How to read the genome and build a human being
-
0:01 - 0:03For the next 16 minutes,
I'm going to take you on a journey -
0:03 - 0:06that is probably
the biggest dream of humanity: -
0:07 - 0:09to understand the code of life.
-
0:09 - 0:12So for me, everything started
many, many years ago -
0:12 - 0:15when I met the first 3D printer.
-
0:15 - 0:16The concept was fascinating.
-
0:16 - 0:18A 3D printer needs three elements:
-
0:18 - 0:22a bit of information, some
raw material, some energy, -
0:22 - 0:26and it can produce any object
that was not there before. -
0:27 - 0:29I was doing physics,
I was coming back home -
0:29 - 0:32and I realized that I actually
always knew a 3D printer. -
0:32 - 0:33And everyone does.
-
0:34 - 0:35It was my mom.
-
0:35 - 0:36(Laughter)
-
0:36 - 0:38My mom takes three elements:
-
0:38 - 0:42a bit of information, which is between
my father and my mom in this case, -
0:42 - 0:46raw elements and energy
in the same media, that is food, -
0:46 - 0:49and after several months, produces me.
-
0:49 - 0:51And I was not existent before.
-
0:51 - 0:54So apart from the shock of my mom
discovering that she was a 3D printer, -
0:54 - 0:59I immediately got mesmerized
by that piece, -
0:59 - 1:01the first one, the information.
-
1:01 - 1:03What amount of information does it take
-
1:03 - 1:05to build and assemble a human?
-
1:05 - 1:07Is it much? Is it little?
-
1:07 - 1:09How many thumb drives can you fill?
-
1:09 - 1:12Well, I was studying physics
at the beginning -
1:12 - 1:17and I took this approximation of a human
as a gigantic Lego piece. -
1:17 - 1:21So, imagine that the building
blocks are little atoms -
1:21 - 1:26and there is a hydrogen here,
a carbon here, a nitrogen here. -
1:26 - 1:27So in the first approximation,
-
1:27 - 1:32if I can list the number of atoms
that compose a human being, -
1:32 - 1:33I can build it.
-
1:33 - 1:35Now, you can run some numbers
-
1:35 - 1:38and that happens to be
quite an astonishing number. -
1:38 - 1:41So the number of atoms,
-
1:41 - 1:46the file that I will save in my thumb
drive to assemble a little baby, -
1:46 - 1:51will actually fill an entire Titanic
of thumb drives -- -
1:51 - 1:53multiplied 2,000 times.
-
1:54 - 1:57This is the miracle of life.
-
1:57 - 2:00Every time you see from now on
a pregnant lady, -
2:00 - 2:03she's assembling the biggest
amount of information -
2:03 - 2:04that you will ever encounter.
-
2:04 - 2:07Forget big data, forget
anything you heard of. -
2:07 - 2:10This is the biggest amount
of information that exists. -
2:10 - 2:14(Applause)
-
2:14 - 2:19But nature, fortunately, is much smarter
than a young physicist, -
2:19 - 2:22and in four billion years, managed
to pack this information -
2:22 - 2:25in a small crystal we call DNA.
-
2:26 - 2:30We met it for the first time in 1950
when Rosalind Franklin, -
2:30 - 2:31an amazing scientist, a woman,
-
2:32 - 2:33took a picture of it.
-
2:33 - 2:38But it took us more than 40 years
to finally poke inside a human cell, -
2:38 - 2:40take out this crystal,
-
2:40 - 2:43unroll it, and read it for the first time.
-
2:44 - 2:47The code comes out to be
a fairly simple alphabet, -
2:47 - 2:51four letters: A, T, C and G.
-
2:51 - 2:54And to build a human,
you need three billion of them. -
2:55 - 2:56Three billion.
-
2:56 - 2:58How many are three billion?
-
2:58 - 3:01It doesn't really make
any sense as a number, right? -
3:01 - 3:05So I was thinking how
I could explain myself better -
3:05 - 3:08about how big and enormous this code is.
