演讲MP3+双语文稿:下一次软件革命是什么？
听力课堂TED音频栏目主要包括TED演讲的音频MP3及中英双语文稿，供各位英语爱好者学习使用。本文主要内容为演讲MP3+双语文稿:下一次软件革命是什么？，希望你会喜欢！
【演讲人及介绍】Sara-Jane Dunn
计算生物学家萨拉·简·邓恩（Sara-Jane Dunn），致力于生物学与计算之间的联系，利用数学和计算分析来了解生命系统如何处理信息。
【演讲主题】下一次软件革命：生物细胞编程
【演讲文稿-中英文】
翻译者 Jiasi Hao 校对 psjmz mz
00:12
The second half of the last century wascompletely defined by a technological revolution: the software revolution. Theability to program electrons on a material called silicon made possibletechnologies, companies and industries that were at one point unimaginable tomany of us, but which have now fundamentally changed the way the world works.The first half of this century, though, is going to be transformed by a newsoftware revolution: the living software revolution. And this will be poweredby the ability to program biochemistry on a material called biology. And doingso will enable us to harness the properties of biology to generate new kinds oftherapies, to repair damaged tissue, to reprogram faulty cells or even buildprogrammable operating systems out of biochemistry. If we can realize this --and we do need to realize it -- its impact will be so enormous that it willmake the first software revolution pale in comparison.
上世纪后半叶，全然是一个被科学革命所定义的时代：软件革命。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科技、公司和行业变为可能。这如今已彻底改变了世界运作的方式。不过，本世纪上半叶将要被一个 崭新的软件革命所转化： 生物软件革命。在一种名为生物的材料上 对生物化学进行编程的能力 将会支持这一革命。如此一来，我们将能够利用生物特征 去开发新型疗法，去修复受损组织，去重编缺陷细胞，甚至利用生物化学构建一个可编程的操作系统。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，以至于第一个软件革命，相比之下，会变得微不足道。
01:20
And that's because living software wouldtransform the entirety of medicine, agriculture and energy, and these aresectors that dwarf those dominated by IT. Imagine programmable plants that fixnitrogen more effectively or resist emerging fungal pathogens, or evenprogramming crops to be perennial rather than annual so you could double yourcrop yields each year. That would transform agriculture and how we'll keep ourgrowing and global population fed. Or imagine programmable immunity, designingand harnessing molecular devices that guide your immune system to detect,eradicate or even prevent disease. This would transform medicine and how we'llkeep our growing and aging population healthy.
这是因为生物软件可以变革整个医疗，农业和能源领域，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，或可以抵御新型真菌病原体，甚至能够将农作物编程为多年生而非一年生，使你的年产量可以翻倍。这会改变农业，同时改变全球不断增长的粮食需求的方法。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去检测、根除，甚至预防疾病。这将改变医疗，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。
02:07
We already have many of the tools that willmake living software a reality. We can precisely edit genes with CRISPR. We canrewrite the genetic code one base at a time. We can even build functioningsynthetic circuits out of DNA. But figuring out how and when to wield thesetools is still a process of trial and error. It needs deep expertise, years ofspecialization. And experimental protocols are difficult to discover and alltoo often, difficult to reproduce. And, you know, we have a tendency in biologyto focus a lot on the parts, but we all know that something like flyingwouldn't be understood by only studying feathers. So programming biology is notyet as simple as programming your computer. And then to make matters worse,living systems largely bear no resemblance to the engineered systems that youand I program every day. In contrast to engineered systems, living systemsself-generate, they self-organize, they operate at molecular scales. And thesemolecular-level interactions lead generally to robust macro-scale output. Theycan even self-repair.
