TEDSummit 2019
Sara-Jane Dunn: The next software revolution: programming biological cells
萨拉-简·邓恩: 下一场软件革命：生物细胞的编程
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你身体中的细胞就像是电脑软件：它们被“编程”以在特定时间完成特定功能。如果我们能够更好地了解这个过程，就能够解锁重编程细胞的能力，计算生物学家萨拉-简·邓恩（Sara-Jane Dunn）如是说。在这场关于前沿科学的演讲中，她解释了她的团队是如何研究胚胎干细胞（ES 细胞）从而对赋予生命活力的生物编程产生新的认识，并且开发能够转变医疗、农业和能源的“生物软件”。
Sara-Jane Dunn - Computational biologist
Sara-Jane Dunn is a scientist working at the interface between biology and computation, using mathematics and computational analysis to make sense of how living systems process information. Full bio
Sara-Jane Dunn is a scientist working at the interface between biology and computation, using mathematics and computational analysis to make sense of how living systems process information. Full bio
Double-click the English transcript below to play the video.
00:12
The second half of the last century
was completely defined
was completely defined
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上世纪后半叶，全然是一个
被科学革命所定义的时代：
00:17
by a technological revolution:
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软件革命。
00:19
the software revolution.
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在一种硅半导体材料上
对电子进行编程的能力
对电子进行编程的能力
00:21
The ability to program electrons
on a material called silicon
on a material called silicon
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使得我们许多人曾无法想象的
00:26
made possible technologies,
companies and industries
companies and industries
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科技、公司和行业变为可能。
00:29
that were at one point
unimaginable to many of us,
unimaginable to many of us,
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这如今已彻底改变了
世界运作的方式。
世界运作的方式。
00:33
but which have now fundamentally changed
the way the world works.
the way the world works.
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不过，本世纪上半叶
00:38
The first half of this century, though,
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00:40
is going to be transformed
by a new software revolution:
by a new software revolution:
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将要被一个
崭新的软件革命所转化：
崭新的软件革命所转化：
生物软件革命。
00:44
the living software revolution.
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在一种名为生物的材料上
对生物化学进行编程的能力
对生物化学进行编程的能力
00:46
And this will be powered by the ability
to program biochemistry
to program biochemistry
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将会支持这一革命。
00:50
on a material called biology.
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如此一来，
我们将能够利用生物特征
我们将能够利用生物特征
00:53
And doing so will enable us to harness
the properties of biology
the properties of biology
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去开发新型疗法，
00:57
to generate new kinds of therapies,
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去修复受损组织，
01:00
to repair damaged tissue,
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去重编缺陷细胞，
01:02
to reprogram faulty cells
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甚至利用生物化学
构建一个可编程的操作系统。
构建一个可编程的操作系统。
01:04
or even build programmable
operating systems out of biochemistry.
operating systems out of biochemistry.
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如果我们能实现它——
而且我们确实需要实现它——
而且我们确实需要实现它——
01:10
If we can realize this --
and we do need to realize it --
and we do need to realize it --
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其影响将会如此巨大，
01:14
its impact will be so enormous
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以至于第一个软件革命，
相比之下，会变得微不足道。
相比之下，会变得微不足道。
01:16
that it will make the first
software revolution pale in comparison.
software revolution pale in comparison.
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这是因为生物软件
可以变革整个医疗，
可以变革整个医疗，
01:20
And that's because living software
would transform the entirety of medicine,
would transform the entirety of medicine,
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农业和能源领域，
01:24
agriculture and energy,
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01:25
and these are sectors that dwarf
those dominated by IT.
those dominated by IT.
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以及那些被 IT 人员掌控的部门。
想象一下可编程植物：
能够更有效进行固氮，
能够更有效进行固氮，
01:30
Imagine programmable plants
that fix nitrogen more effectively
that fix nitrogen more effectively
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或可以抵御新型真菌病原体，
01:35
or resist emerging fungal pathogens,
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01:37
or even programming crops
to be perennial rather than annual
to be perennial rather than annual
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甚至能够将农作物编程为
多年生而非一年生，
多年生而非一年生，
使你的年产量可以翻倍。
01:41
so you could double
your crop yields each year.
your crop yields each year.
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这会改变农业，
01:43
That would transform agriculture
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同时改变全球不断增长的
粮食需求的方法。
粮食需求的方法。
01:45
and how we'll keep our growing
and global population fed.
and global population fed.
