The next software revolution: programming biological cells | Sara-Jane Dunn

翻译人员: Jiasi Hao 校对人员: psjmz mz

The second half of the last century was completely defined

上世纪后半叶，全然是一个

by a technological revolution:

被科学革命所定义的时代：

the software revolution.

软件革命。

The ability to program electrons on a material called silicon

在一种硅半导体材料上 对电子进行编程的能力

使得我们许多人曾无法想象的

made possible technologies, companies and industries

that were at one point unimaginable to many of us,

科技、公司和行业变为可能。

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

这如今已彻底改变了 世界运作的方式。

The first half of this century, though,

不过，本世纪上半叶

is going to be transformed by a new software revolution:

将要被一个 崭新的软件革命所转化：

生物软件革命。

the living software revolution.

And this will be powered by the ability to program biochemistry

在一种名为生物的材料上 对生物化学进行编程的能力

on a material called biology.

将会支持这一革命。

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

如此一来， 我们将能够利用生物特征

to generate new kinds of therapies,

去开发新型疗法，

to repair damaged tissue,

去修复受损组织，

去重编缺陷细胞，

to reprogram faulty cells

or even build programmable operating systems out of biochemistry.

甚至利用生物化学 构建一个可编程的操作系统。

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

如果我们能实现它—— 而且我们确实需要实现它——

其影响将会如此巨大，

its impact will be so enormous

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

以至于第一个软件革命， 相比之下，会变得微不足道。

这是因为生物软件 可以变革整个医疗，

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

agriculture and energy,

农业和能源领域，

and these are sectors that dwarf those dominated by IT.

以及那些被 IT 人员掌控的部门。

Imagine programmable plants that fix nitrogen more effectively

想象一下可编程植物： 能够更有效进行固氮，

或可以抵御新型真菌病原体，

or resist emerging fungal pathogens,

or even programming crops to be perennial rather than annual

甚至能够将农作物编程为 多年生而非一年生，

so you could double your crop yields each year.

使你的年产量可以翻倍。

That would transform agriculture

这会改变农业，

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

同时改变全球不断增长的 粮食需求的方法。

Or imagine programmable immunity,

或想象可编程的免疫力，

设计并利用能够指导 你免疫系统的分子设备

designing and harnessing molecular devices that guide your immune system

to detect, eradicate or even prevent disease.

去检测、根除，甚至预防疾病。

这将改变医疗，

This would transform medicine

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

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。

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

我们已经拥有很多 能让生物软件成为现实的工具。

We can precisely edit genes with CRISPR.

我们能使用 CRISPR 技术 精确编辑基因。

我们能每次重写一个遗传密码。

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

We can even build functioning synthetic circuits out of DNA.

我们甚至能利用 DNA 开发一个合成电路。

But figuring out how and when to wield these tools

但是摸索出 如何且何时使用这些工具

is still a process of trial and error.

依旧是一个试错的过程。

It needs deep expertise, years of specialization.

它要求极高的专业性 和多年的领域专精。

而且实验方法难以发现，

And experimental protocols are difficult to discover

往往更是难以复制。

and all too often, difficult to reproduce.

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

在生物领域，我们倾向 仅专注于局部，

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

但我们都知道有些东西，例如飞行， 单就羽毛进行研究，

by only studying feathers.

是无法理解其原理的。

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

所以生物编程还未能像 电脑编程那样简单。

And then to make matters worse,

更糟糕的是，

living systems largely bear no resemblance to the engineered systems

生物系统和你我 每天编写的工程系统

that you and I program every day.

几乎毫无相似之处。

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

相比工程系统， 生物系统能自我生产、

they self-organize,

自我组织，

they operate at molecular scales.

并以分子规模运作。

And these molecular-level interactions

这些分子层级的相互作用

lead generally to robust macro-scale output.

通常会导致稳健的宏观规模输出，

They can even self-repair.

它甚至可以自我修复。

Consider, for example, the humble household plant,

试想家中一盆不起眼的植物，

like that one sat on your mantelpiece at home

比如你家壁炉台上的那盆

that you keep forgetting to water.

你老是忘记浇水的植物。

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

尽管你会忘记， 那盆植物每天都需要醒来

and figure out how to allocate its resources.

并思考如何分配它所有的资源。

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

它是生长、进行光合作用、 产生种子，还是开花？

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

这是这盆植物所需要做出的决定。

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

但一盆植物没有大脑来弄清这件事。

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

这需要其叶片上细胞的帮助。

They have to respond to the environment

它们需要针对环境做出反应，

and make the decisions that affect the whole plant.

并且做出影响整盆植物的决定。

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

所以在那些叶片细胞中 必定要有一个运行的程序，

a program that responds to input signals and cues

一个能响应输入信号与提示，

and shapes what that cell will do.

