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← How we're using DNA tech to help farmers fight crop diseases

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Showing Revision 7 created 10/10/2019 by Brian Greene.

  1. I get out of bed for two reasons.
  2. One, small-scale family farmers
    need more food.
  3. It's crazy that in 2019
    farmers that feed us are hungry.
  4. And two, science needs to be
    more diverse and inclusive.
  5. If we're going to solve
    the toughest challenges on the planet,
  6. like food insecurity for the millions
    living in extreme poverty,
  7. it's going to take all of us.
  8. I want to use the latest technology

  9. with the most diverse
    and inclusive teams on the planet
  10. to help farmers have more food.
  11. I'm a computational biologist.
  12. I know -- what is that
    and how is it going to help end hunger?
  13. Basically, I like computers and biology
  14. and somehow,
    putting that together is a job.
  15. (Laughter)

  16. I don't have a story

  17. of wanting to be a biologist
    from a young age.
  18. The truth is, I played
    basketball in college.
  19. And part of my financial aid package
    was I needed a work-study job.
  20. So one random day,
  21. I wandered to the nearest building
    to my dorm room.
  22. And it just so happens
    it was the biology building.
  23. I went inside and looked at the job board.
  24. Yes, this is pre-the-internet.
  25. And I saw a three-by-five card
  26. advertising a job
    to work in the herbarium.
  27. I quickly took down the number,
  28. because it said "flexible hours,"
  29. and I needed that to work around
    my basketball schedule.
  30. I ran to the library
    to figure out what an herbarium was.
  31. (Laughter)

  32. And it turns out

  33. an herbarium is where they store
    dead, dried plants.
  34. I was lucky to land the job.
  35. So my first scientific job
  36. was gluing dead plants onto paper
    for hours on end.
  37. (Laughter)

  38. It's so glamorous.

  39. This is how I became
    a computational biologist.
  40. During that time,
  41. genomics and computing were coming of age.
  42. And I went on to do my masters
  43. combining biology and computers.
  44. During that time,

  45. I worked at Los Alamos National Lab
  46. in the theoretical biology
    and biophysics group.
  47. And it was there I had my first encounter
    with the supercomputer,
  48. and my mind was blown.
  49. With the power of supercomputing,
  50. which is basically thousands
    of connected PCs on steroids,
  51. we were able to uncover the complexities
    of influenza and hepatitis C.
  52. And it was during this time
    that I saw the power
  53. of using computers
    and biology combined, for humanity.
  54. And I wanted this to be my career path.
  55. So, since 1999,
  56. I've spent the majority
    of my scientific career
  57. in very high-tech labs,
  58. surrounded by really expensive equipment.
  59. So many ask me

  60. how and why do I work
    for farmers in Africa.
  61. Well, because of my computing skills,
  62. in 2013, a team of East African scientists
  63. asked me to join the team
    in the plight to save cassava.
  64. Cassava is a plant whose leaves and roots
    feed 800 million people globally.
  65. And 500 million in East Africa.
  66. So that's nearly a billion people
  67. relying on this plant
    for their daily calories.
  68. If a small-scale family farmer
    has enough cassava,
  69. she can feed her family
  70. and she can sell it at the market
    for important things like school fees,
  71. medical expenses and savings.
  72. But cassava is under attack in Africa.

  73. Whiteflies and viruses
    are devastating cassava.
  74. Whiteflies are tiny insects
  75. that feed on the leaves
    of over 600 plants.
  76. They are bad news.
  77. There are many species;
  78. they become pesticide resistant;
  79. and they transmit hundreds
    of plant viruses
  80. that cause cassava brown streak disease
  81. and cassava mosaic disease.
  82. This completely kills the plant.
  83. And if there's no cassava,
  84. there's no food or income
    for millions of people.
  85. It took me one trip to Tanzania

  86. to realize that these women
    need some help.
  87. These amazing, strong,
    small-scale family farmers,
  88. the majority women,
  89. are doing it rough.
  90. They don't have enough food
    to feed their families,
  91. and it's a real crisis.
  92. What happens is
  93. they go out and plant fields of cassava
    when the rains come.
  94. Nine months later,
  95. there's nothing, because of these
    pests and pathogens.
  96. And I thought to myself,
  97. how in the world can farmers be hungry?
  98. So I decided to spend
    some time on the ground

  99. with the farmers and the scientists
  100. to see if I had any skills
    that could be helpful.
  101. The situation on the ground is shocking.
  102. The whiteflies have destroyed the leaves
    that are eaten for protein,
  103. and the viruses have destroyed the roots
    that are eaten for starch.
  104. An entire growing season will pass,
  105. and the farmer will lose
    an entire year of income and food,
  106. and the family will suffer
    a long hunger season.
  107. This is completely preventable.
  108. If the farmer knew
  109. what variety of cassava
    to plant in her field,
  110. that was resistant
    to those viruses and pathogens,
  111. they would have more food.
  112. We have all the technology we need,

  113. but the knowledge and the resources
  114. are not equally distributed
    around the globe.
  115. So what I mean specifically is,
  116. the older genomic technologies
  117. that have been required
    to uncover the complexities
  118. in these pests and pathogens --
  119. these technologies were not made
    for sub-Saharan Africa.
  120. They cost upwards of a million dollars;
  121. they require constant power
  122. and specialized human capacity.
  123. These machines are few
    and far between on the continent,
  124. which is leaving many scientists
    battling on the front lines no choice
  125. but to send the samples overseas.
  126. And when you send the samples overseas,
  127. samples degrade, it costs a lot of money,
  128. and trying to get the data back
    over weak internet
  129. is nearly impossible.
  130. So sometimes it can take six months
    to get the results back to the farmer.
  131. And by then, it's too late.
  132. The crop is already gone,
  133. which results in further poverty
    and more hunger.
  134. We knew we could fix this.

