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← The age of genetic wonder

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Zeige Revision 9 erzeugt am 02/15/2019 von Oliver Friedman.

  1. So let me with start with Roy Amara.
  2. Roy's argument is that most new
    technologies tend to be overestimated
  3. in their impact to begin with,
  4. and then they get underestimated
    in the long term
  5. because we get used to them.
  6. These really are days
    of miracle and wonder.

  7. You remember that wonderful
    song by Paul Simon?
  8. There were two lines in it.
  9. So what was it that was considered
    miraculous back then?
  10. Slowing down things -- slow motion --
  11. and the long-distance call.
  12. Because, of course, you used
    to get interrupted by operators
  13. who'd tell you, "Long distance calling.
    Do you want to hang up?"
  14. And now we think nothing of calling
    all over the world.
  15. Well, something similar may be happening
  16. with reading and programming life.
  17. But before I unpack that,

  18. let's just talk about telescopes.
  19. Telescopes were overestimated
    originally in their impact.
  20. This is one of Galileo's early models.
  21. People thought it was just
    going to ruin all religion.
  22. (Laughter)

  23. So we're not paying that much
    attention to telescopes.

  24. But, of course, telescopes launched
    10 years ago, as you just heard,
  25. could take this Volkswagen,
    fly it to the moon,
  26. and you could see the lights
    on that Volkswagen light up on the moon.
  27. And that's the kind of resolution power
    that allowed you to see
  28. little specks of dust
    floating around distant suns.
  29. Imagine for a second that this
    was a sun a billion light years away,
  30. and you had a little speck of dust
    that came in front of it.
  31. That's what detecting
    an exoplanet is like.
  32. And the cool thing is, the telescopes
    that are now being launched
  33. would allow you to see
    a single candle lit on the moon.
  34. And if you separated it by one plate,
  35. you could see two candles
    separately at that distance.
  36. And that's the kind
    of resolution that you need

  37. to begin to image
    that little speck of dust
  38. as it comes around the sun
  39. and see if it has a blue-green signature.
  40. And if it does have
    a blue-green signature,
  41. it means that life
    is common in the universe.
  42. The first time you ever see a blue-green
    signature on a distant planet,
  43. it means there's photosynthesis there,
  44. there's water there,
  45. and the chances that you saw
    the only other planet with photosynthesis
  46. are about zero.
  47. And that's a calendar-changing event.
  48. There's a before and after
    we were alone in the universe:
  49. forget about the discovery
    of whatever continent.
  50. So as you're thinking about this,
  51. we're now beginning
    to be able to image most of the universe.
  52. And that is a time of miracle and wonder.
  53. And we kind of take that for granted.
  54. Something similar is happening in life.

  55. So we're hearing about life
    in these little bits and pieces.
  56. We hear about CRISPR,
    and we hear about this technology,
  57. and we hear about this technology.
  58. But the bottom line on life
    is that life turns out to be code.
  59. And life as code is a really
    important concept because it means,
  60. just in the same way
    as you can write a sentence
  61. in English or in French or Chinese,
  62. just in the same way
    as you can copy a sentence,
  63. just in the same way
    as you can edit a sentence,
  64. just in the same way
    as you can print a sentence,
  65. you're beginning to be able
    to do that with life.
  66. It means that we're beginning
    to learn how to read this language.
  67. And this, of course, is the language
    that is used by this orange.
  68. So how does this orange execute code?

  69. It doesn't do it in ones and zeroes
    like a computer does.
  70. It sits on a tree, and one day it does:
  71. plop!
  72. And that means: execute.
  73. AATCAAG: make me a little root.
  74. TCGACC: make me a little stem.
  75. GAC: make me some leaves.
    AGC: make me some flowers.
  76. And then GCAA: make me some more oranges.
  77. If I edit a sentence in English
    on a word processor,

  78. then what happens is you can go
    from this word to that word.
  79. If I edit something in this orange
  80. and put in GCAAC, using CRISPR
    or something else that you've heard of,
  81. then this orange becomes a lemon,
  82. or it becomes a grapefruit,
  83. or it becomes a tangerine.
  84. And if I edit one in a thousand letters,
  85. you become the person
    sitting next to you today.
  86. Be more careful where you sit.
  87. (Laughter)

  88. What's happening on this stuff
    is it was really expensive to begin with.

  89. It was like long-distance calls.
  90. But the cost of this is dropping
    50 percent faster than Moore's law.
  91. The first $200 full genome
    was announced yesterday by Veritas.
  92. And so as you're looking at these systems,
  93. it doesn't matter, it doesn't matter,
    it doesn't matter, and then it does.
  94. So let me just give you
    the map view of this stuff.

