
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ื•ืงื•ืคืฆื™ื ืœืกืœื™ืœ ืื—ืจ.
06:53
So there's a strand that goes like this, goes along one helix and binds --
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ื”ื ื” ื’ื“ื™ืœ ืฉื”ื•ืœืš ื‘ืฆื•ืจื” ื›ื–ื•, ืžืชื—ื™ืœ ืœืื•ืจืš ืกืœื™ืœ ืื—ื“
06:56
it jumps to another helix and comes back.
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ืงื•ืคืฅ ืœืกืœื™ืœ ืฉื ื™ ื•ื—ื•ื–ืจ ื—ื–ืจื”,
06:58
That holds the long strand like this.
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ื›ื›ื” ื”ื•ื ืžื—ื–ื™ืง ืืช ื”ื’ื“ื™ืœ ื”ืืจื•ืš.
07:00
Now, to show that we could make any shape or pattern
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ื›ื“ื™ ืœื”ืจืื•ืช ืฉืืคืฉืจ ืœื™ืฆื•ืจ ืื™ื–ื• ืฆื•ืจื” ืื• ืชื‘ื ื™ืช ืฉื ืจืฆื”
07:03
that we wanted, I tried to make this shape.
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ื ื™ืกื™ืชื™ ืœื™ืฆื•ืจ ืืช ื”ืฆื•ืจื” ื”ื–ืืช.
07:06
I wanted to fold DNA into something that goes up over the eye,
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ืจืฆื™ืชื™ ืœืงืคืœ DNA ืœืฆื•ืจื” ืฉืขื•ืœื” ืžืขืœ ื”ืขื™ืŸ,
07:08
down the nose, up the nose, around the forehead,
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ื™ื•ืจื“ืช ืœืืฃ, ืกื‘ื™ื‘ ื”ืžืฆื—,
07:11
back down and end in a little loop like this.
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ื—ื–ืจื” ืœืžื˜ื” ื•ืžืกืชื™ื™ืžืช ื‘ืœื•ืœืื” ืงื˜ื ื”.
07:14
And so, I thought, if this could work, anything could work.
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ื—ืฉื‘ืชื™ ืฉืื ื–ื” ื™ืฆืœื™ื—, ื”ื›ืœ ื™ื›ื•ืœ ืœื”ืฆืœื™ื—.
07:17
So I had the computer program design the short staples to do this.
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ื›ืชื‘ืชื™ ืชื•ื›ื ื” ืฉืชืขืฆื‘ ืืช ื”ืžื”ื“ืงื™ื ืฉื™ืขืฉื• ื–ืืช.
07:20
I ordered them; they came by FedEx.
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ื”ื–ืžื ืชื™ ืื•ืชื, ื”ื ื”ื’ื™ืขื• ื‘ื“ื•ืืจ ืฉืœื™ื—ื™ื.
07:22
I mixed them up, heated them, cooled them down,
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ืขืจื‘ื‘ืชื™ ืื•ืชื, ื—ื™ืžืžืชื™, ืงื™ืจืจืชื™
07:24
and I got 50 billion little smiley faces
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ื•ืงื™ื‘ืœืชื™ 50 ืžื™ืœื™ืืจื“ ืกืžื™ื™ืœื™ื ืงื˜ื ื˜ื ื™ื
07:28
floating around in a single drop of water.
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ืžืจื—ืคื™ื ืžืกื‘ื™ื‘ ื‘ืชื•ืš ื˜ื™ืคืช ืžื™ื ืื—ืช.
07:30
And each one of these is just
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ื›ืœ ืื—ื“ ืžื”ื ื‘ืขื•ื‘ื™ ืฉืœ
07:32
one-thousandth the width of a human hair, OK?
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ืืœืคื™ืช ืฉืœ ืฉืขืจื” ืื ื•ืฉื™ืช, ืื•ืงื™ื™?
07:36
So, they're all floating around in solution, and to look at them,
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ื›ื•ืœื ืžืจื—ืคื™ื ืœื”ื ืžืกื‘ื™ื‘ ื‘ืชื•ืš ื”ืชืžื™ืกื”, ื•ื›ื“ื™ ืœื”ืชื‘ื•ื ืŸ ื‘ื”ื
07:39
you have to get them on a surface where they stick.
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ืฆืจื™ืš ืœื”ื“ื‘ื™ืง ืื•ืชื ืขืœ ื’ื‘ื™ ืžืฉื˜ื—.
07:41
So, you pour them out onto a surface
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ืื– ืฉื•ืคื›ื™ื ืื•ืชื ืขืœ ื”ืžืฉื˜ื—
07:43
and they start to stick to that surface,
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ื•ื”ื ืžืชื—ื™ืœื™ื ืœื”ื™ื“ื‘ืง ืืœื™ื•.
07:45
and we take a picture using an atomic-force microscope.
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ืฆื™ืœืžื ื• ืชืžื•ื ื” ื‘ืขื–ืจืช ืžื™ืงืจื•ืกืงื•ืค ืื˜ื•ืžื™
07:47
It's got a needle, like a record needle,
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ืฉื™ืฉ ืœื• ืžื™ืŸ ืกื™ื›ื” ืฉืžืฆืœืžืช
07:49
that goes back and forth over the surface,
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ื”ืœื•ืš ื•ื—ื–ื•ืจ ืžืขืœ ืคื ื™ ื”ืžืฉื˜ื—
07:51
bumps up and down, and feels the height of the first surface.
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ืžื™ื˜ืœื˜ืœืช ื•ื—ืฉื” ืืช ื”ื’ื•ื‘ื” ืฉืœ ื”ืžืฉื˜ื—.
07:54
It feels the DNA origami.
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ื”ื™ื ื—ืฉื” ืืช ืื•ืจื™ื’ืžื™ ื”-DNA.
07:56
There's the atomic-force microscope working
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ื”ื ื” ื”ืžื™ืงืจื•ืกืงื•ืค ื”ืื˜ื•ืžื™ ื‘ืคืขื•ืœื”
07:59
and you can see that the landing's a little rough.
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ื•ืืชื ื™ื›ื•ืœื™ื ืœืจืื•ืช ืฉื”ื’ื™ืžื•ืจ ืงืฆืช ื’ืก.