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或想象可编程的免疫力，
01:50
Or imagine programmable immunity,
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设计并利用能够指导
你免疫系统的分子设备
你免疫系统的分子设备
01:53
designing and harnessing molecular devices
that guide your immune system
that guide your immune system
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去检测、根除，甚至预防疾病。
01:57
to detect, eradicate
or even prevent disease.
or even prevent disease.
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这将改变医疗，
02:01
This would transform medicine
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同时改变我们试图保持
不断增长且老龄化的人口健康的方法。
不断增长且老龄化的人口健康的方法。
02:02
and how we'll keep our growing
and aging population healthy.
and aging population healthy.
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我们已经拥有很多
能让生物软件成为现实的工具。
能让生物软件成为现实的工具。
02:07
We already have many of the tools
that will make living software a reality.
that will make living software a reality.
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我们能使用 CRISPR 技术
精确编辑基因。
精确编辑基因。
02:11
We can precisely edit genes with CRISPR.
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我们能每次重写一个遗传密码。
02:14
We can rewrite the genetic code
one base at a time.
one base at a time.
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我们甚至能利用 DNA
开发一个合成电路。
开发一个合成电路。
02:17
We can even build functioning
synthetic circuits out of DNA.
synthetic circuits out of DNA.
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但是摸索出
如何且何时使用这些工具
如何且何时使用这些工具
02:22
But figuring out how and when
to wield these tools
to wield these tools
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依旧是一个试错的过程。
02:24
is still a process of trial and error.
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它要求极高的专业性
和多年的领域专精。
和多年的领域专精。
02:27
It needs deep expertise,
years of specialization.
years of specialization.
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而且实验方法难以发现，
02:31
And experimental protocols
are difficult to discover
are difficult to discover
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往往更是难以复制。
02:34
and all too often, difficult to reproduce.
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02:37
And, you know, we have a tendency
in biology to focus a lot on the parts,
in biology to focus a lot on the parts,
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在生物领域，我们倾向
仅专注于局部，
仅专注于局部，
但我们都知道有些东西，例如飞行，
单就羽毛进行研究，
单就羽毛进行研究，
02:41
but we all know that something like flying
wouldn't be understood
wouldn't be understood
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是无法理解其原理的。
02:44
by only studying feathers.
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所以生物编程还未能像
电脑编程那样简单。
电脑编程那样简单。
02:46
So programming biology is not yet
as simple as programming your computer.
as simple as programming your computer.
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更糟糕的是，
02:51
And then to make matters worse,
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02:53
living systems largely bear no resemblance
to the engineered systems
to the engineered systems
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生物系统和你我
每天编写的工程系统
每天编写的工程系统
几乎毫无相似之处。
02:57
that you and I program every day.
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相比工程系统，
生物系统能自我生产、
生物系统能自我生产、
02:59
In contrast to engineered systems,
living systems self-generate,
living systems self-generate,
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自我组织，
03:03
they self-organize,
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并以分子规模运作。
03:05
they operate at molecular scales.
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03:07
And these molecular-level interactions
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这些分子层级的相互作用
通常会导致稳健的宏观规模输出，
03:09
lead generally to robust
macro-scale output.
macro-scale output.
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它甚至可以自我修复。
03:12
They can even self-repair.
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试想家中一盆不起眼的植物，
03:16
Consider, for example,
the humble household plant,
the humble household plant,
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比如你家壁炉台上的那盆
03:19
like that one sat
on your mantelpiece at home
on your mantelpiece at home
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你老是忘记浇水的植物。
03:21
that you keep forgetting to water.
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尽管你会忘记，
那盆植物每天都需要醒来
那盆植物每天都需要醒来
03:23
Every day, despite your neglect,
that plant has to wake up
that plant has to wake up
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并思考如何分配它所有的资源。
03:27
and figure out how
to allocate its resources.
to allocate its resources.
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03:30
Will it grow, photosynthesize,
produce seeds, or flower?
produce seeds, or flower?
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它是生长、进行光合作用、
产生种子，还是开花？
产生种子，还是开花？
这是这盆植物所需要做出的决定。
03:33
And that's a decision that has to be made
at the level of the whole organism.
at the level of the whole organism.
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但一盆植物没有大脑来弄清这件事。
03:37
But a plant doesn't have a brain
to figure all of that out.
to figure all of that out.
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这需要其叶片上细胞的帮助。
03:41
It has to make do
with the cells on its leaves.
with the cells on its leaves.
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它们需要针对环境做出反应，
03:43
They have to respond to the environment
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并且做出影响整盆植物的决定。
03:45
and make the decisions
that affect the whole plant.
that affect the whole plant.
