Albert-László Barabási at TEDMED 2012
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0:00 - 0:26[MUSIC]
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0:26 - 0:31So, in many ways a broken car is not so different from a disease,
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0:31 - 0:34when the engine is smoking and the lights don't come up.
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0:34 - 0:38There's a fundamental difference, however, between humans and cars.
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0:38 - 0:44If I can get my car to a mechanic, I can be pretty certain that they can fix it,
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0:44 - 0:49which is much more than we can say about many of our diseases today.
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0:49 - 0:56So why can a mechanic, with much less education and much less bucks than a doctor,
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0:56 - 1:02fix our car, while our doctors often let us go with diseases persisting in our body?
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1:02 - 1:08Well, there are a number of things that actually a mechanic has that our doctor doesn't have right now.
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1:08 - 1:12First of all, it's got a parts list.
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1:12 - 1:16It has a blueprint telling us how the pieces connect together.
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1:16 - 1:21It has diagnostics tools to figure out where the components, which is broken and what is healthy.
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1:21 - 1:25It has the means, essentially, to replace the parts.
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1:25 - 1:29Now let's think about it. Which of these components are available to our doctor today?
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1:29 - 1:34Well, the good news is that they've finally got the parts list.
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1:34 - 1:38That was the outcome of the Human Genome Project.
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1:38 - 1:42And when the human genome was actually mapped about ten years ago, we thought
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1:42 - 1:48It's going to be easy from now. From the parts, we will have essentially the world bonanza that we need to fix us, humans.
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1:48 - 1:58But of course reality sinks in. We also thought that these many pieces will eventually give us lots of drugs.
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1:58 - 2:05In 2001, or 2000, the year before the genome project was unveiled, the FDA approved about a hundred drugs per year.
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2:05 - 2:09We thought this number could only go up.
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2:09 - 2:12It could only just increase.
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2:12 - 2:14Yet the reality sinks in.
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2:14 - 2:19The number of new drugs in just the last ten years, went from a hundred before the genome, to about twenty per year.
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2:19 - 2:24In hindsight, the reason is pretty clear.
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2:24 - 2:28It's not enough to have the parts list.
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2:28 - 2:32We also need to actually figure out how the pieces fit together.
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2:32 - 2:40That is, we should not look at this picture, but rather we should be looking at how the wiring diagram of the car looks like.
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2:40 - 2:44How the wiring of ourselves actually look like.
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2:44 - 2:51How the genes and the proteins and the metabolites link to each other, forming a conistent network.
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2:51 - 2:58Because this network, which I am going to try to tell you today, is really the key to understanding human diseases.
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2:58 - 3:05Now, the problem is that if you look at this map, you soon realize that it looks completely random.
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3:05 - 3:08Randomness certainly has the upper hand.
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3:08 - 3:13But down the line, it is not. I believe there is a deep order behind this wiring diagram.
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3:13 - 3:19And understanding that order is the key to understand human diseases.
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3:19 - 3:25Now, I am a physicist, and the conventional wisdom is that as a physicist, I should be studying very big objects:
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3:25 - 3:29stars, quasars, or very tiny ones like the Higgs boson and quarks.
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3:29 - 3:36Yet about a decade ago, my interest has turned to a completely different subject: Complex systems and networks.
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3:36 - 3:44And that's because our very existence depends on the successful functioning of systems and networks behind us.
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3:44 - 3:54And I also believe the scientific challenges behind complex systems and networks are just as deep as behind quarks or quasars.
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3:54 - 3:58So I started looking at the structure of the Internet.
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3:58 - 4:04Telling us how many, many computers are linked together by various cables.
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4:04 - 4:14I looked at the structure of the social network, telling us how do societies wire together through many friendship and other linkages.
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4:14 - 4:18And eventually I started looking at the structure of the cell.
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4:18 - 4:23Telling us you our genes and proteins link to each other into a coherent network.
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4:23 - 4:27And through that path, I arrived at human diseases.
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4:27 - 4:30A path that is rarely taken by physicists.
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4:30 - 4:35Now, the fundamental question that really comes up from that is:
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4:35 - 4:43How do we think about diseases in the context of these of these very very complicated networks?