08:00
When you zoom in, they've got, you know,
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ื›ืฉืžืกืชื›ืœื™ื ืžืงืจื•ื‘ ื™ืฉ ืœื”ื, ืืชื ื™ื•ื“ืขื™ื,
08:02
weak jaws that flip over their heads
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ืœืกืชื•ืช ืจื•ืคืคื•ืช ื•ืžืขื•ืงืžื•ืช
08:03
and some of their noses get punched out, but it's pretty good.
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ืื• ืืคื™ื ืžืขื•ื›ื™ื, ืื‘ืœ ื–ื” ื ืจืื” ื“ื™ ื˜ื•ื‘.
08:06
You can zoom in and even see the extra little loop,
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ืžืงืจื•ื‘ ืชืจืื• ืืคื™ืœื• ืืช ื”ืœื•ืœืื” ื”ื ื•ืกืคืช,
08:08
this little nano-goatee.
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ื ืื ื•-ื–ืงืŸ ืชื™ืฉ.
08:10
Now, what's great about this is anybody can do this.
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ืžื” ืฉืื“ื™ืจ ื‘ืกื™ืคื•ืจ ื”ื•ื ืฉื›ืœ ืื—ื“ ื™ื›ื•ืœ ืœืขืฉื•ืช ืืช ื–ื”.
08:13
And so, I got this in the mail about a year after I did this, unsolicited.
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ืœืžืฉืœ ืงื™ื‘ืœืชื™ ืืช ื–ื” ื‘ื“ื•ืืจ, ืžืฉื”ื• ื›ืžื• ืฉื ื” ืื—ืจื™ ื”ื ื™ืกื•ื™, ืžื‘ืœื™ ืฉื‘ื™ืงืฉืชื™.
08:17
Anyone know what this is? What is it?
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ืžื™ืฉื”ื• ื™ื•ื“ืข ืžื” ื–ื”?
08:20
It's China, right?
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ื–ืืช ืกื™ืŸ, ื ื›ื•ืŸ?
08:22
So, what happened is, a graduate student in China,
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ื‘ื•ื’ืจืช ืชื•ืืจ ืจืืฉื•ืŸ ื‘ืกื™ืŸ,
08:24
Lulu Qian, did a great job.
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ืœื•ืœื• ืฆ'ื™ืืŸ, ืขืฉืชื” ืขื‘ื•ื“ื” ืžืฆื•ื™ื ืช.
08:26
She wrote all her own software
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ื”ื™ื ื›ืชื‘ื” ืชื•ื›ื ื” ืžืฉืœื”
08:28
to design and built this DNA origami,
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ืฉืขื™ืฆื‘ื” ื•ื‘ื ืชื” ืืช ืื•ืจื™ื’ืžื™ ื”-DNA ื”ื–ื”
08:30
a beautiful rendition of China, which even has Taiwan,
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ืฉื™ืงื•ืฃ ืžืงืกื™ื ืฉืœ ืกื™ืŸ. ื™ืฉ ื›ืืŸ ืืคื™ืœื• ืืช ื˜ื™ื™ื•ื•ืืŸ
08:33
and you can see it's sort of on the world's shortest leash, right?
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ื”ื™ื ื›ืื™ืœื• ื™ื•ืฉื‘ืช ืขืœ ื”ืจืฆื•ืขื” ื”ืงืฆืจื” ื‘ื™ื•ืชืจ ื‘ืขื•ืœื, ืœื?
08:36
(Laughter)
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(ืฆื—ื•ืง)
08:39
So, this works really well
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ื–ื” ืขื•ื‘ื“ ืœื ืจืข,
08:41
and you can make patterns as well as shapes, OK?
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ื•ืืคืฉืจ ืœื™ืฆื•ืจ ืขื ื–ื” ืชื‘ื ื™ื•ืช ื•ืฆื•ืจื•ืช.
08:44
And you can make a map of the Americas and spell DNA with DNA.
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ืืคืฉืจ ืœื™ืฆื•ืจ ืžืคื” ืฉืœ ืืžืจื™ืงื” ืื• ืœื›ืชื•ื‘ DNA ื‘ืขื–ืจืช DNA.
08:47
And what's really neat about it --
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ื•ืžื” ืฉืžืžืฉ ืžื“ืœื™ืง ื‘ื›ืœ ื–ื”,
08:50
well, actually, this all looks like nano-artwork,
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ื—ื•ืฅ ืžื–ื” ืฉื–ืืช ืžืžืฉ ื ืื ื•-ืืžื ื•ืช,
08:52
but it turns out that nano-artwork
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ืื‘ืœ ื ืื ื•-ืืžื ื•ืช ื–ื•
08:53
is just what you need to make nano-circuits.
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ื”ื™ื ื‘ื“ื™ื•ืง ืžื” ืฉืฆืจื™ืš ื›ื“ื™ ืœื™ืฆื•ืจ ื ืื ื•-ืžืขื’ืœื™ื ื—ืฉืžืœื™ื™ื.
08:55
So, you can put circuit components on the staples,
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ื›ืš, ืฉืืคืฉืจ ืœืฉื™ื ืขืœ ืžื”ื“ืงื™ื ืจื›ื™ื‘ื™ื ืฉืœ ืžืขื’ืœื™ื,
08:57
like a light bulb and a light switch.
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ื›ืžื• ื ื•ืจื” ืื• ืžืชื’ ืœื ื•ืจื”,
08:59
Let the thing assemble, and you'll get some kind of a circuit.
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ืœื”ืจื›ื™ื‘ ืืช ื”ืกื™ืคื•ืจ, ื•ืœืงื‘ืœ ืกื•ื’ ืฉืœ ืžืขื’ืœ.
09:02
And then you can maybe wash the DNA away and have the circuit left over.
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ื•ืื– ืื•ืœื™ ืœืฉื˜ื•ืฃ ืืช ื”-DNA ื•ืœื”ื™ืฉืืจ ืขื ื”ืžืขื’ืœ ืขืฆืžื•.
09:05
So, this is what some colleagues of mine at Caltech did.
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ื–ื” ืžื” ืฉืขืžื™ืชื™ื ืฉืœื™ ื‘ืงืืœ-ื˜ืง ืขืฉื•.
09:07
They took a DNA origami, organized some carbon nano-tubes,
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ื”ื ืœืงื—ื• ืื•ืจื™ื’ืžื™ DNA, ืืจื’ื ื• ื›ืžื” ื ืื ื•-ืฆื™ื ื•ืจื•ืช ืคื—ืžืŸ,
09:10
made a little switch, you see here, wired it up,
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ื”ื›ื™ื ื• ืžืคืกืง ืงื˜ืŸ, ื—ื™ื•ื•ื˜ื• ื”ื›ืœ ื™ื—ื“,
09:12
tested it and showed that it is indeed a switch.