我们已经拥有很多能让生物软件成为现实的工具。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。我们甚至能利用 DNA 开发一个合成电路。但是摸索出如何且何时使用这些工具依旧是一个试错的过程。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，往往更是难以复制。在生物领域，我们倾向仅专注于局部，但我们都知道有些东西，例如飞行，单就羽毛进行研究，是无法理解其原理的。所以生物编程还未能像电脑编程那样简单。更糟糕的是，生物系统和你我每天编写的工程系统几乎毫无相似之处。相比工程系统，生物系统能自我生产、自我组织，并以分子规模运作。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，它甚至可以自我修复。
03:16
Consider, for example, the humble householdplant, like that one sat on your mantelpiece at home that you keep forgettingto water. Every day, despite your neglect, that plant has to wake up and figureout how to allocate its resources. Will it grow, photosynthesize, produceseeds, or flower? And that's a decision that has to be made at the level of thewhole organism. But a plant doesn't have a brain to figure all of that out. Ithas to make do with the cells on its leaves. They have to respond to theenvironment and make the decisions that affect the whole plant. So somehowthere must be a program running inside these cells, a program that responds toinput signals and cues and shapes what that cell will do. And then thoseprograms must operate in a distributed way across individual cells, so thatthey can coordinate and that plant can grow and flourish.
试想家中一盆不起眼的植物，比如你家壁炉台上的那盆你老是忘记浇水的植物。尽管你会忘记，那盆植物每天都需要醒来并思考如何分配它所有的资源。它是生长、进行光合作用、产生种子，还是开花？这是这盆植物所需要做出的决定。但一盆植物没有大脑来弄清这件事。这需要其叶片上细胞的帮助。它们需要针对环境做出反应，并且做出影响整盆植物的决定。所以在那些叶片细胞中必定要有一个运行的程序，一个能响应输入信号与提示，以及调整细胞行为的程序。之后，那些程序必须以分布式运行，覆盖每一个细胞单元，从而进行协作让植物茁壮成长。
04:07
If we could understand these biologicalprograms, if we could understand biological computation, it would transform ourability to understand how and why cells do what they do. Because, if weunderstood these programs, we could debug them when things go wrong. Or wecould learn from them how to design the kind of synthetic circuits that trulyexploit the computational power of biochemistry.
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，这将会转变我们对细胞的行为方式和行为原因的理解能力。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。或我们可以向它们学习如何设计这样 能充分利用生物化学 计算能力的合成电路。
04:34
My passion about this idea led me to acareer in research at the interface of maths, computer science and biology. Andin my work, I focus on the concept of biology as computation. And that meansasking what do cells compute, and how can we uncover these biological programs?And I started to ask these questions together with some brilliant collaboratorsat Microsoft Research and the University of Cambridge, where together we wantedto understand the biological program running inside a unique type of cell: anembryonic stem cell. These cells are unique because they're totally naïve. Theycan become anything they want: a brain cell, a heart cell, a bone cell, a lungcell, any adult cell type. This naïvety, it sets them apart, but it alsoignited the imagination of the scientific community, who realized, if we couldtap into that potential, we would have a powerful tool for medicine. If wecould figure out how these cells make the decision to become one cell type oranother, we might be able to harness them to generate cells that we need torepair diseased or damaged tissue. But realizing that vision is not without itschallenges, not least because these particular cells, they emerge just six daysafter conception. And then within a day or so, they're gone. They have set offdown the different paths that form all the structures and organs of your adultbody.
我对这个想法的热情，让我进入了数学、计算机科学和生物学的交叉领域。工作中，我专注于一个概念：生物学计算。这代表着不断询问细胞在计算什么，以及我们如何能解开这些生物程序的奥秘？我开始和微软研究院与剑桥大学的一些出色的合作人士一起询问这些问题，我们想要了解在一种独特细胞中运行的生物程序：胚胎干细胞（ ES 细胞）。这些细胞很独特，因为它们非常稚嫩（即未高度分化）。它们能够分化为它们想要变成的东西：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。这一稚嫩状态让这些细胞变得与众不同，但也激发了科学界的想象力。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。如果我们能搞清这些细胞是如何决定自己要发育为何种细胞的，我们或许能够利用 ES 细胞的这一能力，生成我们需要的细胞，来修复携带疾病的或受损的组织。但这一愿景的实现存在着挑战，不仅是因为这些特定细胞在受孕的 6 天后才出现，之后大约在 1 天内，就会消失。它们走上了不同的道路，共同形成成年人体的所有结构和器官。
05:59
But it turns out that cell fates are a lotmore plastic than we might have imagined. About 13 years ago, some scientistsshowed something truly revolutionary. By inserting just a handful of genes intoan adult cell, like one of your skin cells, you can transform that cell back tothe naïve state. And it's a process that's actually known as"reprogramming," and it allows us to imagine a kind of stem cellutopia, the ability to take a sample of a patient's own cells, transform themback to the naïve state and use those cells to make whatever that patient mightneed, whether it's brain cells or heart cells.
