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

譯者: Helen Chang 審譯者: Bruce Sung

The second half of the last century was completely defined

上個世紀的後半

全由一場科技革命所定義：

by a technological revolution:

the software revolution.

軟體革命。

The ability to program electrons on a material called silicon

能在矽材料上編寫電子程式的能力，

made possible technologies, companies and industries

讓許多我們過去難以想像的

that were at one point unimaginable to many of us,

技術、公司和行業變為可能，

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

如今已從根本改變世界運作的方式。

The first half of this century, though,

不過本世紀的前半段

is going to be transformed by a new software revolution:

將被一場新的軟體革命轉變：

the living software revolution.

生物軟體革命。

And this will be powered by the ability to program biochemistry

促成這場革命的

將是在生物材料上 編寫生物化學的能力。

on a material called biology.

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

這讓我們能利用生物的特性

to generate new kinds of therapies,

來產生新療法，

to repair damaged tissue,

以修復受損的組織；

to reprogram faulty cells

重新編寫有瑕疵的細胞；

or even build programmable operating systems out of biochemistry.

甚至創造可編寫的 生物化學作業系統。

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

若能了解這一點—— 我們的確需要了解這一點——

its impact will be so enormous

它的影響會大到

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

讓第一場軟體革命相形失色。

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

那是因為生物軟體會轉變整個醫界、

agriculture and energy,

農業界，和能源界，

and these are sectors that dwarf those dominated by IT.

會讓那些 IT 主宰的部門 顯得無足輕重。

Imagine programmable plants that fix nitrogen more effectively

想像可編寫的植物能更效率地固氮，

or resist emerging fungal pathogens,

能抵抗新興的菌類病原體，

or even programming crops to be perennial rather than annual

甚至能把一年生的作物 編寫成多年生，

so you could double your crop yields each year.

使年產量加倍，

That would transform agriculture

那就會轉變農業，

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

確保不斷成長的全球人口 都有食物可吃。

Or imagine programmable immunity,

或可想像編寫免疫力，

designing and harnessing molecular devices that guide your immune system

設計利用分子裝置來引導免疫系統

to detect, eradicate or even prevent disease.

去偵測、根除，甚至預防疾病。

This would transform medicine

這會轉變醫學，

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

確保不斷成長和老化的人口 能夠維持健康。

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

目前已有許多工具 能讓生物軟體成真。

We can precisely edit genes with CRISPR.

我們能用 CRISPR 精確地編輯基因；

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

我們能夠重寫基因編碼， 一次重寫一個鹼基；

We can even build functioning synthetic circuits out of DNA.

我們甚至能用 DNA 做成能運作的合成電路。

But figuring out how and when to wield these tools

至於弄明白如何、何時使用這些工具

is still a process of trial and error.

則仍處於試誤的過程中，

It needs deep expertise, years of specialization.

需要很深的知識技術 和多年的專門化，

And experimental protocols are difficult to discover

而實驗操作方法難以發現，

and all too often, difficult to reproduce.

通常也難以重現。

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

我們通常傾向於聚焦在 生物學的「組件」，

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

但我們都知道，

要了解飛行不能只研究羽毛。

by only studying feathers.

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

因此，編寫生物學程式 仍不如編寫計算機程式那樣簡單。

And then to make matters worse,

更糟糕的是，

living systems largely bear no resemblance to the engineered systems

生命系統在很大程度上 與你我每天編寫的工程系統

that you and I program every day.

毫無相似之處。

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

與工程系統相反，

生命系統自我生成、自我組織，

they self-organize,

they operate at molecular scales.

以「分子」的規模運作。

And these molecular-level interactions

這些分子層級的相互作用

lead generally to robust macro-scale output.

通常導致大規模宏觀的輸出，

They can even self-repair.

甚至能自我修復。

Consider, for example, the humble household plant,

例如，試想不起眼的室內植物，

like that one sat on your mantelpiece at home

就像那放在家裡壁爐上

that you keep forgetting to water.

卻一直忘記澆水的植物。

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

儘管被你忽略了，

它每天仍必須弄清楚如何分配資源：

and figure out how to allocate its resources.

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

要生長、行光合作用、 結子，還是開花？

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

這必須考量整個有機體來做決定。

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

但植物沒大腦來解決這些問題，

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

必須依賴葉子上的細胞，

They have to respond to the environment

細胞必須對環境做出反應

and make the decisions that affect the whole plant.

和做出影響整株植物的決策。

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

因此，這些細胞裡 必定跑著某種程式，

a program that responds to input signals and cues

該程式回應輸入的信號，

提示和調整該細胞即將執行的操作。

and shapes what that cell will do.