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ื‘ื“ืงื• ื•ื”ื•ื›ื™ื—ื• ืฉื–ื” ืื›ืŸ ืžืคืกืง.
09:15
Now, this is just a single switch
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ืขื›ืฉื™ื• ื–ื” ืจืง ืžืคืกืง ืื—ื“
09:17
and you need half a billion for a computer, so we have a long way to go.
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ื•ื‘ืฉื‘ื™ืœ ืžื—ืฉื‘ ืฆืจื™ืš ื—ืฆื™ ืžื™ืœื™ืืจื“, ืื– ื™ืฉ ืœื ื• ืขื•ื“ ื›ื‘ืจืช ื“ืจืš ืืจื•ื›ื”.
09:21
But this is very promising
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ืื‘ืœ ื–ื” ืžืื•ื“ ืžื‘ื˜ื™ื—
09:23
because the origami can organize parts just one-tenth the size
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ื›ื™ ื”ืื•ืจื™ื’ืžื™ ืžืืจื’ืŸ ื›ื›ื” ื—ืœืงื™ื ื‘ื’ื•ื“ืœ ืฉืœ ืขืฉื™ืจื™ืช
09:28
of those in a normal computer.
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ืžื”ืžืฆื•ื™ื™ื ื‘ืžื—ืฉื‘ ืจื’ื™ืœ,
09:29
So it's very promising for making small computers.
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ืื– ื™ื›ื•ืœ ืœื”ื™ื•ืช ืœื–ื” ืคื•ื˜ื ืฆื™ืืœ ื‘ื™ื™ืฆื•ืจ ืžื—ืฉื‘ื™ื ื–ืขื™ืจื™ื.
09:32
Now, I want to get back to that compiler.
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ืื ื™ ืจื•ืฆื” ืœื—ื–ื•ืจ ืœืงื•ืžืคื™ื™ืœืจ ื”ื”ื•ื.
09:35
The DNA origami is a proof that that compiler actually works.
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ืื•ืจื™ื’ืžื™ DNA ื”ื•ื ื”ื•ื›ื—ื” ืฉื”ืงื•ืžืคื™ื™ืœืจ ื”ื”ื•ื ืžืžืฉ ืขื•ื‘ื“.
09:39
So, you start with something in the computer.
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ืžืชื—ื™ืœื™ื ืžืฉื”ื• ื‘ืžื—ืฉื‘.
09:41
You get a high-level description of the computer program,
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ื›ื•ืชื‘ื™ื ืชื™ืื•ืจ ืžื•ืคืฉื˜ ืฉืœ ืชื•ื›ื ืช ื”ืžื—ืฉื‘,
09:44
a high-level description of the origami.
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ืชื™ืื•ืจ ื‘ืฉืคื” ืขื™ืœื™ืช ืฉืœ ื”ืื•ืจื™ื’ืžื™.
09:46
You can compile it to molecules, send it to a synthesizer,
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ืื– ืžืชืจื’ืžื™ื ืื•ืชื• ืœืžื•ืœืงื•ืœื•ืช ื•ืฉื•ืœื—ื™ื ืœืกื™ื ืชืกื™ื™ื–ืจ
09:49
and it actually works.
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ื•ื”ืขืกืง ืขื•ื‘ื“.
09:50
And it turns out that a company has made a nice program
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ืžืกืชื‘ืจ ืฉื—ื‘ืจื” ืื—ืช ื”ื›ื™ื ื” ืชื•ื›ื ื” ื ื—ืžื“ื”
09:54
that's much better than my code, which was kind of ugly,
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ื”ืจื‘ื” ื™ื•ืชืจ ื˜ื•ื‘ื” ืžื”ืงื•ื“, ื”ื“ื™-ืžื›ื•ืขืจ ืฉื›ืชื‘ืชื™
09:56
and will allow us to do this in a nice,
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ืฉืžืืคืฉืจ ืœื‘ืฆืข ื–ืืช ื‘ืฆื•ืจื” ื ื—ืžื“ื”,
09:57
visual, computer-aided design way.
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ื•ื•ื™ื–ื•ืืœื™ืช, ืขื™ืฆื•ื‘-ื‘ืกื™ื•ืข-ืžื—ืฉื‘.
10:00
So, now you can say, all right,
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ืื– ืขื›ืฉื™ื• ืชื’ื™ื“ื• ื‘ืกื“ืจ,
10:01
why isn't DNA origami the end of the story?
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ืœืžื” ืื•ืจื™ื’ืžื™ DNA ื”ื•ื ืœื ืกื•ืฃ ื”ืกื™ืคื•ืจ?
10:03
You have your molecular compiler, you can do whatever you want.
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ื™ืฉ ืงื•ืžืคื™ื™ืœืจ ืžื•ืœืงื•ืœืจื™, ืืคืฉืจ ืœืขืฉื•ืช ื›ืœ ื“ื‘ืจ ืฉืจื•ืฆื™ื.
10:05
The fact is that it does not scale.
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ื”ื‘ืขื™ื” ื”ื™ื ืฉืœื ื ื™ืชืŸ ืœื”ื’ื“ื™ืœ ืืช ื”ืื•ืจื™ื’ืžื™ ื‘ืงื ื” ืžื™ื“ื”.
10:08
So if you want to build a human from DNA origami,
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ื›ืš ืฉืื ืชืจืฆื• ืœื‘ื ื•ืช ื‘ืŸ-ืื“ื ืžืื•ืจื™ื’ืžื™ DNA
10:11
the problem is, you need a long strand
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ื”ื‘ืขื™ื” ื”ื™ื ืฉืชืฆื˜ืจื›ื• ื’ื“ื™ืœ ืืจื•ืš
10:13
that's 10 trillion trillion bases long.
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ื‘ืื•ืจืš 10 ื˜ืจื™ืœื•ื ื™ ื˜ืจื™ืœื•ื ื™ื ืฉืœ ื‘ืกื™ืกื™ื,
10:16
That's three light years' worth of DNA,
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DNA ื‘ืื•ืจืš ืฉืœ ืฉืœื•ืฉ ืฉื ื•ืช ืื•ืจ,
10:18
so we're not going to do this.