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4:43 - 4:51And from that, let me turn to a map that we all understand, probably the most famous map out there, which is the map of Manhattan.
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4:51 - 4:57Now, in many ways, Manhattan is structured different from a cell.
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4:57 - 5:04But let's for a moment carry with me and let's believe together that this is really not a map of Manhattan but a map of a cell.
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5:04 - 5:09Where the intersections showing us nodes are the genes and the proteins.
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5:09 - 5:14And the street segments that connect them corresponds to the interactions between them.
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5:14 - 5:20Now, down the line, this is not so different from what happens in our cells.
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5:20 - 5:27The busy life of Manhattan very easily maps into the crowded life of the cell where molecules interact with each other,
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5:27 - 5:30and recombine and transport and so on.
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5:30 - 5:35So there's lots of similarities on the surface between them.
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5:35 - 5:39And if we look at Manhattan, we also realize that action is not uniformly spread within the city.
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5:39 - 5:46If you want to go, for example, to the theater, you don't go to ANY parts of Manhattan, you would go to the theater district.
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5:46 - 5:50Because that's where most of the theaters are, that's where the shows are.
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5:50 - 5:57If you want to buy an artwork. You will not actually be going ANYwhere in the city, but you would be going to the gallery district.
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5:57 - 6:04Because there is one small region in the town that has most of the high-end galleries, and that's where most of the artwork is for sale.
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6:04 - 6:06The same is true in the cell.
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6:06 - 6:10Its functions are not spread uniformly within the network.
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6:10 - 6:14But there are other pockets within the network that are responsible for particular functions,
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6:14 - 6:18and their breakdown potentially leads to disease.
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6:18 - 6:22So the way to think about disease in the context of the network is to think that
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6:22 - 6:26there are different regions that correspond to different diseases on this map.
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6:26 - 6:31So, for example, you could say that cancer stays somewhere around Wall Street
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6:31 - 6:33[AUDIENCE LAUGHTER]
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6:33 - 6:38And bipolar disease would be somewhere around Times Square.
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6:38 - 6:39[AUDIENCE LAUGHTER]
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6:39 - 6:46And you know asthma, a respiratory disease, it would be somewhere up next to the Washington Bridge.
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6:46 - 6:51Where Manhattan breathes its people and cars into New Jersey and The Bronx.
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6:51 - 6:52[AUDIENCE LAUGHTER]
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6:52 - 6:55Now, under normal conditions
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6:55 - 6:58Manhattan is full of traffic.
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6:58 - 7:01And that's how the cell looks like normally.
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7:01 - 7:07But if we had defects, some genes breaking down, that corresponds to some of the intersections not working, and
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7:07 - 7:12soon enough we would get a very typical Manhattan disease: A traffic jam.
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7:12 - 7:16This is not so different from what happens in our cells.
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7:16 - 7:19Because there are many different ways you can get the same phenotype.
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7:19 - 7:24In the same way, there are many different ways you can get a disease.
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7:24 - 7:33For example, there was a famous study by Burt [???]'s group which sequenced about 300 individuals who all had colo-rectal cancer.
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7:33 - 7:34They had the same phenotype.
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7:34 - 7:41Therefore the expectation was that all of them would have probably the same mutations in the same genes.
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7:41 - 7:49Yet, the study showed that not only did they not have the same set of mutations, but the mutations were all in different groups of genes.
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7:49 - 7:56There were no two individuals who would actually have the same genes exactly the same group of genes' defect.
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7:56 - 8:03The only way to understand how it's possible that many different genes broken down in different combinations linked to the same disease,
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8:03 - 8:06is to think in terms of Manhattan.
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8:06 - 8:12If you think in terms of disease module and to really have the wiring diagram of the disease module,
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8:12 - 8:15to understand the breakdown modes of the particular system.
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8:15 - 8:26Now, if we really believe that particular picture, the next step for us is to say, how do we proceed from here?
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8:26 - 8:31It's very easy. Get the map, find the disease module, and drug it.
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8:31 - 8:34Now of course, you do realize there's a catch here.
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8:34 - 8:40And the catch of course is, unlike for Manhattan, we don't have yet a map for the cells.
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8:40 - 8:46I mean, we do, but some of the maps we have are very incomplete.
