9:59:59.000,9:59:59.000 [MUSIC] 9:59:59.000,9:59:59.000 So, in many ways a broken car is not so different from a disease, 9:59:59.000,9:59:59.000 when the engine is smoking and the lights don't come up. 9:59:59.000,9:59:59.000 There's a fundamental difference, however, between humans and cars. 9:59:59.000,9:59:59.000 If I can get my car to a mechanic, I can be pretty certain that they can fix it, 9:59:59.000,9:59:59.000 which is much more than we can say about many of our diseases today. 9:59:59.000,9:59:59.000 So what can a mechanic, with much less education and much less bucks than a doctor, 9:59:59.000,9:59:59.000 fix our car, while our doctors often let us go with diseases persisting in our body? 9:59:59.000,9:59:59.000 Well, there are actually a number of things that a mechanic has that our doctor doesn't have right now. 9:59:59.000,9:59:59.000 First of all, it's got a parts list. 9:59:59.000,9:59:59.000 It has a blueprint telling us how the pieces connect together. 9:59:59.000,9:59:59.000 It has diagnostics tools to figure out where the components, which is broken and which is healthy. 9:59:59.000,9:59:59.000 It has the means, essentially, to replace the parts. 9:59:59.000,9:59:59.000 Now let's think about it. Which of these components are available to our doctor today? 9:59:59.000,9:59:59.000 Well, the good news is that they've finally got the parts list. 9:59:59.000,9:59:59.000 That was the output of the human genome project. 9:59:59.000,9:59:59.000 And when the human genome was actually mapped about ten years ago, we thought 9:59:59.000,9:59:59.000 It's going to be easy from now. From the parts, we will have essentially the world bonanza that we need to fix humans, us. 9:59:59.000,9:59:59.000 But of course reality sinks in. We also realize that these many pieces will eventually give us many drugs. 9:59:59.000,9:59:59.000 In 2001, or 2000, the year before the genome project was unveiled, the FDA approved about a hundred drugs a year. 9:59:59.000,9:59:59.000 We thought this number could only go up. 9:59:59.000,9:59:59.000 It could only just increase. 9:59:59.000,9:59:59.000 Yet the reality just sinks in. 9:59:59.000,9:59:59.000 The number of new drugs in just the last ten years, went from a hundred before the genome, to about twenty per year. 9:59:59.000,9:59:59.000 In hindsight, the reason is pretty clear. 9:59:59.000,9:59:59.000 It's not enough to have the parts list. 9:59:59.000,9:59:59.000 We also need to actually figure out how the pieces fit together. 9:59:59.000,9:59:59.000 That is, we should not look at this picture, but rather we should be looking at how the wiring diagram of the car should look like. 9:59:59.000,9:59:59.000 How the wiring of ourselves actually look like. 9:59:59.000,9:59:59.000 How the genes and the proteins and the metabolites link to each other, forming a conistent network. 9:59:59.000,9:59:59.000 Because this network, with I am going to try to tell you today, is really the key to understanding human diseases. 9:59:59.000,9:59:59.000 Now, the problem is that if you look at this map, you soon realize that it looks completely random. 9:59:59.000,9:59:59.000 Randomness certainly has the upper hand. 9:59:59.000,9:59:59.000 But down the line, it is not. I believe there is a deep order behind this wiring diagram. 9:59:59.000,9:59:59.000 And understanding that order is the key to understand human diseases. 9:59:59.000,9:59:59.000 Now, I am a physicist, and the conventional wisdom is that as a physicist, I should be studying very large objects: 9:59:59.000,9:59:59.000 stars, quasars, or very tiny ones like the Higgs boson or quarks. 9:59:59.000,9:59:59.000 Yet about a decade ago, my interest has turned to a completely different subject: Complex systems and networks. 9:59:59.000,9:59:59.000 And that's because our very existence depends on the successful functioning of systems and networks behind us. 9:59:59.000,9:59:59.000 And I also believe the scientific challenges behind complex systems and networks are just as [???] as behind quarks or quasars. 9:59:59.000,9:59:59.000 So I started looking at the structure of th Internet. 9:59:59.000,9:59:59.000 Telling us how many, many computers are linked together by various cables. 9:59:59.000,9:59:59.000 I looked at the structure of the social network, telling us how do societies wire together through many friendship and other linkages. 9:59:59.000,9:59:59.000 And eventually I started looking at the structure of the cell. 9:59:59.000,9:59:59.000 Telling us you our genes and proteins link to each other into a coherent network. 