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ืื– ืื ื—ื ื• ืœื ื ืฉืชืžืฉ ื‘ื–ื”.
10:20
We're going to turn to another technology,
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ืื ื—ื ื• ื ืคื ื” ืœื˜ื›ื ื•ืœื•ื’ื™ื” ืื—ืจืช
10:22
called algorithmic self-assembly of tiles.
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ืฉื ืงืจืืช ื”ืจื›ื‘ื” ืขืฆืžื™ืช ืืœื’ื•ืจื™ืชืžื™ืช ืฉืœ ืืจื™ื—ื™ื.
10:24
It was started by Erik Winfree,
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ืืจื™ืง ื•ื™ื ืคืจื™ ื”ืชื—ื™ืœ ืขื ื–ื”
10:26
and what it does,
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ื•ื”ืจืขื™ื•ืŸ ื”ื•ื
10:27
it has tiles that are a hundredth the size of a DNA origami.
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ืœื”ืฉืชืžืฉ ื‘ืืจื™ื—ื™ื ื‘ื’ื•ื“ืœ ืฉืœ ืžืื™ืช ืฉืœ ืื•ืจื™ื’ืžื™ DNA.
10:31
You zoom in, there are just four DNA strands
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ื›ืฉืžืชืงืจื‘ื™ื ืจื•ืื™ื ืฉื™ืฉ ืจืง ืืจื‘ืขื” ื’ื“ื™ืœื™ DNA
10:34
and they have little single-stranded bits on them
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ืขื ื–ื ื‘ื•ืช ืงื˜ื ื™ื ืฉืœ ื’ื“ื™ืœื™ื ื‘ื•ื“ื“ื™ื
10:36
that can bind to other tiles, if they match.
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ืฉืืคืฉืจ ืœืงืฉื•ืจ ืœืืจื™ื—ื™ื ืื—ืจื™ื ืžืชืื™ืžื™ื.
10:38
And we like to draw these tiles as little squares.
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ื•ื”ื™ื™ื ื• ืžืขื•ื ื™ื™ื ื™ื ืœืฆื™ื™ืจ ืืช ื”ืืจื™ื—ื™ื ื”ืืœื”
10:42
And if you look at their sticky ends, these little DNA bits,
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ืื ืชืกืชื›ืœื• ืขืœ ื”ืงืฆื•ื•ืช ื”ื“ื‘ื™ืงื™ื ืฉืœ ื”-DNA
10:44
you can see that they actually form a checkerboard pattern.
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ืชืจืื• ืฉื ื•ืฆืจืช ื›ืืŸ ื“ื•ื’ืžื” ืฉืœ ืœื•ื— ื“ืžืงื”.
10:47
So, these tiles would make a complicated, self-assembling checkerboard.
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ื”ืืจื™ื—ื™ื ื™ื•ืฆืจื™ื ืœื•ื— ื“ืžืงื” ืžืกื•ื‘ืš ืฉืžืจื›ื™ื‘ ืืช ืขืฆืžื•.
10:50
And the point of this, if you didn't catch that,
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ื”ื ืงื•ื“ื” ื”ื™ื, ืื ืœื ืชืคืกืชื,
10:52
is that tiles are a kind of molecular program
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ืฉื”ืืจื™ื—ื™ื ื”ื ืกื•ื’ ืฉืœ ืชื•ื›ื ื” ืžื•ืœืงื•ืœืจื™ืช
10:55
and they can output patterns.
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ืฉื™ื•ืฆืจืช ื“ื•ื’ืžืื•ืช.
10:58
And a really amazing part of this is
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ืžื” ืฉืžื“ื”ื™ื ื›ืืŸ ื‘ืžื™ื•ื—ื“ ื”ื•ื
11:00
that any computer program can be translated
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ืฉื›ืœ ืชื•ื›ื ืช ืžื—ืฉื‘ ืืคืฉืจ ืœืชืจื’ื
11:02
into one of these tile programs -- specifically, counting.
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ืœืชื•ื›ื ืช ืืจื™ื—ื™ื ื›ื–ืืช, ื•ื‘ืคืจื˜ ืชื•ื›ื ื” ืฉืกื•ืคืจืช.
11:05
So, you can come up with a set of tiles
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ืืคืฉืจ ืœื”ื‘ื™ื ืกื“ืจื” ืฉืœ ืืจื™ื—ื™ื
11:08
that when they come together, form a little binary counter
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ืฉื›ืฉืžืกื“ืจื™ื ืื•ืชื ื™ื—ื“ ืžืงื‘ืœื™ื ืžื•ื ื” ื‘ื™ื ืืจื™ ืงื˜ืŸ
11:11
rather than a checkerboard.
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ื‘ืžืงื•ื ืœื•ื— ื“ืžืงื”.
11:13
So you can read off binary numbers five, six and seven.
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ืื– ืืคืฉืจ ืœืงืจื•ื ืžืกืคืจื™ื ื‘ื™ื ืืจื™ื™ื ื›ืžื• ื—ืžืฉ, ืฉืฉ ื•ืฉื‘ืข,
11:16
And in order to get these kinds of computations started right,
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ื•ื›ื“ื™ ืฉื”ื—ื™ืฉื•ื‘ื™ื ื”ืืœื• ื™ืฆืื• ื ื›ื•ืŸ,
11:19
you need some kind of input, a kind of seed.
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ืฆืจื™ืš ืื™ื–ืฉื”ื• ืงืœื˜, ืื• ื’ืจืขื™ืŸ.
11:21
You can use DNA origami for that.
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ืืคืฉืจ ืœื”ืฉืชืžืฉ ื‘ืื•ืจื™ื’ืžื™ DNA ืœืฉื ื›ืš.
11:23
You can encode the number 32
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ืืคืฉืจ ืœืงื•ื“ื“ ืืช ื”ืžืกืคืจ 32
11:25
in the right-hand side of a DNA origami,
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ื‘ืฆื“ ื”ื™ืžื ื™ ืฉืœ ื”ืื•ืจื™ื’ืžื™
11:27
and when you add those tiles that count,
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ื•ื›ืฉื ื•ืกื™ืฃ ืืช ื”ืืจื™ื—ื™ื ืฉืกื•ืคืจื™ื
11:29
they will start to count -- they will read that 32
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ื”ื ื™ืชื—ื™ืœื• ืœืกืคื•ืจ, ื™ื’ื™ืขื• ืœ-32 ื”ื”ื•ื
11:32
and they'll stop at 32.