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8:46 - 8:49For example, the best protein interaction that we have right now,
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8:49 - 8:54we believe it has only five percent of the links that are supposed to be in our cells.
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8:54 - 9:01Now, having five percent of the links means that we are missing 95% of the links.
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9:01 - 9:04And that has dramatic consequences on the system.
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9:04 - 9:07Let me illustrate that on Manhattan.
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9:07 - 9:14Let's go ahead and take 95% of street segments and remove it from the map, and let's see what does it do to Manhattan.
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9:14 - 9:20And the consequence is obvious. The network is broken into tiny pieces.
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9:20 - 9:25And as a result, the modules, the Wall Street neighborhood and the Times Square neighborhood
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9:25 - 9:29that were clearly distinguishable before would be all over the map.
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9:29 - 9:32You don't know any more where your disease module is.
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9:32 - 9:34So what can we do then?
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9:34 - 9:38Well, first and foremost, we must improve on our maps.
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9:38 - 9:44And that's what my colleague Marc Vidal does at Dana-Farber Cancer Institute,
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9:44 - 9:50who in the last twenty years has developed a whole series of automatic tools to systematically map
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9:50 - 9:55the protein interactions within the cell, one of the very important components of the cellular network.
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9:55 - 10:02As a result of his work, a few years ago, we got what we call the 5% map, the one I referred to earlier.
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10:02 - 10:09This year, he's about to unveil another landmark: the 20% map of the human cell.
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10:09 - 10:14And if we left him on the same track, actually he would do the full network.
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10:14 - 10:21It may take a decade or two to get to it, but eventually [???] and many others, we will get a map.
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10:21 - 10:23But what until then?
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10:23 - 10:25Shall we just wait for him to finish the work?
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10:25 - 10:27And the answer is, well, not really.
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10:27 - 10:31There's lots of things we can actually do using the existing maps.
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10:31 - 10:33This is how the map looks like right now.
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10:33 - 10:36This is all the interactions we believe should be in the cell.
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10:36 - 10:41And in order to understand where diseases lie in that, what I'm going to do next is
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10:41 - 10:46I will go ahead and place on this map a particular disease, in this case asthma.
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10:46 - 10:52Asthma is a respiratory disease that leads to coughing, shortness of breath, and many other symptoms, and
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10:52 - 10:57there has been tremendous amount of research on the [???] genetic origins of asthma.
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10:57 - 11:01Therefore, we about a hundred genes that are known to be associated with asthma.
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11:01 - 11:07So if we put them on the map, and I'm showing them now here as purple nodes,
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11:07 - 11:10then we would expect them to be all together.
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11:10 - 11:12Because they really should correspond to our disease model.
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11:12 - 11:14But they're not. They're all over the map.
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11:14 - 11:20And the reason they're all over the map is because we're missing 95% of the interactions.
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11:20 - 11:25So the critical links that would really hold them together in one module are all gone, they are not there yet.
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11:25 - 11:29So what is it we can do next?
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11:29 - 11:31We can use the power of the network.
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11:31 - 11:39They are really built into the network and try to figure out other genes that may also be involved in asthma, about whom we don't know yet.
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11:39 - 11:41And that's exactly what we did next.
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11:41 - 11:48We took this map and we run algorithm through that, that really extract the information from this map,
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11:48 - 11:50and identify what you see in front of your eyes.
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11:50 - 11:53The asthma module within the cell.
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11:53 - 12:00Now if we know the asthma module, from there we can understand the disease's mechanism, the disease's pathways,
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12:00 - 12:04and one day can actually help us understand the drugs.
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12:04 - 12:07But this is not only true for asthma.
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12:07 - 12:10Not only asthma is located well in the network.
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12:10 - 12:15You can take some other diseases, for example COPD, and try to do the same thing.
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12:15 - 12:18COPD is often called the smokers' disease,
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12:18 - 12:20because smokers are at a very high chance of getting it,
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12:20 - 12:23and has somewhat similar symptoms to asthma.
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12:23 - 12:29Not surprisingly, it seems to be that the two modules are significantly overlapping,
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12:29 - 12:32and are certainly in the same region of the network.
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12:32 - 12:38We do expect, however, to have other diseases that would be in a completely different part of the network.