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所以在那些叶片细胞中
必定要有一个运行的程序，
必定要有一个运行的程序，
03:48
So somehow there must be a program
running inside these cells,
running inside these cells,
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一个能响应输入信号与提示，
03:52
a program that responds
to input signals and cues
to input signals and cues
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以及调整细胞行为的程序。
03:55
and shapes what that cell will do.
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03:57
And then those programs must operate
in a distributed way
in a distributed way
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之后，那些程序
必须以分布式运行，
必须以分布式运行，
覆盖每一个细胞单元，
04:00
across individual cells,
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从而进行协作
让植物茁壮成长。
让植物茁壮成长。
04:02
so that they can coordinate
and that plant can grow and flourish.
and that plant can grow and flourish.
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如果我们能够了解那些生物程序，
04:07
If we could understand
these biological programs,
these biological programs,
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如果我们能够明白那些生物计算，
04:11
if we could understand
biological computation,
biological computation,
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04:14
it would transform our ability
to understand how and why
to understand how and why
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这将会转变我们对细胞
的行为方式和行为原因的
的行为方式和行为原因的
理解能力。
04:18
cells do what they do.
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04:20
Because, if we understood these programs,
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因为，如果我们了解那些程序，
当出现问题时，
我们可以为它们排错。
我们可以为它们排错。
04:22
we could debug them when things go wrong.
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04:24
Or we could learn from them how to design
the kind of synthetic circuits
the kind of synthetic circuits
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或我们可以向它们学习
如何设计这样
如何设计这样
能充分利用生物化学
计算能力的合成电路。
计算能力的合成电路。
04:28
that truly exploit
the computational power of biochemistry.
the computational power of biochemistry.
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我对这个想法的热情，
让我进入了
让我进入了
04:34
My passion about this idea
led me to a career in research
led me to a career in research
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数学、计算机科学
和生物学的交叉领域。
和生物学的交叉领域。
04:37
at the interface of maths,
computer science and biology.
computer science and biology.
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04:41
And in my work, I focus on the concept
of biology as computation.
of biology as computation.
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工作中，我专注于一个概念：
生物学计算。
生物学计算。
这代表着不断询问
细胞在计算什么，
细胞在计算什么，
04:46
And that means asking
what do cells compute,
what do cells compute,
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以及我们如何能
解开这些生物程序的奥秘？
解开这些生物程序的奥秘？
04:49
and how can we uncover
these biological programs?
these biological programs?
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我开始和微软研究院与剑桥大学
04:53
And I started to ask these questions
together with some brilliant collaborators
together with some brilliant collaborators
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的一些出色的合作人士
一起询问这些问题，
一起询问这些问题，
04:57
at Microsoft Research
and the University of Cambridge,
and the University of Cambridge,
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05:00
where together we wanted to understand
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我们想要了解
在一种独特细胞中
运行的生物程序：
运行的生物程序：
05:02
the biological program
running inside a unique type of cell:
running inside a unique type of cell:
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胚胎干细胞（ ES 细胞）。
05:06
an embryonic stem cell.
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05:09
These cells are unique
because they're totally naïve.
because they're totally naïve.
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这些细胞很独特，因为它们
非常稚嫩（即未高度分化）。
非常稚嫩（即未高度分化）。
它们能够分化
为它们想要变成的东西：
为它们想要变成的东西：
05:12
They can become anything they want:
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一个脑细胞，一个心脏细胞，
一个骨细胞，一个肺细胞，
一个骨细胞，一个肺细胞，
05:14
a brain cell, a heart cell,
a bone cell, a lung cell,
a bone cell, a lung cell,
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任何一种成熟细胞。
05:17
any adult cell type.
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这一稚嫩状态让这些细胞
变得与众不同，
变得与众不同，
05:19
This naïvety, it sets them apart,
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但也激发了科学界的想象力。
05:20
but it also ignited the imagination
of the scientific community,
of the scientific community,
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科学家们意识到，
如果我们能挖掘这一特性的潜力，
如果我们能挖掘这一特性的潜力，
05:23
who realized, if we could
tap into that potential,
tap into that potential,
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我们将会拥有一个
强大的医疗工具。
强大的医疗工具。
05:27
we would have a powerful
tool for medicine.
tool for medicine.
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如果我们能搞清
这些细胞是如何决定
这些细胞是如何决定
05:29
If we could figure out
how these cells make the decision
how these cells make the decision
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自己要发育为何种细胞的，
05:32
to become one cell type or another,
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我们或许能够利用
ES 细胞的这一能力，
ES 细胞的这一能力，
05:34
we might be able to harness them
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生成我们需要的细胞，
来修复携带疾病的或受损的组织。
来修复携带疾病的或受损的组织。
05:36
to generate cells that we need
to repair diseased or damaged tissue.
to repair diseased or damaged tissue.