  95. This is a big discovery.
  96. There's 23 chromosomes.
  97. Cool.
  98. Let's now start using a telescope version,
    but instead of using a telescope,
  99. let's use a microscope to zoom in
  100. on the inferior of those chromosomes,
  101. which is the Y chromosome.
  102. It's a third the size of the X.
    It's recessive and mutant.
  103. But hey,
  104. just a male.
  105. And as you're looking at this stuff,
  106. here's kind of a country view
  107. at a 400 base pair resolution level,
  108. and then you zoom in to 550,
    and then you zoom in to 850,
  109. and you can begin to identify
    more and more genes as you zoom in.
  110. Then you zoom in to the state level,
  111. and you can begin to tell
    who's got leukemia,
  112. how did they get leukemia,
    what kind of leukemia do they have,
  113. what shifted from what place
    to what place.
  114. And then you zoom in
    to the Google street view level.
  115. So this is what happens
    if you have colorectal cancer
  116. for a very specific patient
    on the letter-by-letter resolution.
  117. So what we're doing in this stuff
    is we're gathering information

  118. and just generating
    enormous amounts of information.
  119. This is one of the largest
    databases on the planet
  120. and it's growing faster
    than we can build computers to store it.
  121. You can create some incredible
    maps with this stuff.
  122. You want to understand the plague
    and why one plague is bubonic
  123. and the other one
    is a different kind of plague
  124. and the other one
    is a different kind of plague?
  125. Well, here's a map of the plague.
  126. Some are absolutely deadly to humans,
  127. some are not.
  128. And note, by the way,
    as you go to the bottom of this,
  129. how does it compare to tuberculosis?
  130. So this is the difference between
    tuberculosis and various kinds of plagues,
  131. and you can play detective
    with this stuff,
  132. because you can take
    a very specific kind of cholera
  133. that affected Haiti,
  134. and you can look at
    which country it came from,
  135. which region it came from,
  136. and probably which soldier took that
    from that African country to Haiti.
  137. Zoom out.

  138. It's not just zooming in.
  139. This is one of the coolest maps
    ever done by human beings.
  140. What they've done is taken
    all the genetic information they have
  141. about all the species,
  142. and they've put a tree of life
    on a single page
  143. that you can zoom in and out of.
  144. So this is what came first,
    how did it diversify, how did it branch,
  145. how large is that genome,
  146. on a single page.
  147. It's kind of the universe
    of life on Earth,
  148. and it's being constantly
    updated and completed.
  149. And so as you're looking at this stuff,

  150. the really important change is
    the old biology used to be reactive.
  151. You used to have a lot of biologists
    that had microscopes,
  152. and they had magnifying glasses
    and they were out observing animals.
  153. The new biology is proactive.
  154. You don't just observe stuff,
    you make stuff.
  155. And that's a really big change
  156. because it allows us
    to do things like this.
  157. And I know you're really
    excited by this picture.
  158. (Laughter)

  159. It only took us four years
    and 40 million dollars

  160. to be able to take this picture.
  161. (Laughter)

  162. And what we did

  163. is we took the full gene code
    out of a cell --
  164. not a gene, not two genes,
    the full gene code out of a cell --
  165. built a completely new gene code,
  166. inserted it into the cell,
  167. figured out a way to have the cell
    execute that code
  168. and built a completely new species.
  169. So this is the world's first
    synthetic life form.
  170. And so what do you do with this stuff?

  171. Well, this stuff is going
    to change the world.
  172. Let me give you three short-term trends
  173. in terms of how it's going
    to change the world.
  174. The first is we're going to see
    a new industrial revolution.