-
3:08 - 3:11But there is -- I mean,
I'm going to have some help, -
3:11 - 3:14and the best person to help me
introduce the code -
3:14 - 3:18is actually the first man
to sequence it, Dr. Craig Venter. -
3:18 - 3:21So welcome onstage, Dr. Craig Venter.
-
3:21 - 3:28(Applause)
-
3:28 - 3:30Not the man in the flesh,
-
3:31 - 3:34but for the first time in history,
-
3:34 - 3:37this is the genome of a specific human,
-
3:37 - 3:41printed page-by-page, letter-by-letter:
-
3:41 - 3:45262,000 pages of information,
-
3:45 - 3:49450 kilograms, shipped
from the United States to Canada -
3:49 - 3:54thanks to Bruno Bowden,
Lulu.com, a start-up, did everything. -
3:54 - 3:56It was an amazing feat.
-
3:56 - 4:00But this is the visual perception
of what is the code of life. -
4:00 - 4:03And now, for the first time,
I can do something fun. -
4:03 - 4:05I can actually poke inside it and read.
-
4:05 - 4:10So let me take an interesting
book ... like this one. -
4:13 - 4:16I have an annotation;
it's a fairly big book. -
4:16 - 4:19So just to let you see
what is the code of life. -
4:21 - 4:24Thousands and thousands and thousands
-
4:24 - 4:27and millions of letters.
-
4:27 - 4:29And they apparently make sense.
-
4:29 - 4:31Let's get to a specific part.
-
4:32 - 4:33Let me read it to you:
-
4:33 - 4:34(Laughter)
-
4:34 - 4:38"AAG, AAT, ATA."
-
4:39 - 4:41To you it sounds like mute letters,
-
4:41 - 4:45but this sequence gives
the color of the eyes to Craig. -
4:46 - 4:48I'll show you another part of the book.
-
4:48 - 4:50This is actually a little
more complicated. -
4:51 - 4:54Chromosome 14, book 132:
-
4:54 - 4:56(Laughter)
-
4:56 - 4:57As you might expect.
-
4:57 - 5:01(Laughter)
-
5:03 - 5:07"ATT, CTT, GATT."
-
5:08 - 5:10This human is lucky,
-
5:10 - 5:15because if you miss just
two letters in this position -- -
5:15 - 5:16two letters of our three billion --
-
5:16 - 5:19he will be condemned
to a terrible disease: -
5:19 - 5:20cystic fibrosis.
-
5:20 - 5:23We have no cure for it,
we don't know how to solve it, -
5:23 - 5:27and it's just two letters
of difference from what we are. -
5:28 - 5:30A wonderful book, a mighty book,
-
5:31 - 5:33a mighty book that helped me understand
-
5:33 - 5:36and show you something quite remarkable.
-
5:36 - 5:41Every one of you -- what makes
me, me and you, you -- -
5:41 - 5:44is just about five million of these,
-
5:44 - 5:45half a book.
-
5:46 - 5:48For the rest,
-
5:48 - 5:50we are all absolutely identical.
-
5:51 - 5:55Five hundred pages
is the miracle of life that you are. -
5:55 - 5:58The rest, we all share it.
-
5:58 - 6:01So think about that again
when we think that we are different. -
6:01 - 6:03This is the amount that we share.
-
6:03 - 6:07So now that I have your attention,
-
6:07 - 6:08the next question is:
-
6:08 - 6:09How do I read it?
-
6:09 - 6:11How do I make sense out of it?
-
6:11 - 6:16Well, for however good you can be
at assembling Swedish furniture, -
6:16 - 6:19this instruction manual
is nothing you can crack in your life. -
6:19 - 6:21(Laughter)
-
6:21 - 6:24And so, in 2014, two famous TEDsters,
-
6:24 - 6:27Peter Diamandis and Craig Venter himself,
-
6:27 - 6:29decided to assemble a new company.