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但这一愿景的实现存在着挑战，
05:41
But realizing that vision
is not without its challenges,
is not without its challenges,
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不仅是因为这些特定细胞
05:44
not least because these particular cells,
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在受孕的 6 天后才出现，
05:47
they emerge just six days
after conception.
after conception.
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之后大约在 1 天内，就会消失。
05:50
And then within a day or so, they're gone.
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它们走上了不同的道路，
05:52
They have set off down the different paths
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共同形成成年人体
的所有结构和器官。
的所有结构和器官。
05:54
that form all the structures
and organs of your adult body.
and organs of your adult body.
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但事实证明，细胞的命运
05:59
But it turns out that cell fates
are a lot more plastic
are a lot more plastic
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比我们所想象的更具有可塑性。
06:02
than we might have imagined.
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大概在 13 年前，一些科学家们
展示了一些极具革命性的东西：
展示了一些极具革命性的东西：
06:04
About 13 years ago, some scientists
showed something truly revolutionary.
showed something truly revolutionary.
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通过把少量基因导入成熟细胞，
06:09
By inserting just a handful of genes
into an adult cell,
into an adult cell,
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例如你的一个皮肤细胞，
06:13
like one of your skin cells,
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你可以把这个成熟细胞
转化回未分化状态。
转化回未分化状态。
06:15
you can transform that cell
back to the naïve state.
back to the naïve state.
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这一过程被称为“重编程”。
06:19
And it's a process that's actually
known as "reprogramming,"
known as "reprogramming,"
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这让我们联想到
“干细胞乌托邦”，
“干细胞乌托邦”，
06:22
and it allows us to imagine
a kind of stem cell utopia,
a kind of stem cell utopia,
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06:26
the ability to take a sample
of a patient's own cells,
of a patient's own cells,
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这种能力可以采集
患者自身的细胞样本，
患者自身的细胞样本，
将其转化回未分化的原始形态，
06:29
transform them back to the naïve state
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并使用那些细胞
制造患者可能需要的细胞，
制造患者可能需要的细胞，
06:32
and use those cells to make
whatever that patient might need,
whatever that patient might need,
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不论是脑细胞，还是心脏细胞。
06:35
whether it's brain cells or heart cells.
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但在过去的 10 年，
06:38
But over the last decade or so,
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搞清楚如何改变细胞命运
06:40
figuring out how to change cell fate,
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仍然是一个试错的过程。
06:43
it's still a process of trial and error.
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即使是在那些我们已经发现了
成功实验方法的情况下，
成功实验方法的情况下，
06:45
Even in cases where we've uncovered
successful experimental protocols,
successful experimental protocols,
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它们仍旧低效，
06:50
they're still inefficient,
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而且我们缺少关于
它们如何以及为何运作的基本理解。
它们如何以及为何运作的基本理解。
06:51
and we lack a fundamental understanding
of how and why they work.
of how and why they work.
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如果你能摸清如何把一个干细胞
诱导为一个心脏细胞，
诱导为一个心脏细胞，
06:56
If you figured out how to change
a stem cell into a heart cell,
a stem cell into a heart cell,
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你依然不知道如何把一个干细胞
06:59
that hasn't got any way of telling you
how to change a stem cell
how to change a stem cell
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诱导为一个脑细胞。
07:02
into a brain cell.
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所以我们想要了解
07:04
So we wanted to understand
the biological program
the biological program
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在 ES 细胞中运行的生物程序，
07:07
running inside an embryonic stem cell,
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而且，了解该生物系统中
所运行的计算
所运行的计算
07:10
and understanding the computation
performed by a living system
performed by a living system
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始于提出一个极为简单的问题：
07:13
starts with asking
a devastatingly simple question:
a devastatingly simple question:
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这个系统到底需要做什么？
07:17
What is it that system actually has to do?
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07:21
Now, computer science actually
has a set of strategies
has a set of strategies
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计算机科学实际上已有一套策略
来执行软件和硬件的功能。
07:24
for dealing with what it is the software
and hardware are meant to do.
and hardware are meant to do.
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当你编写程序时，
你用代码编写了一个软件，
你用代码编写了一个软件，
07:28
When you write a program,
you code a piece of software,
you code a piece of software,
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你希望这个软件能够正确运行，
07:31
you want that software to run correctly.