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12:38 - 12:43And what is crucial here is to understand that the relationship between these diseases,
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12:43 - 12:48to what degree they overlap, and how they relate to each other is really crucial to understand
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12:48 - 12:52how they relate to each other.
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12:52 - 12:55and whether they are similar or very different from each other.
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12:55 - 13:01So one way to look at it is to let's look at the relationship of all diseases.
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13:01 - 13:03And that's what I'm showing you here.
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13:03 - 13:09Here in the next slide, every node corresponds to a particular disease,
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13:09 - 13:13and two diseases are connected to each other if they share a gene.
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13:13 - 13:18Why would you do that? Because if they share a gene, then very likely their disease module overlaps,
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13:18 - 13:22and therefore they must be in the same region of the network.
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13:22 - 13:28And what is amazing about this map is that there are links between apparently unrelated diseases,
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13:28 - 13:35which is telling us that if you really want to treat--if you have two diseases and want to treat them, today you may go to different doctors,
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13:35 - 13:37different hospitals, different floors.
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13:37 - 13:41But down at the level of the cell, they are not independent of each other.
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13:41 - 13:45They're connected because they're rooted in somewhat the same neighborhood.
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13:45 - 13:52So what this is telling us, this "diseasedom" as I will call it, is that if we want to understand disease,
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13:52 - 14:01we should not be looking really at what we normally look at, but we should be looking at the network within our cells.
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14:01 - 14:09This is the one that really matters. This is the one that really should tell us how to classify diseases.
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14:09 - 14:10You know, we probably got it fundamentally wrong.
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14:10 - 14:13It's not heart, it's not brains, it's not kidneys.
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14:13 - 14:20Sooner or later we must abandon this organ-based description of disease and turn to what really matter.
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14:20 - 14:31We should stop training cardiologists and neurologists, and rather the doctor of the future needs to become a bit of networkologist,
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14:31 - 14:36to understand where diseases are lying within that network and how they relate to each other.
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14:36 - 14:45So I personally believe we need a new medicine, to truly execute the paradigm change that genomics allowed us to achieve.
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14:45 - 14:52I would call it network medicine, and I think it's really within our footstep to do and achieve that.
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14:52 - 14:59I also think that network medicine will not only help us understand the mechanism of disease,
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14:59 - 15:10but it will affect all aspects of healthcare, from the role of the environment all the way to how we actually deliver care to a particular patient.
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15:10 - 15:23So, coming back to our original question, the good news is that doctors are increasing many of the tools that the car mechanic has today.
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15:23 - 15:27If you think about it, the genomics provides the parts list,
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15:27 - 15:30metabiomics and proteomics provide the diagnostic tools,
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15:30 - 15:38and gene therapy is really giving us the way one day to replace the components, with the pieces that are not broken.
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15:38 - 15:42But a car mechanic would be useless without a blueprint.
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15:42 - 15:49And in the same way I believe that to truly understand diseases, we need to give into the hands of our doctors the map.
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15:49 - 15:56Now I'm a physicist, and a network scientist. I am not a medical doctor.
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15:56 - 16:00Hence, I will never cure any of your diseases.
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16:00 - 16:04I can help, however, decipher the map:
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16:04 - 16:09The real book of life, the book that is currently missing most of its pages.
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16:09 - 16:16But once we learn to read it, we'll get much closer to the secret of life and curing disease.
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16:16 - 16:18Thank you very much.
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16:18 -[APPLAUSE]
- Title:
- Albert-László Barabási at TEDMED 2012
- Description:
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Networks guru and author Albert-László Barabási says diseases are the results of system breakdowns within the body, and mapping intracellular protein networks will help us discover cures.
- Video Language:
- English
- Team:
- Captions Requested
- Duration:
- 16:22
Retired user edited English subtitles for Albert-László Barabási at TEDMED 2012 | ||
Retired user edited English subtitles for Albert-László Barabási at TEDMED 2012 | ||
Retired user edited English subtitles for Albert-László Barabási at TEDMED 2012 | ||
Retired user edited English subtitles for Albert-László Barabási at TEDMED 2012 | ||
Retired user edited English subtitles for Albert-László Barabási at TEDMED 2012 | ||
Retired user edited English subtitles for Albert-László Barabási at TEDMED 2012 |