< Return to Video

Albert-László Barabási at TEDMED 2012

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

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.

more » « less
Video Language:
English
Team:
Captions Requested
Duration:
16:22

English subtitles

Revisions Compare revisions