And then those programs must operate in a distributed way

而這些程式必須 分佈在各個細胞間執行，

across individual cells,

彼此協調，

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

以便使植物生長繁衍。

If we could understand these biological programs,

若能理解這些生物的程式，

if we could understand biological computation,

若能理解生物的運算，

it would transform our ability to understand how and why

就會改變我們對細胞

如何、為何執行其工作的理解能力。

cells do what they do.

Because, if we understood these programs,

一旦了解這些程式，

我們就可以在出問題時偵錯；

we could debug them when things go wrong.

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

可以向它們學習如何設計

真正利用生物化學 運算能力的合成電路。

that truly exploit the computational power of biochemistry.

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

對這想法的熱情引領我進入

at the interface of maths, computer science and biology.

與數學、計算機科學 和生物學介面的研究領域。

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

我的工作專注於以生物運算的概念。

And that means asking what do cells compute,

這意味著探詢細胞如何運算，

and how can we uncover these biological programs?

與如何揭露這些生物的程式？

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

我開始與一些微軟研究所 和劍橋大學的傑出人士

at Microsoft Research and the University of Cambridge,

合作提出這些問題，

where together we wanted to understand

我們一起，想了解

the biological program running inside a unique type of cell:

在「胚胎幹細胞」 這種獨特類型的細胞中

an embryonic stem cell.

運行的生物程式。

These cells are unique because they're totally naïve.

這些細胞格非常獨特， 因為它們處於稚年。

They can become anything they want:

它們可以變成任何東西：

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

腦細胞、心臟細胞、 骨細胞、肺細胞，

any adult cell type.

任何成年細胞類型。

This naïvety, it sets them apart,

稚年使它們與眾不同，

but it also ignited the imagination of the scientific community,

也激發了科學界的想像力，

who realized, if we could tap into that potential,

科學家意識到， 如果能挖掘這種潛力，

we would have a powerful tool for medicine.

我們將擁有強大的醫學工具。

If we could figure out how these cells make the decision

如果能弄清楚這些細胞

如何決定成為哪一類型的細胞，

to become one cell type or another,

we might be able to harness them

或許我們能控制、 利用它們生成新細胞

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

來修復罹病或受損的組織。

But realizing that vision is not without its challenges,

但要實現此一願景面對著挑戰，

not least because these particular cells,

尤其是因為這些特殊的細胞

they emerge just six days after conception.

在受孕後僅六天就出現了。

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

然後一天左右就消失了，

They have set off down the different paths

踏上構成成年人人體的 各個結構和器官的不同途徑。

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

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

但事實上細胞的命運 比我們想像的要可塑得多。

than we might have imagined.

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

大約 13 年前，有些科學家 展示真正具有革命意義的東西。

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

通過將少量基因插入成年細胞，

like one of your skin cells,

例如皮膚的細胞，

you can transform that cell back to the naïve state.

可以將該細胞轉回稚年狀態。

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

這個實際上稱為「重新編寫」的過程

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

使我們能夠想像幹細胞的完美世界，

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

能採集患者自身細胞的樣本，

transform them back to the naïve state

將其轉回稚年狀態，

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

利用它們製造新細胞，

無論患者需要的是腦細胞、 心臟細胞，還是其他細胞。

whether it's brain cells or heart cells.

But over the last decade or so,

但是過去的十年左右，

figuring out how to change cell fate,

弄清楚如何改變細胞的命運

it's still a process of trial and error.

仍然是個反複試驗的過程。

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

即使我們發現若干成功的實驗案例，

they're still inefficient,

其效率仍然低下，

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

我們對其如何、為何起作用 缺乏基本的了解。

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

明白把幹細胞轉變為心臟細胞的方法

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

是無法把幹細胞轉變為腦細胞的。

into a brain cell.

So we wanted to understand the biological program

因此我們想了解

在胚胎幹細胞中運行的生物程式。

running inside an embryonic stem cell,

and understanding the computation performed by a living system

了解生物系統執行的運算

starts with asking a devastatingly simple question:

首先要問個簡單的問題：

What is it that system actually has to do?

系統實際上必須做什麼？

Now, computer science actually has a set of strategies

如今計算機科學實際上有一套策略

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

來處理軟體和硬體的功能。

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

我們編寫軟體程式，

you want that software to run correctly.

希望該軟體能夠正確運行，

You want performance, functionality.

具備性能和功能，

You want to prevent bugs.

還要防錯。

They can cost you a lot.

錯誤會造成重大的損失。

So when a developer writes a program,

因此開發人員編寫程式時

they could write down a set of specifications.