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你希望它具备完善的功能与性能，
07:33
You want performance, functionality.
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能防止错误，
07:35
You want to prevent bugs.
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做到这些的成本很高。
07:36
They can cost you a lot.
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07:38
So when a developer writes a program,
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所以当一个开发者编写程序时，
他们能编写出一套技术规范。
07:40
they could write down
a set of specifications.
a set of specifications.
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这些是你的程序应该做的“工作”。
07:42
These are what your program should do.
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或许它能比较两个数的大小，
07:44
Maybe it should compare
the size of two numbers
the size of two numbers
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或将数字进行正序排序。
07:46
or order numbers by increasing size.
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这样的技术存在：
允许我们自动检查
允许我们自动检查
07:49
Technology exists that allows us
automatically to check
automatically to check
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我们的代码是否符合技术规范，
07:53
whether our specifications are satisfied,
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07:56
whether that program
does what it should do.
does what it should do.
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程序是否在完成它的本职工作。
于是我们的想法很类似，
07:59
And so our idea was that in the same way,
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实验观察值，也就是
我们在实验室中测量的东西，
我们在实验室中测量的东西，
08:02
experimental observations,
things we measure in the lab,
things we measure in the lab,
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符合生物编程本职工作中
怎样的技术规范？
怎样的技术规范？
08:05
they correspond to specifications
of what the biological program should do.
of what the biological program should do.
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所以我们只需要找到一个方法
08:10
So we just needed to figure out a way
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来编译这个新型的技术规范。
08:12
to encode this new type of specification.
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比方说，你在实验室忙活了很久，
你一直在测量基因，
你一直在测量基因，
08:16
So let's say you've been busy in the lab
and you've been measuring your genes
and you've been measuring your genes
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发现如果基因 A 是活跃的，
08:20
and you've found that if Gene A is active,
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那么基因 B 或 C 也会看似活跃。
08:22
then Gene B or Gene C seems to be active.
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如果我们能用一种逻辑语言，
就可以将这种观察
就可以将这种观察
08:26
We can write that observation down
as a mathematical expression
as a mathematical expression
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编写为一种数学表达：
08:30
if we can use the language of logic:
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08:33
If A, then B or C.
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如果 A ，那么 B 或 C 。
这是一个非常简单的例子，
08:36
Now, this is a very simple example, OK.
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只是为了解释清楚我的意思。
08:38
It's just to illustrate the point.
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我们可以编译很多丰富的表达，
08:40
We can encode truly rich expressions
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在多个不同的实验中，
随着时间的推移，这些表达可以捕捉
随着时间的推移，这些表达可以捕捉
08:43
that actually capture the behavior
of multiple genes or proteins over time
of multiple genes or proteins over time
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08:47
across multiple different experiments.
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多种基因或蛋白质的行为。
运用这种方法，
把我们的观察值
把我们的观察值
08:50
And so by translating our observations
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编译为一种数学表达，
08:53
into mathematical expression in this way,
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现在有可能测试这些观察结果
是否可以从基因相互作用
是否可以从基因相互作用
08:55
it becomes possible to test whether
or not those observations can emerge
or not those observations can emerge
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09:00
from a program of genetic interactions.
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的程序中得到。
我们开发了一个工具
来实现这个目的。
来实现这个目的。
09:04
And we developed a tool to do just this.
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我们能用这个工具
将观察值编译为
将观察值编译为
09:06
We were able to use this tool
to encode observations
to encode observations
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数学表达。
09:09
as mathematical expressions,
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09:10
and then that tool would allow us
to uncover the genetic program
to uncover the genetic program
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该工具能让我们发现可以解释
所有原因的遗传程序。
09:14
that could explain them all.
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之后，我们运用这个方法
09:17
And we then apply this approach
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来揭示 ES 细胞中运行的遗传程序，
09:19
to uncover the genetic program
running inside embryonic stem cells
running inside embryonic stem cells
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来看看我们是否能理解
如何诱导未分化状态的细胞。
如何诱导未分化状态的细胞。
09:23
to see if we could understand
how to induce that naïve state.
how to induce that naïve state.
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这个工具实际上是建立在
09:28
And this tool was actually built
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经常被部署在世界各地
用于传统的软件验证
用于传统的软件验证
09:30
on a solver that's deployed
routinely around the world
routinely around the world
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09:32
for conventional software verification.