  175. And I actually mean that literally.
  176. So in the same way as Switzerland
    and Germany and Britain
  177. changed the world with machines
    like the one you see in this lobby,
  178. created power --
  179. in the same way CERN
    is changing the world,
  180. using new instruments
    and our concept of the universe --
  181. programmable life forms
    are also going to change the world
  182. because once you can program cells
  183. in the same way as you
    program your computer chip,
  184. then you can make almost anything.
  185. So your computer chip
    can produce photographs,

  186. can produce music, can produce film,
  187. can produce love letters,
    can produce spreadsheets.
  188. It's just ones and zeroes
    flying through there.
  189. If you can flow ATCGs through cells,
  190. then this software makes its own hardware,
  191. which means it scales very quickly.
  192. No matter what happens,
  193. if you leave your cell phone
    by your bedside,
  194. you will not have a billion
    cell phones in the morning.
  195. But if you do that with living organisms,
  196. you can make this stuff
    at a very large scale.
  197. One of the things you can do
    is you can start producing
  198. close to carbon-neutral fuels
  199. on a commercial scale by 2025,
  200. which we're doing with Exxon.
  201. But you can also substitute
    for agricultural lands.
  202. Instead of having 100 hectares
    to make oils or to make proteins,
  203. you can make it in these vats
  204. at 10 or 100 times
    the productivity per hectare.
  205. Or you can store information,
    or you can make all the world's vaccines
  206. in those three vats.
  207. Or you can store most of the information
    that's held at CERN in those three vats.
  208. DNA is a really powerful
    information storage device.
  209. Second turn:

  210. you're beginning to see the rise
    of theoretical biology.
  211. So, medical school departments are one
    of the most conservative places on earth.
  212. The way they teach anatomy is similar
    to the way they taught anatomy
  213. 100 years ago.
  214. "Welcome, student. Here's your cadaver."
  215. One of the things medical schools are
    not good at is creating new departments,
  216. which is why this is so unusual.
  217. Isaac Kohane has now created a department
    based on informatics, data, knowledge
  218. at Harvard Medical School.
  219. And in a sense,
    what's beginning to happen is
  220. biology is beginning to get enough data
  221. that it can begin to follow
    the steps of physics,
  222. which used to be observational physics
  223. and experimental physicists,
  224. and then started creating
    theoretical biology.
  225. Well, that's what you're beginning to see
  226. because you have so many medical records,
  227. because you have
    so much data about people:
  228. you've got their genomes,
    you've got their viromes,
  229. you've got their microbiomes.
  230. And as this information stacks,
  231. you can begin to make predictions.
  232. The third thing that's happening
    is this is coming to the consumer.

  233. So you, too, can get your genes sequenced.
  234. And this is beginning to create
    companies like 23andMe,
  235. and companies like 23andMe
    are going to be giving you
  236. more and more and more data,
  237. not just about your relatives,
  238. but about you and your body,
  239. and it's going to compare stuff,
  240. and it's going
    to compare stuff across time,
  241. and these are going to become
    very large databases.
  242. But it's also beginning to affect
    a series of other businesses

  243. in unexpected ways.
  244. Normally, when you advertise something,
    you really don't want the consumer
  245. to take your advertisement
    into the bathroom to pee on.
  246. Unless, of course, if you're IKEA.
  247. Because when you rip this
    out of a magazine and you pee on it,
  248. it'll turn blue if you're pregnant.
  249. (Laughter)

  250. And they'll give you
    a discount on your crib.

  251. (Laughter)

  252. Right? So when I say consumer empowerment,

  253. and this is spreading beyond biotech,
  254. I actually really mean that.
  255. We're now beginning to produce,
    at Synthetic Genomics,

  256. desktop printers
  257. that allow you to design a cell,
  258. print a cell,
  259. execute the program on the cell.
  260. We can now print vaccines
  261. real time as an airplane takes off
  262. before it lands.
  263. We're shipping 78
    of these machines this year.
  264. This is not theoretical biology.
    This is printing biology.
  265. Let me talk about two long-term trends