9:59:59.000,9:59:59.000 And through that path, I arrived at human diseases. 9:59:59.000,9:59:59.000 A path that is rarely taken by physicists. 9:59:59.000,9:59:59.000 Now, the fundamental question that really comes up from that is: 9:59:59.000,9:59:59.000 How do we think about diseases in the context of these of these very very complicated networks? 9:59:59.000,9:59:59.000 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. 9:59:59.000,9:59:59.000 Now, in many ways, Manhattan is structured different from a cell. 9:59:59.000,9:59:59.000 But let's for a moment carry with me and let's believe together that it's not a map of Manhattan but a map of a cell. 9:59:59.000,9:59:59.000 Where the intersections showing us nodes are the genes and the proteins. 9:59:59.000,9:59:59.000 And the street segments that connect them corresponds to the interactions between them. 9:59:59.000,9:59:59.000 Now, down the line, this is not so different from what happens in our cells. 9:59:59.000,9:59:59.000 The busy life of Manhattan very easily maps into the crowded life of the cell where molecules interact with each other, 9:59:59.000,9:59:59.000 and recombine and transport and so on. 9:59:59.000,9:59:59.000 So there's lots of similarities on the surface between them. 9:59:59.000,9:59:59.000 And if we look at Manhattan, we also realize that action is not uniformly spread within the cit. 9:59:59.000,9:59:59.000 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. 9:59:59.000,9:59:59.000 Because that's where most of the theaters are, that's where the shows are. 9:59:59.000,9:59:59.000 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. 9:59:59.000,9:59:59.000 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. 9:59:59.000,9:59:59.000 The same is true in the cell. 9:59:59.000,9:59:59.000 Its functions are not spread uniformly within the network. 9:59:59.000,9:59:59.000 But there are other pockets within the network that are responsible for particular functions, 9:59:59.000,9:59:59.000 and their breakdown potentially leads to disease. 9:59:59.000,9:59:59.000 So the way to think about disease in the context of the network is to think that 9:59:59.000,9:59:59.000 there are different regions that correspond to different diseases on this map. 9:59:59.000,9:59:59.000 So, for example, you could say that cancer stays somewhere around Wall Street 9:59:59.000,9:59:59.000 [AUDIENCE LAUGHTER] 9:59:59.000,9:59:59.000 And bipolar disease would be somewhere around Times Square. 9:59:59.000,9:59:59.000 [AUDIENCE LAUGHTER] 9:59:59.000,9:59:59.000 And you know asthma, a respiratory disease, it would be somewhere up near the Washington Bridge. 9:59:59.000,9:59:59.000 Where Washington brings the people and cars into New Jersey and The Bronx. 9:59:59.000,9:59:59.000 [AUDIENCE LAUGHTER] 9:59:59.000,9:59:59.000 Now, under normal conditions 9:59:59.000,9:59:59.000 Manhattan is full of traffic. 9:59:59.000,9:59:59.000 And that's how the cell looks like normally. 9:59:59.000,9:59:59.000 But if we had defects, some genes breaking down, that corresponds to some of the intersections now working, and 9:59:59.000,9:59:59.000 soon enough we would get a very typical Manhattan disease: A traffic jam. 9:59:59.000,9:59:59.000 This is not so different from what happens in our cells. 9:59:59.000,9:59:59.000 Because there are many different ways you can get the same phenotype. 9:59:59.000,9:59:59.000 In the same way, there are many different ways you can get a disease. 9:59:59.000,9:59:59.000 For example, there was a famous study by Burt [???]'s group which sequenced about 300 individuals who all had colo-rectal cancer. 9:59:59.000,9:59:59.000 They had the same phenotype. 9:59:59.000,9:59:59.000 Therefore the expectation was that all of them would have probably the same mutations in the same genes. 9:59:59.000,9:59:59.000 Yet, the study showed that not only did they not have the same set of mutations, but the mutations were all in different genes. 9:59:59.000,9:59:59.000 There were no two individuals who would actually have the same genes exactly the same group of genes' defect. 9:59:59.000,9:59:59.000 The only way to understand how it's possible that many different genes broken down in different combinations linked to the same disease, 9:59:59.000,9:59:59.000 is to think in terms of Manhattan. 9:59:59.000,9:59:59.000 If you think in terms of disease module and to really have the wiring diagram of the disease module, 9:59:59.000,9:59:59.000 to understand the breakdown modes of the particular system. 