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的解算器上的。
我们从一套将近有 50 个
不同的技术规范开始，
不同的技术规范开始，
09:35
So we started with a set
of nearly 50 different specifications
of nearly 50 different specifications
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这些是我们从对 ES 细胞的
实验观察值中得出的。
实验观察值中得出的。
09:39
that we generated from experimental
observations of embryonic stem cells.
observations of embryonic stem cells.
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利用这个工具，
通过编译这些观察值，
通过编译这些观察值，
09:43
And by encoding these
observations in this tool,
observations in this tool,
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我们能够揭开第一个
09:46
we were able to uncover
the first molecular program
the first molecular program
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能够解释所有分子的程序。
09:49
that could explain all of them.
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这本身听着是一种壮举，是吧？
09:52
Now, that's kind of a feat
in and of itself, right?
in and of itself, right?
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2513
将所有的观察值协调到一起，
09:54
Being able to reconcile
all of these different observations
all of these different observations
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2902
09:57
is not the kind of thing
you can do on the back of an envelope,
you can do on the back of an envelope,
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不是那种你可以
在信封背面做的事情，
在信封背面做的事情，
即使你有一个很大的信封。
10:00
even if you have a really big envelope.
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因为我们有着这样的理解，
10:04
Because we've got
this kind of understanding,
this kind of understanding,
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我们能够再进一步。
10:06
we could go one step further.
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我们能够用这个程序
在尚未测试的条件下，
在尚未测试的条件下，
10:07
We could use this program to predict
what this cell might do
what this cell might do
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来预测这个细胞可能会做什么。
10:11
in conditions we hadn't yet tested.
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我们能够在硅上探索该程序。
10:13
We could probe the program in silico.
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所以我们行动了起来：
10:16
And so we did just that:
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我们依据实验室检测值
生成了预测，
生成了预测，
10:18
we generated predictions
that we tested in the lab,
that we tested in the lab,
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10:21
and we found that this program
was highly predictive.
was highly predictive.
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并发现该程序非常具有可预测性。
它告诉我们如何能够
10:24
It told us how we could
accelerate progress
accelerate progress
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加速细胞返回未分化状态的过程，
使之快速且有效。
使之快速且有效。
10:26
back to the naïve state
quickly and efficiently.
quickly and efficiently.
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它告诉我们
可以针对哪些基因进行操作，
可以针对哪些基因进行操作，
10:29
It told us which genes
to target to do that,
to target to do that,
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又有哪些基因会阻碍这一过程。
10:32
which genes might even
hinder that process.
hinder that process.
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我们甚至发现了一个
能够预测基因开启顺序的程序。
能够预测基因开启顺序的程序。
10:35
We even found the program predicted
the order in which genes would switch on.
the order in which genes would switch on.
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这个方法让我们得以
10:40
So this approach really allowed us
to uncover the dynamics
to uncover the dynamics
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10:44
of what the cells are doing.
204
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揭秘细胞行为的动态。
我们开发的不只是一种
仅限于干细胞生物的方法。
仅限于干细胞生物的方法。
10:47
What we've developed, it's not a method
that's specific to stem cell biology.
that's specific to stem cell biology.
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相反，这能帮助我们理解
10:51
Rather, it allows us to make sense
of the computation
of the computation
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在遗传相互作用的环境下
10:54
being carried out by the cell
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10:55
in the context of genetic interactions.
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2831
细胞内在的计算程序。
所以这其实只是拼图中的一块。
10:58
So really, it's just one building block.
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该领域急需开发新方法
11:00
The field urgently needs
to develop new approaches
to develop new approaches
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来更广泛地在不同层次上
11:03
to understand biological
computation more broadly
computation more broadly
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了解生物计算，
11:06
and at different levels,
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1367
从 DNA 到细胞间的信息流。
11:07
from DNA right through
to the flow of information between cells.
to the flow of information between cells.
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只有这样的变革性理解
11:11
Only this kind of
transformative understanding
transformative understanding
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才能够使我们以可预测和可靠
的方式利用生物学。
的方式利用生物学。
11:14
will enable us to harness biology
in ways that are predictable and reliable.
in ways that are predictable and reliable.
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但是对于编程生物学，
我们也将需要开发
我们也将需要开发
11:21
But to program biology,
we will also need to develop
we will also need to develop
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允许实验人员和计算科学家
11:24
the kinds of tools and languages
217
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1995
11:26
that allow both experimentalists
and computational scientists
and computational scientists
218
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3408
使用的工具和语言
来设计生物函数，
11:29
to design biological function
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并将这些设计编译成
细胞的机器代码，
细胞的机器代码，
11:32
and have those designs compile down
to the machine code of the cell,
to the machine code of the cell,
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也就是它的生物化学，
11:35
its biochemistry,
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1181
11:36
so that we could then
build those structures.
build those structures.