會寫下一組規格，

These are what your program should do.

列出程式應該做些什麼。

Maybe it should compare the size of two numbers

也許該比較兩個數字的大小，

or order numbers by increasing size.

或依大小排列。

Technology exists that allows us automatically to check

現有的技術能夠自動檢查

whether our specifications are satisfied,

是否滿足了我們所列的規範，

whether that program does what it should do.

該程式是否執行了應做的工作。

And so our idea was that in the same way,

因此，我們的想法是以同樣的方式

experimental observations, things we measure in the lab,

將實驗室中量得的實驗觀察結果

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

對應規範生物程式應該做些什麼。

So we just needed to figure out a way

因此，我們只需想出

to encode this new type of specification.

這種新型規範的編碼方法即可。

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

假設你一直在實驗室裡 忙著測量基因，

發現如果基因 A 處於活動狀態，

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

then Gene B or Gene C seems to be active.

那麼基因 B 或基因 C 似乎處於活動狀態。

We can write that observation down as a mathematical expression

如果用邏輯語言，

則能將該觀察結果表達為數學式：

if we can use the language of logic:

If A, then B or C.

若 A，則 B 或 C。

Now, this is a very simple example, OK.

這是一個非常簡單的例子，

It's just to illustrate the point.

只為了說明這一點。

We can encode truly rich expressions

我們能編碼真正豐富的表達式，

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

實際用在多個不同的實驗中，

表達多個基因或蛋白質 隨時間進程展現的行為。

across multiple different experiments.

And so by translating our observations

因此，通過以這種方式

將我們的觀察結果 轉化為數學表達式，

into mathematical expression in this way,

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

就有可能測試這些觀察結果

是否能經由基因的相互作用而產生。

from a program of genetic interactions.

And we developed a tool to do just this.

我們開發一種工具來做。

We were able to use this tool to encode observations

我們能用此工具

將觀察結果編碼為數學表達式，

as mathematical expressions,

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

該工具將使我們能發現

可以解釋所有現象的基因程式。

that could explain them all.

And we then apply this approach

然後用這種方法來揭示

to uncover the genetic program running inside embryonic stem cells

在胚胎幹細胞內部運行的基因程式，

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

試圖理解如何誘導出稚年的狀態。

And this tool was actually built

該工具實際上是基於

on a solver that's deployed routinely around the world

部署於全球的通用常規 軟體驗證的求解器構建的。

for conventional software verification.

So we started with a set of nearly 50 different specifications

我們從對胚胎幹細胞的實驗觀察中

that we generated from experimental observations of embryonic stem cells.

生成的近 50 種不同規格開始。

And by encoding these observations in this tool,

通過在此工具中 對這些觀察結果進行編碼，

we were able to uncover the first molecular program

我們得以發現第一個

能解釋所有觀察結果的分子程式。

that could explain all of them.

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

這本身就是一種壯舉，對吧？

Being able to reconcile all of these different observations

能夠協調所有這些不同的觀察結果，

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

那是再怎麼大的信封背面 都算不出來的。

even if you have a really big envelope.

Because we've got this kind of understanding,

因為已經有了這理解，

we could go one step further.

我們能再前進一步。

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

我們能用該程式來預測

在尚未測試的條件下 該細胞可能會做什麼。

in conditions we hadn't yet tested.

We could probe the program in silico.

我們可以在計算機上探索該程式。

And so we did just that:

那正是我們做的：

we generated predictions that we tested in the lab,

生成在實驗室中測試過的預測，

and we found that this program was highly predictive.

發現該程式具有很高的預測性。

It told us how we could accelerate progress

它告訴我們如何加速進程

back to the naïve state quickly and efficiently.

才能快速有效地回到稚年狀態。

It told us which genes to target to do that,

它告訴應該操作哪些基因；

which genes might even hinder that process.

哪些基因可能阻礙過程。

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

程式甚至預測了基因開啟的順序。

So this approach really allowed us to uncover the dynamics

這方法確實使我們能夠揭示

細胞的動態進行過程。

of what the cells are doing.

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

我們開發的並不是

針對幹細胞生物學的特定方法，

Rather, it allows us to make sense of the computation

而是使我們能夠 在基因相互作用的背景下

being carried out by the cell

in the context of genetic interactions.

理解細胞正在執行的運算。

So really, it's just one building block.