以及调整细胞行为的程序。

And then those programs must operate in a distributed way

之后，那些程序 必须以分布式运行，

across individual cells,

覆盖每一个细胞单元，

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

从而进行协作 让植物茁壮成长。

If we could understand these biological programs,

如果我们能够了解那些生物程序，

如果我们能够明白那些生物计算，

if we could understand biological computation,

it would transform our ability to understand how and why

这将会转变我们对细胞 的行为方式和行为原因的

cells do what they do.

理解能力。

Because, if we understood these programs,

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。

we could debug them when things go wrong.

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

或我们可以向它们学习 如何设计这样

that truly exploit the computational power of biochemistry.

能充分利用生物化学 计算能力的合成电路。

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

我对这个想法的热情， 让我进入了

at the interface of maths, computer science and biology.

数学、计算机科学 和生物学的交叉领域。

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

工作中，我专注于一个概念： 生物学计算。

And that means asking what do cells compute,

这代表着不断询问 细胞在计算什么，

and how can we uncover these biological programs?

以及我们如何能 解开这些生物程序的奥秘？

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

我开始和微软研究院与剑桥大学

at Microsoft Research and the University of Cambridge,

的一些出色的合作人士 一起询问这些问题，

where together we wanted to understand

我们想要了解

the biological program running inside a unique type of cell:

在一种独特细胞中 运行的生物程序：

an embryonic stem cell.

胚胎干细胞（ ES 细胞）。

These cells are unique because they're totally naïve.

这些细胞很独特，因为它们 非常稚嫩（即未高度分化）。

They can become anything they want:

它们能够分化 为它们想要变成的东西：

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

一个脑细胞，一个心脏细胞， 一个骨细胞，一个肺细胞，

任何一种成熟细胞。

any adult cell type.

这一稚嫩状态让这些细胞 变得与众不同，

This naïvety, it sets them apart,

but it also ignited the imagination of the scientific community,

但也激发了科学界的想象力。

who realized, if we could tap into that potential,

科学家们意识到， 如果我们能挖掘这一特性的潜力，

我们将会拥有一个 强大的医疗工具。

we would have a powerful tool for medicine.

If we could figure out how these cells make the decision

如果我们能搞清 这些细胞是如何决定

to become one cell type or another,

自己要发育为何种细胞的，

we might be able to harness them

我们或许能够利用 ES 细胞的这一能力，

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

生成我们需要的细胞， 来修复携带疾病的或受损的组织。

But realizing that vision is not without its challenges,

但这一愿景的实现存在着挑战，

not least because these particular cells,

不仅是因为这些特定细胞

they emerge just six days after conception.

在受孕的 6 天后才出现，

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

之后大约在 1 天内，就会消失。

They have set off down the different paths

它们走上了不同的道路，

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

共同形成成年人体 的所有结构和器官。

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

但事实证明，细胞的命运

比我们所想象的更具有可塑性。

than we might have imagined.

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

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：

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

通过把少量基因导入成熟细胞，

like one of your skin cells,

例如你的一个皮肤细胞，

you can transform that cell back to the naïve state.

你可以把这个成熟细胞 转化回未分化状态。

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

这一过程被称为“重编程”。

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

这让我们联想到 “干细胞乌托邦”，

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

这种能力可以采集 患者自身的细胞样本，

transform them back to the naïve state

将其转化回未分化的原始形态，

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

并使用那些细胞 制造患者可能需要的细胞，

whether it's brain cells or heart cells.

不论是脑细胞，还是心脏细胞。

But over the last decade or so,

但在过去的 10 年，

figuring out how to change cell fate,

搞清楚如何改变细胞命运

it's still a process of trial and error.

仍然是一个试错的过程。

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

即使是在那些我们已经发现了 成功实验方法的情况下，

they're still inefficient,

它们仍旧低效，

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

而且我们缺少关于 它们如何以及为何运作的基本理解。

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

如果你能摸清如何把一个干细胞 诱导为一个心脏细胞，

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

你依然不知道如何把一个干细胞

into a brain cell.

诱导为一个脑细胞。

So we wanted to understand the biological program

所以我们想要了解

running inside an embryonic stem cell,

在 ES 细胞中运行的生物程序，

而且，了解该生物系统中 所运行的计算

and understanding the computation performed by a living system

starts with asking a devastatingly simple question:

始于提出一个极为简单的问题：

What is it that system actually has to do?

这个系统到底需要做什么？

Now, computer science actually has a set of strategies

计算机科学实际上已有一套策略

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

来执行软件和硬件的功能。

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

当你编写程序时， 你用代码编写了一个软件，

you want that software to run correctly.

你希望这个软件能够正确运行，

You want performance, functionality.

你希望它具备完善的功能与性能，

You want to prevent bugs.

能防止错误，

They can cost you a lot.