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这样我们就可以构建这些结构。
这就类似于一个
活的生物软件编译器，
活的生物软件编译器，
11:39
Now, that's something akin
to a living software compiler,
to a living software compiler,
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我非常自豪能成为
11:43
and I'm proud to be
part of a team at Microsoft
part of a team at Microsoft
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微软开发此类软件团队的一员。
11:45
that's working to develop one.
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尽管，说这是一个
很大的挑战有点轻描淡写，
很大的挑战有点轻描淡写，
11:47
Though to say it's a grand challenge
is kind of an understatement,
is kind of an understatement,
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但如果能实现，
11:50
but if it's realized,
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这将会成为
软件和湿件最后的桥梁。
软件和湿件最后的桥梁。
11:51
it would be the final bridge
between software and wetware.
between software and wetware.
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但更广泛地说，如果我们
能够将其转变为真正的跨学科领域，
能够将其转变为真正的跨学科领域，
11:57
More broadly, though, programming biology
is only going to be possible
is only going to be possible
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12:00
if we can transform the field
into being truly interdisciplinary.
into being truly interdisciplinary.
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编程生物学才会变成可能。
这需要我们搭建起
物理与生命科学的桥梁，
物理与生命科学的桥梁，
12:04
It needs us to bridge
the physical and the life sciences,
the physical and the life sciences,
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来自相关学术背景的科学家们
12:07
and scientists from
each of these disciplines
each of these disciplines
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需要能够利用共同语言进行合作，
12:10
need to be able to work together
with common languages
with common languages
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并分享共同的科学问题。
12:12
and to have shared scientific questions.
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长远来看，值得记住的是：
12:16
In the long term, it's worth remembering
that many of the giant software companies
that many of the giant software companies
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3993
当我们第一次开始
在硅微芯片上编程时，
在硅微芯片上编程时，
12:20
and the technology
that you and I work with every day
that you and I work with every day
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几乎无法想象有一天会出现
12:23
could hardly have been imagined
237
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1503
我们如今每天都需要打交道的
那些大型软件公司和技术。
那些大型软件公司和技术。
12:24
at the time we first started
programming on silicon microchips.
programming on silicon microchips.
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如果我们现在开始思考
12:28
And if we start now to think about
the potential for technology
the potential for technology
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3031
由计算生物学支持的科技潜能，
12:31
enabled by computational biology,
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2426
我们将会看到为实现这一目标
12:33
we'll see some of the steps
that we need to take along the way
that we need to take along the way
241
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2935
一路上需要做出的努力。
12:36
to make that a reality.
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1433
如今存在一种令人警醒的想法：
12:39
Now, there is the sobering thought
that this kind of technology
that this kind of technology
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3082
这种科技可能会被滥用。
12:42
could be open to misuse.
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如果我们愿意探讨
12:44
If we're willing to talk
about the potential
about the potential
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编程免疫细胞的潜力，
12:46
for programming immune cells,
246
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1436
我们也应该考虑到
改造后的细菌成功躲避
改造后的细菌成功躲避
12:47
we should also be thinking
about the potential of bacteria
about the potential of bacteria
247
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3188
那些免疫细胞的可能。
12:50
engineered to evade them.
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1661
可能有些人打算从事这方面的研究。
12:52
There might be people willing to do that.
249
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2087
关于这个话题也存在
一个令人欣慰的想法——
一个令人欣慰的想法——
12:55
Now, one reassuring thought in this
250
763506
1722
科学家大概不这么认为——
12:57
is that -- well, less so
for the scientists --
for the scientists --
251
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2289
生物太脆弱，在工作中难以把控。
12:59
is that biology is
a fragile thing to work with.
a fragile thing to work with.
252
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3269
所以编程生物学不会
13:02
So programming biology
is not going to be something
is not going to be something
253
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2412
进入你的生活。
13:05
you'll be doing in your garden shed.
254
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1848
但因为我们才刚起步，
13:07
But because we're at the outset of this,
255
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2080
所以我们可以
大胆且谨慎的往前走。
大胆且谨慎的往前走。
13:09
we can move forward
with our eyes wide open.
with our eyes wide open.
256
777746
2583
我们可以事先提出难题，
13:12
We can ask the difficult
questions up front,
questions up front,
257
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2324
我们可以采取必要的保护措施，
13:14
we can put in place
the necessary safeguards
the necessary safeguards
258
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3040
同时，作为其中的一部分，
还需要思考我们的道德标准，
还需要思考我们的道德标准，
13:17
and, as part of that,
we'll have to think about our ethics.
we'll have to think about our ethics.