因此，實際上這只是個構建模塊。

The field urgently needs to develop new approaches

這領域迫切需要開發新的方法，

to understand biological computation more broadly

更廣泛、不同層次地理解生物運算，

and at different levels,

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

從 DNA 到細胞之間的信息流。

Only this kind of transformative understanding

只有這種變革性的理解

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

才能使我們以可預測 和可靠的方式利用生物學。

But to program biology, we will also need to develop

但是為要編寫生物學，

我們還需要開發各種工具和語言，

the kinds of tools and languages

that allow both experimentalists and computational scientists

使實驗人員和計算科學家

to design biological function

都可以設計生物學功能，

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

並將這些設計編譯為 細胞的機器代碼和生物化學，

its biochemistry,

so that we could then build those structures.

就可以建造那些結構。

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

這類似於生物軟體編譯器，

我很自豪能成為

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

微軟致力於開發 生物軟體團隊的一員。

that's working to develop one.

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

雖然說這是個巨大的挑戰 有點輕描淡寫，

but if it's realized,

但是如果實現了，

it would be the final bridge between software and wetware.

那將是軟體和濕體之間的最終橋樑。

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

從更廣泛的意義上講，

只有轉變為真正的跨學科領域， 才有可能編寫生物程式。

if we can transform the field into being truly interdisciplinary.

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

這需要我們在物理科學 和生命科學之間架起橋樑，

and scientists from each of these disciplines

而其中每個學科的科學家

need to be able to work together with common languages

得要能以共通的語言一起工作

and to have shared scientific questions.

和共享科學問題。

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

從長遠來看，值得記住的是

當初我們首次編寫矽微晶片時，

and the technology that you and I work with every day

幾乎無法想像

許多現今你我天天用到的

could hardly have been imagined

at the time we first started programming on silicon microchips.

巨型軟體公司和技術。

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

如果現在開始考慮

由計算生物學實現的技術的潛力，

enabled by computational biology,

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

我們將看到為實現這一目標 所需要採取的一些步驟。

to make that a reality.

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

有個警醒的想法

認為這種技術可能會被濫用。

could be open to misuse.

If we're willing to talk about the potential

如果要談編寫免疫細胞的潛力，

for programming immune cells,

we should also be thinking about the potential of bacteria

我們還應該考慮

設計細菌逃避免疫的潛力。

engineered to evade them.

There might be people willing to do that.

可能有人願意這樣做。

Now, one reassuring thought in this

一個令人放心的想法是——

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

科學家不那麼容易放心——

is that biology is a fragile thing to work with.

生物研究很脆弱，不好弄。

So programming biology is not going to be something

生物程式不是能躲在後院 工具間裡編寫得出的。

you'll be doing in your garden shed.

But because we're at the outset of this,

由於才剛起步，

we can move forward with our eyes wide open.

我們能睜大眼睛向前邁進。

We can ask the difficult questions up front,

我們能預先提出困難的問題，

we can put in place the necessary safeguards

能採取必要的保障措施。

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

得要考慮道德規範，

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

得要考慮限制實現生物功能的範圍。

of biological function.

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

因此其中一部分

必須將生物倫理學 研究作為優先事項，

It can't be relegated to second place

不能在科學創新的激情中 將其降為第二。

in the excitement of scientific innovation.

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

但是最終的獎賞，

也是此旅程的最終目的地，

would be breakthrough applications and breakthrough industries

將是應用和產業的大突破，

in areas from agriculture and medicine to energy and materials

在農業、醫藥、能源、材料，

and even computing itself.

乃至計算本身領域的大突破。

Imagine, one day we could be powering the planet sustainably

想像有一天我們能夠在地球上

利用植物終極永續的綠色能源，

on the ultimate green energy

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

如果能夠模仿植物 在幾千年前已經會了的方法——

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

如何有效地利用太陽能，

目前的太陽能電池無法有效辦到——

by our current solar cells.

If we understood that program of quantum interactions

如果理解植物高效吸收陽光的 量子相互作用程式，

that allow plants to absorb sunlight so efficiently,

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

我們也許可以將其轉化為 構建合成的 DNA 電路，

that offer the material for better solar cells.

從而為更好的太陽能電池提供材料。

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

現有團隊和科學家致力於此一基礎，

倘若得到正確的關注和投資，

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

或許能在十或十五年內實現。

it could be realized in 10 or 15 years.

So we are at the beginning of a technological revolution.

因此我們正處於技術革命的起點。

Understanding this ancient type of biological computation

了解這古老的生物運算類型 是關鍵的第一步。

is the critical first step.

And if we can realize this,

若能意識到這一點，

we would enter in the era of an operating system

我們即將進入運行 實時生物軟體作業系統的時代。

that runs living software.

Thank you very much.

非常感謝。

(Applause)

（掌聲）