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[MUSIC]

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So, in many ways a broken car is not so different from a disease,

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when the engine is smoking and the lights don't come up.

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There's a fundamental difference, however, between humans and cars.

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If I can get my car to a mechanic, I can be pretty certain that they can fix it,

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which is much more than we can say about many of our diseases today.

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So why can a mechanic, with much less education and much less bucks than a doctor,

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fix our car, while our doctors often let us go with diseases persisting in our body?

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Well, there are a number of things that actually a mechanic has that our doctor doesn't have right now.

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First of all, it's got a parts list.

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It has a blueprint telling us how the pieces connect together.

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It has diagnostics tools to figure out where the components, which is broken and what is healthy.

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It has the means, essentially, to replace the parts.

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Now let's think about it. Which of these components are available to our doctor today?

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Well, the good news is that they've finally got the parts list.

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That was the outcome of the Human Genome Project.

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And when the human genome was actually mapped about ten years ago, we thought

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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.

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But of course reality sinks in. We also thought that these many pieces will eventually give us lots of drugs.

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In 2001, or 2000, the year before the genome project was unveiled, the FDA approved about a hundred drugs per year.

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We thought this number could only go up.

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It could only just increase.

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Yet the reality sinks in.

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The 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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In hindsight, the reason is pretty clear.

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It's not enough to have the parts list.

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We also need to actually figure out how the pieces fit together.

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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.

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How the wiring of ourselves actually look like.

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How the genes and the proteins and the metabolites link to each other, forming a conistent network.

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Because this network, which I am going to try to tell you today, is really the key to understanding human diseases.

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Now, the problem is that if you look at this map, you soon realize that it looks completely random.

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Randomness certainly has the upper hand.

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But down the line, it is not. I believe there is a deep order behind this wiring diagram.

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And understanding that order is the key to understand human diseases.

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Now, I am a physicist, and the conventional wisdom is that as a physicist, I should be studying very big objects:

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stars, quasars, or very tiny ones like the Higgs boson and quarks.

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Yet about a decade ago, my interest has turned to a completely different subject: Complex systems and networks.

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And that's because our very existence depends on the successful functioning of systems and networks behind us.

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And I also believe the scientific challenges behind complex systems and networks are just as deep as behind quarks or quasars.

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So I started looking at the structure of the Internet.

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Telling us how many, many computers are linked together by various cables.

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I 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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And eventually I started looking at the structure of the cell.

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Telling us you our genes and proteins link to each other into a coherent network.

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And through that path, I arrived at human diseases.

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A path that is rarely taken by physicists.

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Now, the fundamental question that really comes up from that is:

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How do we think about diseases in the context of these of these very very complicated networks?

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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.

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Now, in many ways, Manhattan is structured different from a cell.

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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.

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Where the intersections showing us nodes are the genes and the proteins.

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And the street segments that connect them corresponds to the interactions between them.

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Now, down the line, this is not so different from what happens in our cells.

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The busy life of Manhattan very easily maps into the crowded life of the cell where molecules interact with each other,

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and recombine and transport and so on.

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So there's lots of similarities on the surface between them.

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And if we look at Manhattan, we also realize that action is not uniformly spread within the city.

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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.

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Because that's where most of the theaters are, that's where the shows are.

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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.

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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.

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The same is true in the cell.

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Its functions are not spread uniformly within the network.

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But there are other pockets within the network that are responsible for particular functions,

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and their breakdown potentially leads to disease.

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So the way to think about disease in the context of the network is to think that

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there are different regions that correspond to different diseases on this map.

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So, for example, you could say that cancer stays somewhere around Wall Street

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[AUDIENCE LAUGHTER]

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And bipolar disease would be somewhere around Times Square.

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[AUDIENCE LAUGHTER]

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And you know asthma, a respiratory disease, it would be somewhere up next to the Washington Bridge.

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Where Manhattan breathes its people and cars into New Jersey and The Bronx.

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[AUDIENCE LAUGHTER]

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Now, under normal conditions

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Manhattan is full of traffic.

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And that's how the cell looks like normally.

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But if we had defects, some genes breaking down, that corresponds to some of the intersections not working, and

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soon enough we would get a very typical Manhattan disease: A traffic jam.

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This is not so different from what happens in our cells.

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Because there are many different ways you can get the same phenotype.

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In the same way, there are many different ways you can get a disease.

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For 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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They had the same phenotype.

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Therefore the expectation was that all of them would have probably the same mutations in the same genes.

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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.

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There were no two individuals who would actually have the same genes exactly the same group of genes' defect.

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The 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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is to think in terms of Manhattan.

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If you think in terms of disease module and to really have the wiring diagram of the disease module,

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to understand the breakdown modes of the particular system.

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Now, 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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It's very easy. Get the map, find the disease module, and drug it.

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Now of course, you do realize there's a catch here.