259
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2797
我们将需要思考那些生物函数
13:20
We'll have to think about putting bounds
on the implementation
on the implementation
260
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3172
实行的界限。
13:23
of biological function.
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所以其中的生物伦理学研究
将被优先考虑。
将被优先考虑。
13:25
So as part of this, research in bioethics
will have to be a priority.
will have to be a priority.
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在令人激动的科学创新中，
13:29
It can't be relegated to second place
263
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2407
这个话题不能屈居第二。
13:31
in the excitement
of scientific innovation.
of scientific innovation.
264
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2514
但我们这场旅行的最终目的地
13:35
But the ultimate prize,
the ultimate destination on this journey,
the ultimate destination on this journey,
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将会是突破性的应用
以及突破性行业，
以及突破性行业，
13:38
would be breakthrough applications
and breakthrough industries
and breakthrough industries
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从农业，医疗，到能源和材料，
13:42
in areas from agriculture and medicine
to energy and materials
to energy and materials
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甚至计算机技术本身。
13:45
and even computing itself.
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13:48
Imagine, one day we could be powering
the planet sustainably
the planet sustainably
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试想，有一天，我们能
使用终极绿色能源
使用终极绿色能源
为地球提供可持续的动力，
13:51
on the ultimate green energy
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因为我们已经能够模仿植物
在千年前发现的东西：
在千年前发现的东西：
13:53
if we could mimic something
that plants figured out millennia ago:
that plants figured out millennia ago:
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如何利用我们现有太阳能电池
13:57
how to harness the sun's energy
with an efficiency that is unparalleled
with an efficiency that is unparalleled
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无法比拟的效率来利用太阳能。
14:01
by our current solar cells.
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14:03
If we understood that program
of quantum interactions
of quantum interactions
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如果我们能理解
让植物高效吸收太阳光的
让植物高效吸收太阳光的
14:06
that allow plants to absorb
sunlight so efficiently,
sunlight so efficiently,
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量子相互作用的程序，
我们或许能将其编译为
能够为太阳能电池提供
能够为太阳能电池提供
14:09
we might be able to translate that
into building synthetic DNA circuits
into building synthetic DNA circuits
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14:13
that offer the material
for better solar cells.
for better solar cells.
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更好材料的合成 DNA 电路。
现在有一些团队和科学家
正着手于解决这个课题的基本问题，
正着手于解决这个课题的基本问题，
14:17
There are teams and scientists working
on the fundamentals of this right now,
on the fundamentals of this right now,
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如果这个课题能获得
足够的关注和正确的投资，
足够的关注和正确的投资，
14:21
so perhaps if it got the right attention
and the right investment,
and the right investment,
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在未来的 10 或 15 年内，
或许就有可能实现。
或许就有可能实现。
14:24
it could be realized in 10 or 15 years.
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我们正处在科技革新的开端。
14:27
So we are at the beginning
of a technological revolution.
of a technological revolution.
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了解这种古老的生物计算类型
14:31
Understanding this ancient type
of biological computation
of biological computation
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是关键的第一步。
14:34
is the critical first step.
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如果我们能意识到这件事，
14:36
And if we can realize this,
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就将进入一个拥有
能够运行生物软件
能够运行生物软件
14:37
we would enter in the era
of an operating system
of an operating system
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的操作系统的时代。
14:40
that runs living software.
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非常感谢。
14:42
Thank you very much.
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（掌声）
14:43
(Applause)
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ABOUT THE SPEAKER
Sara-Jane Dunn - Computational biologistSara-Jane Dunn is a scientist working at the interface between biology and computation, using mathematics and computational analysis to make sense of how living systems process information.
Why you should listen
Sara-Jane Dunn started her career as a mathematician, finding a niche in "mathematical biology" -- a field where mathematical models and simulations provide insights into the behavior and development of living systems. For Dunn, this research opened up new ways to explore old problems, from understanding how diseases spread to how cancer evolves. To this end, she created computational models of the gut that could be used to test hypotheses about the earliest stages of colorectal cancer.
At Microsoft Research, Dunn collaborates with experimenters in embryonic stem cell biology and seeks to uncover the biological program that governs how these unique cells are able to generate all of the different cell types of your adult body. This work could, in time, "unlock" biology and make cells programmable, which could fundamentally transform medicine, agriculture and even how we power the planet.
Sara-Jane Dunn | Speaker | TED.com