做到这些的成本很高。

So when a developer writes a program,

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。

they could write down a set of specifications.

These are what your program should do.

这些是你的程序应该做的“工作”。

Maybe it should compare the size of two numbers

或许它能比较两个数的大小，

or order numbers by increasing size.

或将数字进行正序排序。

这样的技术存在： 允许我们自动检查

Technology exists that allows us automatically to check

whether our specifications are satisfied,

我们的代码是否符合技术规范，

whether that program does what it should do.

程序是否在完成它的本职工作。

And so our idea was that in the same way,

于是我们的想法很类似，

实验观察值，也就是 我们在实验室中测量的东西，

experimental observations, things we measure in the lab,

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

符合生物编程本职工作中 怎样的技术规范？

So we just needed to figure out a way

所以我们只需要找到一个方法

to encode this new type of specification.

来编译这个新型的技术规范。

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

比方说，你在实验室忙活了很久， 你一直在测量基因，

发现如果基因 A 是活跃的，

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

then Gene B or Gene C seems to be active.

那么基因 B 或 C 也会看似活跃。

We can write that observation down as a mathematical expression

如果我们能用一种逻辑语言， 就可以将这种观察

if we can use the language of logic:

编写为一种数学表达：

If A, then B or C.

如果 A ，那么 B 或 C 。

Now, this is a very simple example, OK.

这是一个非常简单的例子，

It's just to illustrate the point.

只是为了解释清楚我的意思。

We can encode truly rich expressions

我们可以编译很多丰富的表达，

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

在多个不同的实验中， 随着时间的推移，这些表达可以捕捉

across multiple different experiments.

多种基因或蛋白质的行为。

And so by translating our observations

运用这种方法， 把我们的观察值

into mathematical expression in this way,

编译为一种数学表达，

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

现在有可能测试这些观察结果 是否可以从基因相互作用

from a program of genetic interactions.

的程序中得到。

我们开发了一个工具 来实现这个目的。

And we developed a tool to do just this.

We were able to use this tool to encode observations

我们能用这个工具 将观察值编译为

as mathematical expressions,

数学表达。

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

该工具能让我们发现可以解释

that could explain them all.

所有原因的遗传程序。

And we then apply this approach

之后，我们运用这个方法

to uncover the genetic program running inside embryonic stem cells

来揭示 ES 细胞中运行的遗传程序，

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

来看看我们是否能理解 如何诱导未分化状态的细胞。

And this tool was actually built

这个工具实际上是建立在

经常被部署在世界各地 用于传统的软件验证

on a solver that's deployed routinely around the world

for conventional software verification.

的解算器上的。

So we started with a set of nearly 50 different specifications

我们从一套将近有 50 个 不同的技术规范开始，

that we generated from experimental observations of embryonic stem cells.

这些是我们从对 ES 细胞的 实验观察值中得出的。

And by encoding these observations in this tool,

利用这个工具， 通过编译这些观察值，

we were able to uncover the first molecular program

我们能够揭开第一个

that could explain all of them.

能够解释所有分子的程序。

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

这本身听着是一种壮举，是吧？

Being able to reconcile all of these different observations

将所有的观察值协调到一起，

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

不是那种你可以 在信封背面做的事情，

even if you have a really big envelope.

即使你有一个很大的信封。

Because we've got this kind of understanding,

因为我们有着这样的理解，

we could go one step further.

我们能够再进一步。

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

我们能够用这个程序 在尚未测试的条件下，

in conditions we hadn't yet tested.

来预测这个细胞可能会做什么。

We could probe the program in silico.

我们能够在硅上探索该程序。

And so we did just that:

所以我们行动了起来：

我们依据实验室检测值 生成了预测，

we generated predictions that we tested in the lab,

and we found that this program was highly predictive.

并发现该程序非常具有可预测性。

It told us how we could accelerate progress

它告诉我们如何能够

back to the naïve state quickly and efficiently.

加速细胞返回未分化状态的过程， 使之快速且有效。

It told us which genes to target to do that,

它告诉我们 可以针对哪些基因进行操作，

which genes might even hinder that process.

又有哪些基因会阻碍这一过程。

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

我们甚至发现了一个 能够预测基因开启顺序的程序。

So this approach really allowed us to uncover the dynamics

这个方法让我们得以

of what the cells are doing.

揭秘细胞行为的动态。

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

我们开发的不只是一种 仅限于干细胞生物的方法。

Rather, it allows us to make sense of the computation

相反，这能帮助我们理解

being carried out by the cell

在遗传相互作用的环境下

in the context of genetic interactions.

细胞内在的计算程序。

So really, it's just one building block.