9:59:59.000,9:59:59.000 Now, if we really believe that particular picture, the next step for us is to say, how do we proceed from here? 9:59:59.000,9:59:59.000 It's very easy. Get the map, get the disease module, and drug it. 9:59:59.000,9:59:59.000 Now of course, you do realize there's a catch here. 9:59:59.000,9:59:59.000 And the catch of course is, unlike for Manhattan, we don't have yet a map for the cells. 9:59:59.000,9:59:59.000 I mean, we do, but some of the maps we have are very incomplete. 9:59:59.000,9:59:59.000 For example, the best protein interaction that we have right now, 9:59:59.000,9:59:59.000 we believe it has only five percent of the links that are supposed to be in our cells. 9:59:59.000,9:59:59.000 Now, having five percent of the links means that we are missing 95% of the links. 9:59:59.000,9:59:59.000 And that has dramatic consequences on the system. 9:59:59.000,9:59:59.000 Let me illustrate that on Manhattan. 9:59:59.000,9:59:59.000 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. 9:59:59.000,9:59:59.000 And the consequences are obvious. The network is broken into tiny pieces. 9:59:59.000,9:59:59.000 And as a result, the modules, the Wall Street neighborhood and the Times Square neighborhood 9:59:59.000,9:59:59.000 that were clearly distinguishable before would be all over the map. 9:59:59.000,9:59:59.000 You don't know any more where your disease module is. 9:59:59.000,9:59:59.000 So what can we do then? 9:59:59.000,9:59:59.000 Well, first and foremost, we must improve on our maps. 9:59:59.000,9:59:59.000 And that's what my colleague Mark [???] does at Dana-Farber Cancer Institute. 9:59:59.000,9:59:59.000 who in the last twenty years has developed an automatic series of tools to systematically map 9:59:59.000,9:59:59.000 the protein interactions within the cell, one of the very important components of the cellular network. 9:59:59.000,9:59:59.000 As a result of his work, a few years ago, we got what we call the 5% map, the one I referred to earlier. 9:59:59.000,9:59:59.000 This year, he's about to unveil another landmark: the 20% map of the human cell. 9:59:59.000,9:59:59.000 And if we left him on the same track, actually he would do the full network. 9:59:59.000,9:59:59.000 It may take a decade or two to get to it, but eventually [???] we will get a map. 9:59:59.000,9:59:59.000 But what until then? 9:59:59.000,9:59:59.000 Shall we just wait for him to finish the work? 9:59:59.000,9:59:59.000 And the answer is, well, not really. 9:59:59.000,9:59:59.000 There's lots of things we can actually do using the existing maps. 9:59:59.000,9:59:59.000 This is how the map looks like right now. 9:59:59.000,9:59:59.000 This is all the interactions we believe should be in the cell. 9:59:59.000,9:59:59.000 And in order to understand where diseases lie in that, what I'm going to do next is 9:59:59.000,9:59:59.000 I will go ahead and place on this map a particular disease, in this case asthma. 9:59:59.000,9:59:59.000 Asthma is a respiratory disease that leads to coughing, shortness of breath, and many other symptoms, and 9:59:59.000,9:59:59.000 there has been tremendous amount of research on the [???] origins of asthma. 9:59:59.000,9:59:59.000 Therefore, we about a hundred genes that are known to be associated with asthma. 9:59:59.000,9:59:59.000 So if we put them on the map, and I'm showing them now here as purple notes, 9:59:59.000,9:59:59.000 then we would expect them to be all together. 9:59:59.000,9:59:59.000 Because they really should correspond to our disease model. 9:59:59.000,9:59:59.000 But they're not. They're all over the map. 9:59:59.000,9:59:59.000 And the reason they're all over the map is because we're missing 95% of the interactions. 9:59:59.000,9:59:59.000 So the missing links that would really hold them all together in one module are all gone, they are not there yet. 9:59:59.000,9:59:59.000 So what is it we can do next? 9:59:59.000,9:59:59.000 We can use the power of the network. 9:59:59.000,9:59:59.000 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. 9:59:59.000,9:59:59.000 And that's exactly what we do next. 9:59:59.000,9:59:59.000 We took this map and we run algorithm through that, which really extract the information from this map, 9:59:59.000,9:59:59.000 and identify what you see in front of your eyes. 9:59:59.000,9:59:59.000 The asthma module within the cell. 9:59:59.000,9:59:59.000 Now if we know the asthma module, from there we can understand the disease's mechanism, the disease's pathways, 9:59:59.000,9:59:59.000 and one day can actually help us understand the drugs. 