但事实证明，细胞的命运比我们所想象的更具有可塑性。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：通过把少量基因导入成熟细胞，例如你的一个皮肤细胞，你可以把这个成熟细胞转化回未分化状态。这一过程被称为“重编程”。这让我们联想到“干细胞乌托邦”，这种能力可以采集患者自身的细胞样本，将其转化回未分化的原始形态，并使用那些细胞制造患者可能需要的细胞，不论是脑细胞，还是心脏细胞。
06:38
But over the last decade or so, figuringout how to change cell fate, it's still a process of trial and error. Even incases where we've uncovered successful experimental protocols, they're stillinefficient, and we lack a fundamental understanding of how and why they work.If you figured out how to change a stem cell into a heart cell, that hasn't gotany way of telling you how to change a stem cell into a brain cell. So we wantedto understand the biological program running inside an embryonic stem cell, andunderstanding the computation performed by a living system starts with asking adevastatingly simple question: What is it that system actually has to do?
但在过去的 10 年，搞清楚如何改变细胞命运仍然是一个试错的过程。即使是在那些我们已经发现了成功实验方法的情况下，它们仍旧低效，而且我们缺少关于它们如何以及为何运作的基本理解。如果你能摸清如何把一个干细胞诱导为一个心脏细胞，你依然不知道如何把一个干细胞诱导为一个脑细胞。所以我们想要了解在 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 始于提出一个极为简单的问题： 这个系统到底需要做什么？
07:21
Now, computer science actually has a set ofstrategies for dealing with what it is the software and hardware are meant todo. When you write a program, you code a piece of software, you want thatsoftware to run correctly. You want performance, functionality. You want toprevent bugs. They can cost you a lot. So when a developer writes a program,they could write down a set of specifications. These are what your programshould do. Maybe it should compare the size of two numbers or order numbers byincreasing size. Technology exists that allows us automatically to checkwhether our specifications are satisfied, whether that program does what itshould do. And so our idea was that in the same way, experimental observations,things we measure in the lab, they correspond to specifications of what thebiological program should do.
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。当你编写程序时，你用代码编写了一个软件，你希望这个软件能够正确运行，你希望它具备完善的功能与性能，能防止错误，做到这些的成本很高。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。这些是你的程序应该做的“工作”。或许它能比较两个数的大小，或将数字进行正序排序。这样的技术存在：允许我们自动检查我们的代码是否符合技术规范，程序是否在完成它的本职工作。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，符合生物编程本职工作中怎样的技术规范？
08:10
So we just needed to figure out a way toencode this new type of specification. So let's say you've been busy in the laband you've been measuring your genes and you've found that if Gene A is active,then Gene B or Gene C seems to be active. We can write that observation down asa mathematical expression if we can use the language of logic: If A, then B orC. Now, this is a very simple example, OK. It's just to illustrate the point.We can encode truly rich expressions that actually capture the behavior ofmultiple genes or proteins over time across multiple different experiments. Andso by translating our observations into mathematical expression in this way, itbecomes possible to test whether or not those observations can emerge from aprogram of genetic interactions.