-
6:29 - 6:30Human Longevity was born,
-
6:30 - 6:31with one mission:
-
6:31 - 6:33trying everything we can try
-
6:33 - 6:36and learning everything
we can learn from these books, -
6:36 - 6:38with one target --
-
6:39 - 6:42making real the dream
of personalized medicine, -
6:42 - 6:45understanding what things
should be done to have better health -
6:45 - 6:48and what are the secrets in these books.
-
6:48 - 6:53An amazing team, 40 data scientists
and many, many more people, -
6:53 - 6:54a pleasure to work with.
-
6:54 - 6:56The concept is actually very simple.
-
6:56 - 6:59We're going to use a technology
called machine learning. -
6:59 - 7:04On one side, we have genomes --
thousands of them. -
7:04 - 7:08On the other side, we collected
the biggest database of human beings: -
7:08 - 7:12phenotypes, 3D scan, NMR --
everything you can think of. -
7:12 - 7:15Inside there, on these two opposite sides,
-
7:15 - 7:18there is the secret of translation.
-
7:18 - 7:20And in the middle, we build a machine.
-
7:21 - 7:23We build a machine
and we train a machine -- -
7:23 - 7:26well, not exactly one machine,
many, many machines -- -
7:26 - 7:31to try to understand and translate
the genome in a phenotype. -
7:31 - 7:35What are those letters,
and what do they do? -
7:35 - 7:37It's an approach that can
be used for everything, -
7:37 - 7:40but using it in genomics
is particularly complicated. -
7:41 - 7:44Little by little we grew and we wanted
to build different challenges. -
7:44 - 7:47We started from the beginning,
from common traits. -
7:47 - 7:49Common traits are comfortable
because they are common, -
7:49 - 7:50everyone has them.
-
7:50 - 7:53So we started to ask our questions:
-
7:53 - 7:54Can we predict height?
-
7:55 - 7:57Can we read the books
and predict your height? -
7:57 - 7:58Well, we actually can,
-
7:58 - 8:00with five centimeters of precision.
-
8:00 - 8:03BMI is fairly connected to your lifestyle,
-
8:03 - 8:07but we still can, we get in the ballpark,
eight kilograms of precision. -
8:07 - 8:08Can we predict eye color?
-
8:08 - 8:10Yeah, we can.
-
8:10 - 8:11Eighty percent accuracy.
-
8:11 - 8:13Can we predict skin color?
-
8:13 - 8:16Yeah we can, 80 percent accuracy.
-
8:16 - 8:17Can we predict age?
-
8:18 - 8:22We can, because apparently,
the code changes during your life. -
8:22 - 8:25It gets shorter, you lose pieces,
it gets insertions. -
8:25 - 8:28We read the signals, and we make a model.
-
8:28 - 8:30Now, an interesting challenge:
-
8:30 - 8:32Can we predict a human face?
-
8:33 - 8:34It's a little complicated,
-
8:34 - 8:38because a human face is scattered
among millions of these letters. -
8:38 - 8:40And a human face is not
a very well-defined object. -
8:40 - 8:42So, we had to build an entire tier of it
-
8:42 - 8:45to learn and teach
a machine what a face is, -
8:45 - 8:47and embed and compress it.
-
8:47 - 8:49And if you're comfortable
with machine learning, -
8:49 - 8:52you understand what the challenge is here.
-
8:52 - 8:58Now, after 15 years -- 15 years after
we read the first sequence -- -
8:58 - 9:01this October, we started
to see some signals. -
9:01 - 9:04And it was a very emotional moment.
-
9:04 - 9:07What you see here is a subject
coming in our lab. -
9:08 - 9:10This is a face for us.
-
9:10 - 9:13So we take the real face of a subject,
we reduce the complexity, -
9:13 - 9:15because not everything is in your face --
-
9:15 - 9:19lots of features and defects
and asymmetries come from your life. -
9:19 - 9:22We symmetrize the face,
and we run our algorithm. -
9:23 - 9:25The results that I show you right now,
-
9:25 - 9:29this is the prediction we have
from the blood. -
9:30 - 9:31(Applause)
-
9:31 - 9:33Wait a second.