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ื•ื™ืขืฆืจื• ื‘-32.
11:34
So, what we've done is we've figured out a way
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ืžื” ืฉืงืจื” ื›ืืŸ ื”ื•ื ืฉื’ื™ืœื™ื ื• ืฉื™ื˜ื”
11:37
to have a molecular program know when to stop going.
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ืœื’ืจื•ื ืœืชื•ื›ื ื” ืžื•ืœืงื•ืœืจื™ืช ืœื“ืขืช ืžืชื™ ืœื”ืคืกื™ืง ืœื’ื“ื•ืœ.
11:40
It knows when to stop growing because it can count.
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ื”ื™ื ื™ื•ื“ืขืช ืœืขืฆื•ืจ ืืช ื”ื’ื“ื™ืœื” ื›ื™ ื”ื™ื ื™ื›ื•ืœื” ืœืกืคื•ืจ.
11:42
It knows how big it is.
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ื”ื™ื ื™ื•ื“ืขืช ืžื” ื”ื’ื•ื“ืœ ืฉืœื”.
11:44
So, that answers that sort of first question I was talking about.
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ื–ื” ืขื•ื ื” ืขืœ ืกื•ื’ ื”ืฉืืœื•ืช ื”ืจืืฉื•ืŸ ืฉื“ื™ื‘ืจืชื™ ืขืœื™ื•.
11:47
It doesn't tell us how babies do it, however.
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ืื‘ืœ ื–ื” ืœื ืขื•ื ื” ืœื ื• ืื™ืš ืชื™ื ื•ืงื•ืช ื™ื•ื“ืขื™ื ื–ืืช.
11:50
So now, we can use this counting to try and get at much bigger things
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ืื– ืขื›ืฉื™ื• ืืคืฉืจ ืœื”ืฉืชืžืฉ ื‘ืกืคื™ืจื” ื”ื–ืืช ื›ื“ื™ ืœื‘ื ื•ืช ื“ื‘ืจื™ื ื”ืจื‘ื” ื™ื•ืชืจ ื’ื“ื•ืœื™ื ืžืืฉืจ ืขื
11:54
than DNA origami could otherwise.
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ืกืชื ืื•ืจื™ื’ืžื™ DNA.
11:55
Here's the DNA origami, and what we can do
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ื”ื ื” ืื•ืจื™ื’ืžื™ DNA, ื•ืื ื—ื ื• ื™ื›ื•ืœื™ื
11:58
is we can write 32 on both edges of the DNA origami,
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ืœื›ืชื•ื‘ 32 ืขืœ ืฉื ื™ ื”ืงืฆื•ื•ืช ืฉืœื•
12:01
and we can now use our watering can
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ื•ื‘ืขื–ืจืช ื”ืžืฉืคืš ืฉืœื ื•
12:03
and water with tiles, and we can start growing tiles off of that
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ื ืฉืงื” ืื•ืชื• ื‘ืืจื™ื—ื™ื ื•ื ื•ื›ืœ ืœื”ืชื—ื™ืœ ืœื’ื“ืœ ืืจื™ื—ื™ื ืžืžื ื•
12:07
and create a square.
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ื•ืœื™ืฆื•ืจ ืจื™ื‘ื•ืข.
12:09
The counter serves as a template
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ื”ืžื•ื ื” ื”ื‘ื™ื ืืจื™ ืžืฉืžืฉ ื‘ืชื•ืจ ืฉื‘ืœื•ื ื”
12:12
to fill in a square in the middle of this thing.
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ืฉืชืฉืžืฉ ืœืžื™ืœื•ื™ ื”ืจื™ื‘ื•ืข ืฉื‘ืืžืฆืข ื”ื“ื‘ืจ ื”ื–ื”.
12:14
So, what we've done is we've succeeded
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ื‘ืขืฆื ื”ืฆืœื—ื ื•
12:15
in making something much bigger than a DNA origami
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ืœื™ืฆื•ืจ ืžืฉื”ื• ื”ืจื‘ื” ื™ื•ืชืจ ื’ื“ื•ืœ ืžืื•ืจื™ื’ืžื™ DNA
12:18
by combining DNA origami with tiles.
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ื›ืฉื—ื™ื‘ืจื ื• ืื•ืจื™ื’ืžื™ DNA ืขื ืืจื™ื—ื™ื.
12:21
And the neat thing about it is, is that it's also reprogrammable.
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ืžื” ืฉืžื“ืœื™ืง ื›ืืŸ ื”ื•ื ืฉืืคืฉืจ ืœืชื›ื ืช ืžื—ื“ืฉ.
12:24
You can just change a couple of the DNA strands in this binary representation
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ืื ืชืฉื ื• ื›ืžื” ื’ื“ื™ืœื™ DNA ื‘ื™ื™ืฆื•ื’ ื”ื‘ื™ื ืืจื™
12:28
and you'll get 96 rather than 32.
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ืชืงื‘ืœื• 96 ื‘ืžืงื•ื 32
12:31
And if you do that, the origami's the same size,
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ื•ื›ืฉืชืขืฉื• ื–ืืช ื”ืื•ืจื™ื’ืžื™ ื‘ืื•ืชื• ื’ื•ื“ืœ,
12:34
but the resulting square that you get is three times bigger.
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ืื‘ืœ ื”ืจื™ื‘ื•ืข ืฉื ื•ืฆืจ ื›ืชื•ืฆืื” ืžื›ืš ื’ื“ื•ืœ ืคื™ ืฉืœื•ืฉื”.
12:39
So, this sort of recapitulates
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ืื– ื›ื“ื™ ืœืกื›ื
12:40
what I was telling you about development.
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ืืช ืžื” ืฉืกื™ืคืจืชื™ ืœื›ื ืขืœ ื”ืชืคืชื—ื•ืช:
12:42
You have a very sensitive computer program
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ื™ืฉ ืœื›ื ืชื•ื›ื ืช ืžื—ืฉื‘ ืจื’ื™ืฉื”
12:45
where small changes -- single, tiny, little mutations --
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ืฉืžืชื•ืš ืฉื™ื ื•ื™ื™ื ืงืœื™ื - ืžื•ื˜ืฆื™ื•ืช ื–ืขื™ืจื•ืช, ืคืฆืคื•ื ื™ื•ืช, ื™ื—ื™ื“ื•ืช -
12:48
can take something that made one size square
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ืœื•ืงื—ืช ืžืฉื”ื• ืฉื™ืฆืจ ืจื™ื‘ื•ืข ื‘ื’ื•ื“ืœ ืื—ื“
12:50
and make something very much bigger.