所以我们只需要找到一个方法来编译这个新型的技术规范。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，那么基因 B 或 C 也会看似活跃。如果我们能用一种逻辑语言，就可以将这种观察编写为一种数学表达：如果 A ，那么 B 或 C 。这是一个非常简单的例子，只是为了解释清楚我的意思。我们可以编译很多丰富的表达，在多个不同的实验中，随着时间的推移，这些表达可以捕捉多种基因或蛋白质的行为。运用这种方法，把我们的观察值编译为一种数学表达，现在有可能测试这些观察结果是否可以从基因相互作用的程序中得到。
09:04
And we developed a tool to do just this. Wewere able to use this tool to encode observations as mathematical expressions,and then that tool would allow us to uncover the genetic program that couldexplain them all. And we then apply this approach to uncover the geneticprogram running inside embryonic stem cells to see if we could understand howto induce that naïve state. And this tool was actually built on a solver that'sdeployed routinely around the world for conventional software verification. Sowe started with a set of nearly 50 different specifications that we generatedfrom experimental observations of embryonic stem cells. And by encoding theseobservations in this tool, we were able to uncover the first molecular programthat could explain all of them.
我们开发了一个工具来实现这个目的。我们能用这个工具将观察值编译为 数学表达。该工具能让我们发现可以解释 所有原因的遗传程序。之后，我们运用这个方法 来揭示 ES 细胞中运行的遗传程序，来看看我们是否能理解 如何诱导未分化状态的细胞。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 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，这些是我们从对 ES 细胞的实验观察值中得出的。利用这个工具，通过编译这些观察值，我们能够揭开第一个能够解释所有分子的程序。
09:52
Now, that's kind of a feat in and ofitself, right? Being able to reconcile all of these different observations isnot the kind of thing you can do on the back of an envelope, even if you have areally big envelope. Because we've got this kind of understanding, we could goone step further. We could use this program to predict what this cell might doin conditions we hadn't yet tested. We could probe the program in silico.
这本身听着是一种壮举，是吧？将所有的观察值协调到一起，不是那种你可以在信封背面做的事情，即使你有一个很大的信封。因为我们有着这样的理解，我们能够再进一步。我们能够用这个程序在尚未测试的条件下，来预测这个细胞可能会做什么。我们能够在硅上探索该程序。
10:16
And so we did just that: we generatedpredictions that we tested in the lab, and we found that this program washighly predictive. It told us how we could accelerate progress back to thenaïve state quickly and efficiently. It told us which genes to target to dothat, which genes might even hinder that process. We even found the programpredicted the order in which genes would switch on. So this approach reallyallowed us to uncover the dynamics of what the cells are doing.
所以我们行动了起来：我们依据实验室检测值生成了预测，并发现该程序非常具有可预测性。它告诉我们如何能够加速细胞返回未分化状态的过程，使之快速且有效。它告诉我们可以针对哪些基因进行操作，又有哪些基因会阻碍这一过程。我们甚至发现了一个能够预测基因开启顺序的程序。这个方法让我们得以揭秘细胞行为的动态。
10:47
What we've developed, it's not a methodthat's specific to stem cell biology. Rather, it allows us to make sense of thecomputation being carried out by the cell in the context of geneticinteractions. So really, it's just one building block. The field urgently needsto develop new approaches to understand biological computation more broadly andat different levels, from DNA right through to the flow of information betweencells. Only this kind of transformative understanding will enable us to harnessbiology in ways that are predictable and reliable.
我们开发的不只是一种仅限于干细胞生物的方法。相反，这能帮助我们理解在遗传相互作用的环境下细胞内在的计算程序。所以这其实只是拼图中的一块。该领域急需开发新方法来更广泛地在不同层次上了解生物计算，从 DNA 到细胞间的信息流。只有这样的变革性理解才能够使我们以可预测和可靠的方式利用生物学。
11:21
But to program biology, we will also needto develop the kinds of tools and languages that allow both experimentalistsand computational scientists to design biological function and have thosedesigns compile down to the machine code of the cell, its biochemistry, so thatwe could then build those structures. Now, that's something akin to a livingsoftware compiler, and I'm proud to be part of a team at Microsoft that'sworking to develop one. Though to say it's a grand challenge is kind of anunderstatement, but if it's realized, it would be the final bridge betweensoftware and wetware.