-
9:33 - 9:37In these seconds, your eyes are watching,
left and right, left and right, -
9:37 - 9:41and your brain wants
those pictures to be identical. -
9:41 - 9:44So I ask you to do
another exercise, to be honest. -
9:44 - 9:46Please search for the differences,
-
9:46 - 9:47which are many.
-
9:47 - 9:50The biggest amount of signal
comes from gender, -
9:50 - 9:55then there is age, BMI,
the ethnicity component of a human. -
9:55 - 9:59And scaling up over that signal
is much more complicated. -
9:59 - 10:02But what you see here,
even in the differences, -
10:02 - 10:06lets you understand
that we are in the right ballpark, -
10:06 - 10:07that we are getting closer.
-
10:07 - 10:10And it's already giving you some emotions.
-
10:10 - 10:12This is another subject
that comes in place, -
10:12 - 10:14and this is a prediction.
-
10:14 - 10:18A little smaller face, we didn't get
the complete cranial structure, -
10:18 - 10:21but still, it's in the ballpark.
-
10:22 - 10:24This is a subject that comes in our lab,
-
10:24 - 10:25and this is the prediction.
-
10:26 - 10:31So these people have never been seen
in the training of the machine. -
10:31 - 10:34These are the so-called "held-out" set.
-
10:34 - 10:37But these are people that you will
probably never believe. -
10:37 - 10:40We're publishing everything
in a scientific publication, -
10:40 - 10:41you can read it.
-
10:41 - 10:44But since we are onstage,
Chris challenged me. -
10:44 - 10:47I probably exposed myself
and tried to predict -
10:47 - 10:50someone that you might recognize.
-
10:50 - 10:55So, in this vial of blood --
and believe me, you have no idea -
10:55 - 10:58what we had to do to have
this blood now, here -- -
10:58 - 11:02in this vial of blood is the amount
of biological information -
11:02 - 11:04that we need to do a full genome sequence.
-
11:04 - 11:06We just need this amount.
-
11:07 - 11:10We ran this sequence,
and I'm going to do it with you. -
11:10 - 11:14And we start to layer up
all the understanding we have. -
11:14 - 11:17In the vial of blood,
we predicted he's a male. -
11:17 - 11:18And the subject is a male.
-
11:19 - 11:21We predict that he's a meter and 76 cm.
-
11:21 - 11:24The subject is a meter and 77 cm.
-
11:24 - 11:28So, we predicted that he's 76;
the subject is 82. -
11:29 - 11:31We predict his age, 38.
-
11:31 - 11:33The subject is 35.
-
11:34 - 11:36We predict his eye color.
-
11:37 - 11:38Too dark.
-
11:38 - 11:40We predict his skin color.
-
11:40 - 11:41We are almost there.
-
11:42 - 11:43That's his face.
-
11:45 - 11:48Now, the reveal moment:
-
11:48 - 11:50the subject is this person.
-
11:50 - 11:52(Laughter)
-
11:52 - 11:54And I did it intentionally.
-
11:54 - 11:58I am a very particular
and peculiar ethnicity. -
11:58 - 12:01Southern European, Italians --
they never fit in models. -
12:01 - 12:06And it's particular -- that ethnicity
is a complex corner case for our model. -
12:06 - 12:08But there is another point.
-
12:08 - 12:11So, one of the things that we use
a lot to recognize people -
12:11 - 12:13will never be written in the genome.
-
12:13 - 12:15It's our free will, it's how I look.
-
12:15 - 12:18Not my haircut in this case,
but my beard cut. -
12:19 - 12:22So I'm going to show you, I'm going to,
in this case, transfer it -- -
12:22 - 12:25and this is nothing more
than Photoshop, no modeling -- -
12:25 - 12:27the beard on the subject.