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And the catch of course is, unlike for Manhattan, we don't have yet a map for the cells.

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I mean, we do, but some of the maps we have are very incomplete.

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For example, the best protein interaction that we have right now,

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we believe it has only five percent of the links that are supposed to be in our cells.

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Now, having five percent of the links means that we are missing 95% of the links.

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And that has dramatic consequences on the system.

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Let me illustrate that on Manhattan.

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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.

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And the consequence is obvious. The network is broken into tiny pieces.

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And as a result, the modules, the Wall Street neighborhood and the Times Square neighborhood

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that were clearly distinguishable before would be all over the map.

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You don't know any more where your disease module is.

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So what can we do then?

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Well, first and foremost, we must improve on our maps.

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And that's what my colleague Marc Vidal does at Dana-Farber Cancer Institute,

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who in the last twenty years has developed a whole series of automatic tools to systematically map

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the protein interactions within the cell, one of the very important components of the cellular network.

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As 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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This year, he's about to unveil another landmark: the 20% map of the human cell.

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And if we left him on the same track, actually he would do the full network.

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It may take a decade or two to get to it, but eventually [???] and many others, we will get a map.

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But what until then?

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Shall we just wait for him to finish the work?

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And the answer is, well, not really.

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There's lots of things we can actually do using the existing maps.

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This is how the map looks like right now.

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This is all the interactions we believe should be in the cell.

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And in order to understand where diseases lie in that, what I'm going to do next is

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I will go ahead and place on this map a particular disease, in this case asthma.

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Asthma is a respiratory disease that leads to coughing, shortness of breath, and many other symptoms, and

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there has been tremendous amount of research on the [???] genetic origins of asthma.

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Therefore, we about a hundred genes that are known to be associated with asthma.

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So if we put them on the map, and I'm showing them now here as purple nodes,

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then we would expect them to be all together.

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Because they really should correspond to our disease model.

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But they're not. They're all over the map.

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And the reason they're all over the map is because we're missing 95% of the interactions.

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So the critical links that would really hold them together in one module are all gone, they are not there yet.

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So what is it we can do next?

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We can use the power of the network.

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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.

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And that's exactly what we did next.

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We took this map and we run algorithm through that, that really extract the information from this map,

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and identify what you see in front of your eyes.

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The asthma module within the cell.

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Now if we know the asthma module, from there we can understand the disease's mechanism, the disease's pathways,

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and one day can actually help us understand the drugs.

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But this is not only true for asthma.

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Not only asthma is located well in the network.

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You can take some other diseases, for example COPD, and try to do the same thing.

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COPD is often called the smokers' disease,

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because smokers are at a very high chance of getting it,

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and has somewhat similar symptoms to asthma.

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Not surprisingly, it seems to be that the two modules are significantly overlapping,

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and are certainly in the same region of the network.

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We do expect, however, to have other diseases that would be in a completely different part of the network.

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And what is crucial here is to understand that the relationship between these diseases,

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to what degree they overlap, and how they relate to each other is really crucial to understand

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how they relate to each other.

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and whether they are similar or very different from each other.

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So one way to look at it is to let's look at the relationship of all diseases.

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And that's what I'm showing you here.

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Here in the next slide, every node corresponds to a particular disease,

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and two diseases are connected to each other if they share a gene.

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Why would you do that? Because if they share a gene, then very likely their disease module overlaps,

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and therefore they must be in the same region of the network.

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And what is amazing about this map is that there are links between apparently unrelated diseases,

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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,

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different hospitals, different floors.

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But down at the level of the cell, they are not independent of each other.

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They're connected because they're rooted in somewhat the same neighborhood.

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So what this is telling us, this "diseasedom" as I will call it, is that if we want to understand disease,

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we 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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This is the one that really matters. This is the one that really should tell us how to classify diseases.

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You know, we probably got it fundamentally wrong.

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It's not heart, it's not brains, it's not kidneys.

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Sooner or later we must abandon this organ-based description of disease and turn to what really matter.

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We should stop training cardiologists and neurologists, and rather the doctor of the future needs to become a bit of networkologist,

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to understand where diseases are lying within that network and how they relate to each other.

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So I personally believe we need a new medicine, to truly execute the paradigm change that genomics allowed us to achieve.

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I would call it network medicine, and I think it's really within our footstep to do and achieve that.

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I also think that network medicine will not only help us understand the mechanism of disease,

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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.

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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.

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If you think about it, the genomics provides the parts list,

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metabiomics and proteomics provide the diagnostic tools,

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and 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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But a car mechanic would be useless without a blueprint.

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And 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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Now I'm a physicist, and a network scientist. I am not a medical doctor.

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Hence, I will never cure any of your diseases.

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I can help, however, decipher the map:

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The real book of life, the book that is currently missing most of its pages.

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But once we learn to read it, we'll get much closer to the secret of life and curing disease.

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Thank you very much.

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[APPLAUSE]