但是对于编程生物学，我们也将需要开发允许实验人员和计算科学家使用的工具和语言来设计生物函数，并将这些设计编译成细胞的机器代码，也就是它的生物化学，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，我非常自豪能成为微软开发此类软件团队的一员。尽管，说这是一个很大的挑战有点轻描淡写，但如果能实现，这将会成为软件和湿件最后的桥梁。
11:57
More broadly, though, programming biologyis only going to be possible if we can transform the field into being trulyinterdisciplinary. It needs us to bridge the physical and the life sciences,and scientists from each of these disciplines need to be able to work togetherwith common languages and to have shared scientific questions.
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，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，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，并分享共同的科学问题。
12:16
In the long term, it's worth rememberingthat many of the giant software companies and the technology that you and Iwork with every day could hardly have been imagined at the time we firststarted programming on silicon microchips. And if we start now to think aboutthe potential for technology enabled by computational biology, we'll see someof the steps that we need to take along the way to make that a reality. Now,there is the sobering thought that this kind of technology could be open tomisuse. If we're willing to talk about the potential for programming immunecells, we should also be thinking about the potential of bacteria engineered toevade them. There might be people willing to do that. Now, one reassuringthought in this is that -- well, less so for the scientists -- is that biologyis a fragile thing to work with. So programming biology is not going to besomething you'll be doing in your garden shed. But because we're at the outsetof this, we can move forward with our eyes wide open. We can ask the difficultquestions up front, we can put in place the necessary safeguards and, as partof that, we'll have to think about our ethics. We'll have to think aboutputting bounds on the implementation of biological function. So as part ofthis, research in bioethics will have to be a priority. It can't be relegatedto second place in the excitement of scientific innovation.
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，几乎无法想象有一天会出现我们如今每天都需要打交道的那些大型软件公司和技术。如果我们现在开始思考由计算生物学支持的科技潜能，我们将会看到为实现这一目标一路上需要做出的努力。如今存在一种令人警醒的想法：这种科技可能会被滥用。如果我们愿意探讨编程免疫细胞的潜力，我们也应该考虑到改造后的细菌成功躲避那些免疫细胞的可能。可能有些人打算从事这方面的研究。关于这个话题也存在一个令人欣慰的想法——科学家大概不这么认为——生物太脆弱，在工作中难以把控。所以编程生物学不会进入你的生活。但因为我们才刚起步，所以我们可以大胆且谨慎的往前走。我们可以事先提出难题，我们可以采取必要的保护措施，同时，作为其中的一部分，还需要思考我们的道德标准，我们将需要思考那些生物函数实行的界限。所以其中的生物伦理学研究将被优先考虑。在令人激动的科学创新中，这个话题不能屈居第二。
13:35
But the ultimate prize, the ultimatedestination on this journey, would be breakthrough applications andbreakthrough industries in areas from agriculture and medicine to energy andmaterials and even computing itself. Imagine, one day we could be powering theplanet sustainably on the ultimate green energy if we could mimic somethingthat plants figured out millennia ago: how to harness the sun's energy with anefficiency that is unparalleled by our current solar cells. If we understoodthat program of quantum interactions that allow plants to absorb sunlight soefficiently, we might be able to translate that into building synthetic DNAcircuits that offer the material for better solar cells. There are teams andscientists working on the fundamentals of this right now, so perhaps if it gotthe right attention and the right investment, it could be realized in 10 or 15years.
但我们这场旅行的最终目的地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，甚至计算机技术本身。试想，有一天，我们能使用终极绿色能源为地球提供可持续的动力，因为我们已经能够模仿植物在千年前发现的东西：如何利用我们现有太阳能电池无法比拟的效率来利用太阳能。如果我们能理解让植物高效吸收太阳光的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，如果这个课题能获得足够的关注和正确的投资，在未来的 10 或 15 年内，或许就有可能实现。
14:27
So we are at the beginning of atechnological revolution. Understanding this ancient type of biologicalcomputation is the critical first step. And if we can realize this, we wouldenter in the era of an operating system that runs living software.
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是关键的第一步。如果我们能意识到这件事，就将进入一个拥有能够运行生物软件的操作系统的时代。
14:42
Thank you very much.
非常感谢。
14:43
(Applause)
（掌声）