-
12:27 - 12:30And immediately, we get
much, much better in the feeling. -
12:31 - 12:34So, why do we do this?
-
12:36 - 12:41We certainly don't do it
for predicting height -
12:41 - 12:43or taking a beautiful picture
out of your blood. -
12:44 - 12:48We do it because the same technology
and the same approach, -
12:48 - 12:51the machine learning of this code,
-
12:51 - 12:54is helping us to understand how we work,
-
12:54 - 12:56how your body works,
-
12:56 - 12:57how your body ages,
-
12:57 - 13:00how disease generates in your body,
-
13:00 - 13:03how your cancer grows and develops,
-
13:03 - 13:05how drugs work
-
13:05 - 13:07and if they work on your body.
-
13:08 - 13:09This is a huge challenge.
-
13:10 - 13:12This is a challenge that we share
-
13:12 - 13:14with thousands of other
researchers around the world. -
13:14 - 13:16It's called personalized medicine.
-
13:17 - 13:21It's the ability to move
from a statistical approach -
13:21 - 13:23where you're a dot in the ocean,
-
13:23 - 13:24to a personalized approach,
-
13:25 - 13:27where we read all these books
-
13:27 - 13:30and we get an understanding
of exactly how you are. -
13:30 - 13:34But it is a particularly
complicated challenge, -
13:34 - 13:38because of all these books, as of today,
-
13:38 - 13:40we just know probably two percent:
-
13:41 - 13:45four books of more than 175.
-
13:46 - 13:49And this is not the topic of my talk,
-
13:50 - 13:53because we will learn more.
-
13:53 - 13:56There are the best minds
in the world on this topic. -
13:57 - 13:59The prediction will get better,
-
13:59 - 14:01the model will get more precise.
-
14:01 - 14:03And the more we learn,
-
14:03 - 14:08the more we will
be confronted with decisions -
14:08 - 14:11that we never had to face before
-
14:11 - 14:12about life,
-
14:12 - 14:14about death,
-
14:14 - 14:16about parenting.
-
14:21 - 14:25So, we are touching the very
inner detail on how life works. -
14:26 - 14:29And it's a revolution
that cannot be confined -
14:29 - 14:32in the domain of science or technology.
-
14:33 - 14:35This must be a global conversation.
-
14:36 - 14:41We must start to think of the future
we're building as a humanity. -
14:41 - 14:45We need to interact with creatives,
with artists, with philosophers, -
14:45 - 14:47with politicians.
-
14:47 - 14:48Everyone is involved,
-
14:48 - 14:51because it's the future of our species.
-
14:51 - 14:55Without fear, but with the understanding
-
14:55 - 14:59that the decisions
that we make in the next year -
14:59 - 15:03will change the course of history forever.
-
15:04 - 15:05Thank you.
-
15:05 - 15:15(Applause)
- Title:
- How to read the genome and build a human being
- Speaker:
- Riccardo Sabatini
- Description:
-
Secrets, disease and beauty are all written in the human genome, the complete set of genetic instructions needed to build a human being. Now, as scientist and entrepreneur Riccardo Sabatini shows us, we have the power to read this complex code, predicting things like height, eye color, age and even facial structure — all from a vial of blood. And soon, Sabatini says, our new understanding of the genome will allow us to personalize treatments for diseases like cancer. We have the power to change life as we know it. How will we use it?
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 15:28
Brian Greene approved English subtitles for How to read the genome and build a human being | ||
Brian Greene edited English subtitles for How to read the genome and build a human being | ||
Brian Greene edited English subtitles for How to read the genome and build a human being | ||
Brian Greene edited English subtitles for How to read the genome and build a human being | ||
Brian Greene edited English subtitles for How to read the genome and build a human being | ||
Brian Greene edited English subtitles for How to read the genome and build a human being | ||
Brian Greene edited English subtitles for How to read the genome and build a human being | ||
Brian Greene edited English subtitles for How to read the genome and build a human being |