  266. that are coming at you
    over a longer time period.
  267. The first one is, we're starting
    to redesign species.
  268. And you've heard about that, right?
  269. We're redesigning trees.
    We're redesigning flowers.
  270. We're redesigning yogurt,
  271. cheese, whatever else you want.
  272. And that, of course,
    brings up the interesting question:
  273. How and when should we redesign humans?
  274. And a lot of us think,
    "Oh no, we never want to redesign humans."
  275. Unless, of course, if your child
    has a Huntington's gene
  276. and is condemned to death.
  277. Or, unless if you're passing on
    a cystic fibrosis gene,
  278. in which case, you don't just want
    to redesign yourself,
  279. you want to redesign your children
    and their children.
  280. And these are complicated debates
    and they're going to happen in real time.
  281. I'll give you one current example.

  282. One of the debates going on
    at the National Academies today
  283. is you have the power to put
    a gene drive into mosquitoes
  284. so that you will kill
    all the malaria-carrying mosquitoes.
  285. Now, some people say,
  286. "That's going to affect the environment
    in an extreme way, don't do it."
  287. Other people say,
  288. "This is one of the things
    that's killing millions of people yearly.
  289. Who are you to tell me
    that I can't save the kids in my country?"
  290. And why is this debate so complicated?
  291. Because as soon as you
    let this loose in Brazil
  292. or in Southern Florida --
  293. mosquitoes don't respect walls.
  294. You're making a decision for the world
  295. when you put a gene drive into the air.
  296. This wonderful man won a Nobel Prize,

  297. and after winning the Nobel Prize
  298. he's been worrying about
  299. how did life get started on this planet
  300. and how likely is it
    that it's in other places?
  301. So what he's been doing is going around
    to this graduate students
  302. and saying to his graduate students,
  303. "Build me life but don't use
    any modern chemicals or instruments.
  304. Build me stuff that was here
    three billion years ago.
  305. You can't use lasers.
    You can't use this. You can't use that."
  306. He gave me a vial of what he's built
    about three weeks ago.
  307. What has he built?
  308. He's built basically what looked like
    soap bubbles that are made out of lipids.
  309. He's built a precursor of RNA.
  310. He's had the precursor of the RNA
    be absorbed by the cell
  311. and then he's had the cells divide.
  312. We may not be that far --
  313. call it a decade, maybe two decades --
  314. from generating life from scratch
  315. out of proto-communities.
  316. Second long-term trend:

  317. we've been living and are living
    through the digital age --
  318. we're starting to live through
    the age of the genome
  319. and biology and CRISPR
    and synthetic biology --
  320. and all of that is going to merge
    into the age of the brain.
  321. So we're getting to the point where
    we can rebuild most of our body parts,
  322. in the same way as if you break a bone
    or burn your skin, it regrows.
  323. We're beginning to learn
    how to regrow our tracheas
  324. or how to regrow our bladders.
  325. Both of those have been
    implanted in humans.
  326. Tony Atala is working on
    32 different organs.
  327. But the core is going to be this,
  328. because this is you
    and the rest is just packaging.
  329. Nobody's going to live beyond
    120, 130, 140 years
  330. unless if we fix this.
  331. And that's the most interesting challenge.
  332. That's the next frontier, along with:
  333. "How common is life in the universe?"
  334. "Where did we come from?"
  335. and questions like that.
  336. Let me end this with
    an apocryphal quote from Einstein.

  337. [You can live as if
    everything is a miracle,

  338. or you can live as if
    nothing is a miracle.]
  339. It's your choice.

  340. You can focus on the bad,
    you can focus on the scary,
  341. and certainly there's
    a lot of scary out there.
  342. But use 10 percent of your brain
    to focus on that, or maybe 20 percent,
  343. or maybe 30 percent.
  344. But just remember,
  345. we really are living in an age
    of miracle and wonder.
  346. We're lucky to be alive today.
    We're lucky to see this stuff.
  347. We're lucky to be able to interact
    with folks like the folks
  348. who are building
    all the stuff in this room.
  349. So thank you to all of you,
    for all you do.

  350. (Applause)