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ื•ื”ื•ืคื›ืช ืื•ืชื• ืœืžืฉื”ื• ื”ืจื‘ื” ื™ื•ืชืจ ื’ื“ื•ืœ.
12:54
Now, this -- using counting to compute
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ืื ื ื•ืกื™ืฃ ืœื–ื” ืืช ื”ื™ื›ื•ืœืช ืœืกืคื•ืจ
12:57
and build these kinds of things
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ื›ื“ื™ ืœื—ืฉื‘ ื•ืœื‘ื ื•ืช ื“ื‘ืจื™ื
12:59
by this kind of developmental process
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ื‘ืฆื•ืจื” ืฉืœ ืชื”ืœื™ืš ื”ืชืคืชื—ื•ืชื™ ื›ื–ื”
13:01
is something that also has bearing on Craig Venter's question.
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ื ืชืงืจื‘ ืœืขื ื•ืช ืขืœ ื”ืฉืืœื” ืฉืœ ืงืจื™ื™ื’ ื•ื ื˜ืจ.
13:05
So, you can ask, how many DNA strands are required
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ืชื•ื›ืœื• ืœืฉืื•ืœ: ื›ืžื” ื’ื“ื™ืœื™ DNA ืฆืจื™ืš
13:07
to build a square of a given size?
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ื›ื“ื™ ืœื‘ื ื•ืช ืจื™ื‘ื•ืข ื‘ื’ื•ื“ืœ ื ืชื•ืŸ?
13:09
If we wanted to make a square of size 10, 100 or 1,000,
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ืื™ืœื• ืจืฆื™ื ื• ืœื™ืฆื•ืจ ืจื™ื‘ื•ืข ื‘ื’ื•ื“ืœ ืขืฉืจ, ืžืื” ืื• ืืœืฃ
13:14
if we used DNA origami alone,
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ื•ื”ืฉืชืžืฉื ื• ืจืง ื‘ืื•ืจื™ื’ืžื™ DNA,
13:16
we would require a number of DNA strands that's the square
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ื”ื™ื™ื ื• ืฆืจื™ื›ื™ื ืžืกืคืจ ื’ื“ื™ืœื™ DNA ืฉื”ื•ื ืจื™ื‘ื•ืข ืฉืœ
13:19
of the size of that square;
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ื’ื•ื“ืœ ื”ืจื™ื‘ื•ืข ื”ื–ื”
13:21
so we'd need 100, 10,000 or a million DNA strands.
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ื›ืœื•ืžืจ ื ืฆื˜ืจืš ืžืื”, ืขืฉืจืช ืืœืคื™ื ืื• ืžื™ืœื™ื•ืŸ ื’ื“ื™ืœื™ DNA.
13:23
That's really not affordable.
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ื–ื” ืžืžืฉ ืœื ื‘ื ื‘ื—ืฉื‘ื•ืŸ.
13:25
But if we use a little computation --
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ืื ืžื•ืกื™ืคื™ื ืœื–ื” ืงืฆืช ื—ื™ืฉื•ื‘ -
13:27
we use origami, plus some tiles that count --
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ืื•ืจื™ื’ืžื™ ื‘ืชื•ืกืคืช ื›ืžื” ืืจื™ื—ื™ื ืฉื™ื•ื“ืขื™ื ืœืกืคื•ืจ -
13:31
then we can get away with using 100, 200 or 300 DNA strands.
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ืืคืฉืจ ืœื”ืกืชืคืง ื‘ืžืื”, ืžืืชื™ื™ื ืื• ืฉืœื•ืฉ ืžืื•ืช ื’ื“ื™ืœื™ DNA.
13:34
And so we can exponentially reduce the number of DNA strands we use,
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ื›ื›ื” ืื ื—ื ื• ืžืงื˜ื™ื ื™ื ืžืขืจื™ื›ื™ืช ืืช ืžืกืคืจ ื’ื“ื™ืœื™ ื”-DNA
13:39
if we use counting, if we use a little bit of computation.
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ืื ืžืฉืชืžืฉื™ื ื‘ืกืคื™ืจื”, ืื ืžื•ืกื™ืคื™ื ืงืฆืช ื—ื™ืฉื•ื‘.
13:42
And so computation is some very powerful way
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ืœื›ืŸ ื—ื™ืฉื•ื‘ ื”ื•ื ื“ืจืš ื—ื–ืงื” ืžืื•ื“ ืœื”ืงื˜ื™ืŸ
13:45
to reduce the number of molecules you need to build something,
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ืืช ืžืกืคืจ ื”ืžื•ืœืงื•ืœื•ืช ื”ื“ืจื•ืฉื•ืช ื›ื“ื™ ืœื‘ื ื•ืช ืžืฉื”ื•,
13:48
to reduce the size of the genome that you're building.
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ื›ื“ื™ ืœื”ืงื˜ื™ืŸ ืืช ื’ื•ื“ืœ ื”ื’ื ื•ื ืฉืืชื ื‘ื•ื ื™ื.
13:51
And finally, I'm going to get back to that sort of crazy idea
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ืœืกื™ื•ื, ืื ื™ ืจื•ืฆื” ืœื—ื–ื•ืจ ืœืจืขื™ื•ืŸ ื”ืžื˜ื•ืจืฃ ื”ื”ื•ื
13:54
about computers building computers.
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ืฉืœ ืžื—ืฉื‘ื™ื ืฉื‘ื•ื ื™ื ืžื—ืฉื‘ื™ื.
13:56
If you look at the square that you build with the origami
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ืื ืชืกืชื›ืœื• ืขืœ ื”ืจื™ื‘ื•ืข ืฉื‘ื ื™ืชื ืขื ื”ืื•ืจื™ื’ืžื™
13:59
and some counters growing off it,
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ื‘ืชื•ืกืคืช ื›ืžื” ืžื•ื ื™ื ืฉื™ื•ืฆืื™ื ืžืžื ื•,
14:01
the pattern that it has is exactly the pattern that you need
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ื”ืชื‘ื ื™ืช ืฉืœ ื–ื” ื”ื™ื ื‘ื“ื™ื•ืง ื”ืชื‘ื ื™ืช ืฉืฆืจื™ืš
14:04
to make a memory.