所以这其实只是拼图中的一块。

The field urgently needs to develop new approaches

该领域急需开发新方法

to understand biological computation more broadly

来更广泛地在不同层次上

and at different levels,

了解生物计算，

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

从 DNA 到细胞间的信息流。

Only this kind of transformative understanding

只有这样的变革性理解

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

才能够使我们以可预测和可靠 的方式利用生物学。

但是对于编程生物学， 我们也将需要开发

But to program biology, we will also need to develop

允许实验人员和计算科学家

the kinds of tools and languages

that allow both experimentalists and computational scientists

使用的工具和语言

to design biological function

来设计生物函数，

并将这些设计编译成 细胞的机器代码，

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

its biochemistry,

也就是它的生物化学，

so that we could then build those structures.

这样我们就可以构建这些结构。

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

这就类似于一个 活的生物软件编译器，

我非常自豪能成为

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

微软开发此类软件团队的一员。

that's working to develop one.

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

尽管，说这是一个 很大的挑战有点轻描淡写，

but if it's realized,

但如果能实现，

it would be the final bridge between software and wetware.

这将会成为 软件和湿件最后的桥梁。

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，

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

if we can transform the field into being truly interdisciplinary.

编程生物学才会变成可能。

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

这需要我们搭建起 物理与生命科学的桥梁，

and scientists from each of these disciplines

来自相关学术背景的科学家们

需要能够利用共同语言进行合作，

need to be able to work together with common languages

and to have shared scientific questions.

并分享共同的科学问题。

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

长远来看，值得记住的是：

当我们第一次开始 在硅微芯片上编程时，

and the technology that you and I work with every day

几乎无法想象有一天会出现

could hardly have been imagined

at the time we first started programming on silicon microchips.

我们如今每天都需要打交道的 那些大型软件公司和技术。

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

如果我们现在开始思考

enabled by computational biology,

由计算生物学支持的科技潜能，

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

我们将会看到为实现这一目标

to make that a reality.

一路上需要做出的努力。

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

如今存在一种令人警醒的想法：

could be open to misuse.

这种科技可能会被滥用。

If we're willing to talk about the potential

如果我们愿意探讨

编程免疫细胞的潜力，

for programming immune cells,

we should also be thinking about the potential of bacteria

我们也应该考虑到 改造后的细菌成功躲避

engineered to evade them.

那些免疫细胞的可能。

There might be people willing to do that.

可能有些人打算从事这方面的研究。

Now, one reassuring thought in this

关于这个话题也存在 一个令人欣慰的想法——

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

科学家大概不这么认为——

is that biology is a fragile thing to work with.

生物太脆弱，在工作中难以把控。

So programming biology is not going to be something

所以编程生物学不会

you'll be doing in your garden shed.

进入你的生活。

But because we're at the outset of this,

但因为我们才刚起步，

we can move forward with our eyes wide open.

所以我们可以 大胆且谨慎的往前走。

We can ask the difficult questions up front,

我们可以事先提出难题，

we can put in place the necessary safeguards

我们可以采取必要的保护措施，

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

同时，作为其中的一部分， 还需要思考我们的道德标准，

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

我们将需要思考那些生物函数

of biological function.

实行的界限。

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

所以其中的生物伦理学研究 将被优先考虑。

It can't be relegated to second place

在令人激动的科学创新中，

in the excitement of scientific innovation.

这个话题不能屈居第二。

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

但我们这场旅行的最终目的地

would be breakthrough applications and breakthrough industries

将会是突破性的应用 以及突破性行业，

从农业，医疗，到能源和材料，

in areas from agriculture and medicine to energy and materials

and even computing itself.

甚至计算机技术本身。

Imagine, one day we could be powering the planet sustainably

试想，有一天，我们能 使用终极绿色能源

on the ultimate green energy

为地球提供可持续的动力，

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

因为我们已经能够模仿植物 在千年前发现的东西：

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

如何利用我们现有太阳能电池

by our current solar cells.

无法比拟的效率来利用太阳能。

If we understood that program of quantum interactions

如果我们能理解 让植物高效吸收太阳光的

that allow plants to absorb sunlight so efficiently,

量子相互作用的程序，

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

我们或许能将其编译为 能够为太阳能电池提供

that offer the material for better solar cells.

更好材料的合成 DNA 电路。

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

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，

如果这个课题能获得 足够的关注和正确的投资，

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

在未来的 10 或 15 年内， 或许就有可能实现。

it could be realized in 10 or 15 years.

So we are at the beginning of a technological revolution.

我们正处在科技革新的开端。

了解这种古老的生物计算类型

Understanding this ancient type of biological computation

is the critical first step.

是关键的第一步。

And if we can realize this,

如果我们能意识到这件事，

we would enter in the era of an operating system

就将进入一个拥有 能够运行生物软件

that runs living software.

的操作系统的时代。

Thank you very much.

非常感谢。

(Applause)

（掌声）