  135. In 2017,
  136. we had heard of this handheld,
    portable DNA sequencer
  137. called an Oxford Nanopore MinION.
  138. This was being used
    in West Africa to fight Ebola.
  139. So we thought:
  140. Why can't we use this
    in East Africa to help farmers?
  141. So, what we did was we set out to do that.
  142. At the time, the technology was very new,
  143. and many doubted we could
    replicate this on the farm.
  144. When we set out to do this,
  145. one of our "collaborators" in the UK
  146. told us that we would never
    get that to work in East Africa,
  147. let alone on the farm.
  148. So we accepted the challenge.
  149. This person even went so far as to bet us
    two of the best bottles of champagne
  150. that we would never get that to work.
  151. Two words:
  152. pay up.
  153. (Laughter)

  154. (Applause)

  155. Pay up, because we did it.

  156. We took the entire high-tech molecular lab
  157. to the farmers of Tanzania,
    Kenya and Uganda,
  158. and we called it Tree Lab.
  159. So what did we do?
  160. Well, first of all,
    we gave ourselves a team name --
  161. it's called the Cassava Virus
    Action Project.
  162. We made a website,
  163. we gathered support from the genomics
    and computing communities,
  164. and away we went to the farmers.
  165. Everything that we need for our Tree Lab
  166. is being carried by the team here.
  167. All of the molecular and computational
    requirements needed
  168. to diagnose sick plants is there.
  169. And it's actually all
    on this stage here as well.
  170. We figured if we could get the data
    closer to the problem,

  171. and closer to the farmer,
  172. the quicker we could tell her
    what was wrong with her plant.
  173. And not only tell her what was wrong --
  174. give her the solution.
  175. And the solution is,
  176. burn the field and plant varieties
  177. that are resistant to the pests
    and pathogens she has in her field.
  178. So the first thing that we did
    was we had to do a DNA extraction.
  179. And we used this machine here.
  180. It's called a PDQeX,
  181. which stands for
    "Pretty Damn Quick Extraction."
  182. (Laughter)

  183. I know.

  184. My friend Joe is really cool.
  185. One of the biggest challenges
    in doing a DNA extraction
  186. is it usually requires
    very expensive equipment,
  187. and takes hours.
  188. But with this machine,
  189. we've been able to do it in 20 minutes,
  190. at a fraction of the cost.
  191. And this runs off of a motorcycle battery.
  192. From there, we take the DNA extraction
    and prepare it into a library,

  193. getting it ready to load on
  194. to this portable, handheld
    genomic sequencer,
  195. which is here,
  196. and then we plug this
    into a mini supercomputer,
  197. which is called a MinIT.
  198. And both of these things are plugged
    into a portable battery pack.
  199. So we were able to eliminate
  200. the requirements
    of main power and internet,
  201. which are two very limiting factors
    on a small-scale family farm.
  202. Analyzing the data quickly
    can also be a problem.
  203. But this is where me being
    a computational biologist came in handy.
  204. All that gluing of dead plants,
  205. and all that measuring,
  206. and all that computing
  207. finally came in handy
    in a real-world, real-time way.
  208. I was able to make customized databases
  209. and we were able to give the farmers
    results in three hours
  210. versus six months.
  211. (Applause)

  212. The farmers were overjoyed.

  213. So how do we know
    that we're having impact?
  214. Nine moths after our Tree Lab,
  215. Asha went from having
    zero tons per hectare
  216. to 40 tons per hectare.
  217. She had enough to feed her family
  218. and she was selling it at the market,
  219. and she's now building a house
    for her family.
  220. Yeah, so cool.
  221. (Applause)

  222. So how do we scale Tree Lab?

  223. The thing is,
  224. farmers are scaled already in Africa.
  225. These women work in farmer groups,
  226. so helping Asha actually helped
    3,000 people in her village,
  227. because she shared the results
    and also the solution.
  228. I remember every single
    farmer I've ever met.

  229. Their pain and their joy
  230. is engraved in my memories.
  231. Our science is for them.
  232. Tree Lab is our best attempt
    to help them become more food secure.
  233. I never dreamt
  234. that the best science
    I would ever do in my life
  235. would be on that blanket in East Africa,
  236. with the highest-tech genomic gadgets.
  237. But our team did dream
  238. that we could give farmers answers
    in three hours versus six months,
  239. and then we did it.
  240. Because that's the power
    of diversity and inclusion in science.
  241. Thank you.

  242. (Applause)

  243. (Cheers)