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ื‘ืฉื‘ื™ืœ ื–ื™ื›ืจื•ืŸ.
14:05
So if you affix some wires and switches to those tiles --
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ืื ืชืฆืžื™ื“ื• ื›ืžื” ื—ื•ื˜ื™ื ื•ืžืคืกืงื™ื ืœืืจื™ื—ื™ื ื”ืืœื”,
14:08
rather than to the staple strands, you affix them to the tiles --
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ื‘ืžืงื•ื ืœื’ื“ื™ืœื™ื ื”ืžื”ื“ืงื™ื ืชืฆืžื™ื“ื• ืื•ืชื ืœืืจื™ื—ื™ื,
14:11
then they'll self-assemble the somewhat complicated circuits,
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ื”ื ื™ืจื›ื™ื‘ื• ื‘ืขืฆืžื ืžืขื’ืœื™ื ืžืกื•ื‘ื›ื™ื ื™ื—ืกื™ืช
14:14
the demultiplexer circuits, that you need to address this memory.
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ืื•ืชื ืžืขื’ืœื™ ืจื™ื‘ื•ื‘ ืฉื“ืจื•ืฉื™ื ื›ื“ื™ ืœื’ืฉืช ืœื–ื›ืจื•ืŸ ื”ื–ื”.
14:17
So you can actually make a complicated circuit
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ืืชื ื™ื›ื•ืœื™ื ืœืžืขืฉื” ืœื™ืฆื•ืจ ืžืขื’ืœ ืžื•ืจื›ื‘
14:19
using a little bit of computation.
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ื‘ืขื–ืจืช ืงืฆืช ื—ื™ืฉื•ื‘.
14:21
It's a molecular computer building an electronic computer.
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ื–ื” ืžื—ืฉื‘ ืžื•ืœืงื•ืœืจื™ ืฉื‘ื•ื ื” ืžื—ืฉื‘ ืืœืงื˜ืจื•ื ื™.
14:24
Now, you ask me, how far have we gotten down this path?
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ืขื›ืฉื™ื• ืชืฉืืœื•: ื›ืžื” ืจื—ื•ืง ื”ื’ืขื ื• ื‘ืžืกืœื•ืœ ื”ื–ื”?
14:27
Experimentally, this is what we've done in the last year.
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ื‘ืžื”ืœืš ื”ื ื™ืกื•ื™ื™ื, ื–ื” ืžื” ืฉื”ืฉื’ื ื• ื‘ืฉื ื” ื”ืื—ืจื•ื ื”:
14:30
Here is a DNA origami rectangle,
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ื”ื ื” ืžืœื‘ืŸ ืžืื•ืจื™ื’ืžื™ DNA
14:33
and here are some tiles growing from it.
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ื•ื”ื ื” ื›ืžื” ืืจื™ื—ื™ื ืฉื™ื•ืฆืื™ื ืžืžื ื•.
14:35
And you can see how they count.
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ืืชื ื™ื›ื•ืœื™ื ืœืจืื•ืช ืื™ืš ื”ื ืกื•ืคืจื™ื.
14:37
One, two, three, four, five, six, nine, 10, 11, 12, 17.
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ืื—ืช, ืฉืชื™ื™ื, ืฉืœื•ืฉ, ืืจื‘ืข, ื—ืžืฉ, ืฉืฉ... ืชืฉืข, ืขืฉืจ, ืื—ืช-ืขืฉืจื”, ืฉืชื™ื-ืขืฉืจื”, ืฉื‘ืข-ืขืฉืจื”.
14:49
So it's got some errors, but at least it counts up.
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ื™ืฉ ืœื• ื›ืžื” ืฉื’ื™ืื•ืช, ืื‘ืœ ืœืคื—ื•ืช ื”ื•ื ืกื•ืคืจ ื‘ืจืฆืฃ.
14:53
(Laughter)
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(ืฆื—ื•ืง)
14:54
So, it turns out we actually had this idea nine years ago,
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ืžืกืชื‘ืจ ืฉื”ืจืขื™ื•ืŸ ื”ื–ื” ื”ื™ื” ืงื™ื™ื ื›ื‘ืจ ืœืคื ื™ ืชืฉืข ืฉื ื™ื,
14:57
and that's about the time constant for how long it takes
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ื•ื–ื” ื‘ืขืจืš ืงื‘ื•ืข ื”ื–ืžืŸ ืฉืœื•ืงื—
15:00
to do these kinds of things, so I think we made a lot of progress.
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ืœื‘ืฆืข ืืช ื”ื“ื‘ืจื™ื ื”ืืœื”, ืื– ืื ื™ ื—ื•ืฉื‘ ืฉื”ืชืงื“ืžื ื• ื”ืจื‘ื”.
15:02
We've got ideas about how to fix these errors.
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ื™ืฉ ืœื ื• ืจืขื™ื•ื ื•ืช ืื™ืš ืœืชืงืŸ ืืช ื”ืฉื’ื™ืื•ืช ื”ืืœื”,
15:04
And I think in the next five or 10 years,
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ื•ืœื“ืขืชื™ ืชื•ืš ื—ืžืฉ ืื• ืขืฉืจ ืฉื ื™ื
15:06
we'll make the kind of squares that I described
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ื ืฆืœื™ื— ืœื™ื™ืฆืจ ืืช ื”ืจื™ื‘ื•ืขื™ื ืฉืชื™ืืจืชื™
15:08
and maybe even get to some of those self-assembled circuits.
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ื•ืื•ืœื™ ืืคื™ืœื• ืœื”ื’ื™ืข ืœืžืขื’ืœื™ื ืฉืžืจื›ื™ื‘ื™ื ืืช ืขืฆืžื.
15:11
So now, what do I want you to take away from this talk?
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ืื– ืžื” ืื ื™ ืจื•ืฆื” ืฉืชืงื—ื• ืืชื›ื ืžื”ื”ืจืฆืื” ื”ื–ืืช?
15:15
I want you to remember that
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ืื ื™ ืจื•ืฆื” ืฉืชื–ื›ืจื•
15:17
to create life's very diverse and complex forms,
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ืฉืืช ื”ืžื‘ื ื™ื ื”ืžื•ืจื›ื‘ื™ื ื•ื”ืžื’ื•ื•ื ื™ื ืฉื™ืฉ ื‘ื˜ื‘ืข,
15:21
life uses computation to do that.
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ื”ื—ื™ื™ื ื™ื•ืฆืจื™ื ื‘ืขื–ืจืช ื—ื™ืฉื•ื‘,
15:23
And the computations that it uses, they're molecular computations,
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ื•ื”ื—ื™ืฉื•ื‘ื™ื ืฉื”ื—ื™ื™ื ืžื‘ืฆืขื™ื ื”ื ื—ื™ืฉื•ื‘ื™ื ืžื•ืœืงื•ืœืจื™ื™ื.
15:27
and in order to understand this and get a better handle on it,
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ื›ื“ื™ ืœื”ื‘ื™ืŸ ื•ืœืชืคื•ืก ืืช ื–ื” ื˜ื•ื‘ ื™ื•ืชืจ,
15:29
as Feynman said, you know,
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ื›ืžื• ืฉืคื™ื™ื ืžืŸ ืืžืจ,
15:31
we need to build something to understand it.
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ืฆืจื™ืš ืœื‘ื ื•ืช ืžืฉื”ื• ื›ื“ื™ ืœื”ื‘ื™ืŸ ืื•ืชื•.
15:33
And so we are going to use molecules and refashion this thing,
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ืœื›ืŸ ืื ื—ื ื• ืžืฉืชืžืฉื™ื ื‘ืžื•ืœืงื•ืœื•ืช ื•ืžืขืฆื‘ื™ื ืžื—ื“ืฉ ืืช ื”ื“ื‘ืจ ื”ื–ื”,
15:37
rebuild everything from the bottom up,
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ื‘ื•ื ื™ื ื”ื›ืœ ืžื—ื“ืฉ ืžื”ื™ืกื•ื“ ื›ืœืคื™ ืžืขืœื”,
15:39
using DNA in ways that nature never intended,
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ืžืฉืชืžืฉื™ื ื‘-DNA ื‘ื“ืจื›ื™ื ืฉื”ื˜ื‘ืข ืœื ืชื›ื ืŸ,
15:42
using DNA origami,
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ื‘ืขื–ืจืช ืื•ืจื™ื’ืžื™ DNA
15:44
and DNA origami to seed this algorithmic self-assembly.
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ืฉื”ื•ื ื”ื’ืจืขื™ืŸ ืฉืžืจื›ื™ื‘ ืืช ืขืฆืžื• ื‘ืฆื•ืจื” ืืœื’ื•ืจื™ืชืžื™ืช.
15:47
You know, so this is all very cool,
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ืื– ื›ืœ ื–ื” ืžืžืฉ ืžื’ื ื™ื‘,
15:50
but what I'd like you to take from the talk,
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ืื‘ืœ ืžื” ืฉื”ื™ื™ืชื™ ืจื•ืฆื” ืฉืชืงื—ื• ืžื”ื”ืจืฆืื”,
15:51
hopefully from some of those big questions,
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ืื•ืœื™ ืžืื—ืช ื”ืฉืืœื•ืช ื”ื’ื“ื•ืœื•ืช,
15:53
is that this molecular programming isn't just about making gadgets.
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ื”ื•ื ืฉืชื›ื ื•ืช ืžื•ืœืงื•ืœืจื™ ืœื ืžืชืขืกืง ืจืง ื‘ื™ืฆื™ืจืช ื’ืื“ื’'ื˜ื™ื,
15:56
It's not just making about --
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ืœื ืจืง ื™ืฆื™ืจื” ืžืขืฆืžื ืฉืœ
15:58
it's making self-assembled cell phones and circuits.
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ื˜ืœืคื•ื ื™ื ืกืœื•ืœืจื™ื™ื ื•ืžืขื’ืœื™ื ื—ืฉืžืœื™ื™ื.
16:00
What it's really about is taking computer science
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ื”ืจืขื™ื•ืŸ ื”ื•ื ืœืจืชื•ื ืืช ืžื“ืขื™ ื”ืžื—ืฉื‘
16:02
and looking at big questions in a new light,
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ื›ื“ื™ ืœื”ืกืชื›ืœ ืขืœ ื”ืฉืืœื•ืช ื”ื’ื“ื•ืœื•ืช ื‘ืื•ืจ ื—ื“ืฉ,
16:05
asking new versions of those big questions
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ืœืฉืื•ืœ ืืช ื”ืฉืืœื•ืช ื”ื’ื“ื•ืœื•ืช ื‘ื’ืจืกื” ื—ื“ืฉื”
16:07
and trying to understand how biology
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ื•ืœื ืกื•ืช ืœื”ื‘ื™ืŸ ืื™ืš ื”ื‘ื™ื•ืœื•ื’ื™ื”
16:09
can make such amazing things. Thank you.
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ื™ื•ืฆืจืช ื“ื‘ืจื™ื ืžื“ื”ื™ืžื™ื ื›ืืœื”. ืชื•ื“ื”.
16:12
(Applause)
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(ืžื—ื™ืื•ืช ื›ืคื™ื™ื)
ืขืœ ืืชืจ ื–ื”

ืืชืจ ื–ื” ื™ืฆื™ื’ ื‘ืคื ื™ื›ื ืกืจื˜ื•ื ื™ YouTube ื”ืžื•ืขื™ืœื™ื ืœืœื™ืžื•ื“ ืื ื’ืœื™ืช. ืชื•ื›ืœื• ืœืจืื•ืช ืฉื™ืขื•ืจื™ ืื ื’ืœื™ืช ื”ืžื•ืขื‘ืจื™ื ืขืœ ื™ื“ื™ ืžื•ืจื™ื ืžื”ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืžืจื—ื‘ื™ ื”ืขื•ืœื. ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ื”ืžื•ืฆื’ื•ืช ื‘ื›ืœ ื“ืฃ ื•ื™ื“ืื• ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ ืžืฉื. ื”ื›ืชื•ื‘ื™ื•ืช ื’ื•ืœืœื•ืช ื‘ืกื ื›ืจื•ืŸ ืขื ื”ืคืขืœืช ื”ื•ื•ื™ื“ืื•. ืื ื™ืฉ ืœืš ื”ืขืจื•ืช ืื• ื‘ืงืฉื•ืช, ืื ื ืฆื•ืจ ืื™ืชื ื• ืงืฉืจ ื‘ืืžืฆืขื•ืช ื˜ื•ืคืก ื™ืฆื™ืจืช ืงืฉืจ ื–ื”.

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