9:59:59.000,9:59:59.000 But this is not only true for asthma. 9:59:59.000,9:59:59.000 Not only asthma is located well in the network. 9:59:59.000,9:59:59.000 You can take some other diseases, for example COPD, and try to do the same thing. 9:59:59.000,9:59:59.000 COPD is often called the smokers' disease, 9:59:59.000,9:59:59.000 because smokers are at a very high chance of getting it, 9:59:59.000,9:59:59.000 and has somewhat similar symptoms to asthma. 9:59:59.000,9:59:59.000 Not surprisingly, it seems to be that the two modules are significantly overlapping, 9:59:59.000,9:59:59.000 and are certainly in the same region of the network. 9:59:59.000,9:59:59.000 We do expect to have however other diseases that would be in a completely different part of the network. 9:59:59.000,9:59:59.000 And what is crucial here is to understand that the relationship between these two diseases, 9:59:59.000,9:59:59.000 to what degree they overlap, and how they relate to each other is really crucial to understand 9:59:59.000,9:59:59.000 how they relate to each other. 9:59:59.000,9:59:59.000 and whether they are similar or whether they are different from each other. 9:59:59.000,9:59:59.000 So one way to look at it is to let's look at the relationship of all diseases. 9:59:59.000,9:59:59.000 And that's what I'm showing you here. 9:59:59.000,9:59:59.000 Here in the next slide, every node corresponds to a particular disease, 9:59:59.000,9:59:59.000 and two diseases are connected to each other if they share a gene. 9:59:59.000,9:59:59.000 Why would you do that? Because if they share a gene, then very likely their disease module overlaps, 9:59:59.000,9:59:59.000 and therefore they must be in the same region of the network. 9:59:59.000,9:59:59.000 And what is amazing about this map is that there are links between apparently unrelated diseases, 9:59:59.000,9:59:59.000 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, 9:59:59.000,9:59:59.000 different hospitals, different floors. 9:59:59.000,9:59:59.000 But down at the level of the cell, they are not independent of each other. 9:59:59.000,9:59:59.000 They're independent because they're rooted in somewhat the same neighborhood. 9:59:59.000,9:59:59.000 So what this is telling us, this "diseasedom" as I will call it, is that if we want to understand disease, 9:59:59.000,9:59:59.000 we should not be looking really at what we normally look at, but we should be looking at the network within our cells. 9:59:59.000,9:59:59.000 This is the one that really matters. This is the one that really tells us how to classify diseases. 9:59:59.000,9:59:59.000 You know, we probably got it fundamentally wrong. 9:59:59.000,9:59:59.000 It's not heart, it's not brains, it's not kidneys. 9:59:59.000,9:59:59.000 Sooner or later we must abandon this organ-based description of disease and turn to what really matter. 9:59:59.000,9:59:59.000 We should stop training cardiologists and neurologists, and rather the doctor of the future needs to become a bit of networkologist, 9:59:59.000,9:59:59.000 to understand where diseases are lying within that network and how they relate to each other. 9:59:59.000,9:59:59.000 So I personally believe we need a new medicine, to truly execute the paradigm change that genomics allowed us to achieve. 9:59:59.000,9:59:59.000 I would call it network medicine, and I think it's really within our footstep to go out and achieve that. 9:59:59.000,9:59:59.000 I also think that network medicine will not only help us understand the mechanism of disease, 9:59:59.000,9:59:59.000 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. 9:59:59.000,9:59:59.000 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. 9:59:59.000,9:59:59.000 If you think about it, the genomics provides the parts list, 9:59:59.000,9:59:59.000 metabiomics and proteomics provide the diagnostic tools, 9:59:59.000,9:59:59.000 and gene therapies really giving us the way one day to replace the components, with the pieces that are not broken. 9:59:59.000,9:59:59.000 But a car mechanic would be useless without a blueprint. 9:59:59.000,9:59:59.000 And in the same way I believe that to truly understand diseases, we need to give into the hands of our doctors the map. 9:59:59.000,9:59:59.000 Now I'm a physicist, and a network scientist. I am not a medical doctor. 9:59:59.000,9:59:59.000 Hence, I will never cure any of your diseases. 9:59:59.000,9:59:59.000 I can help, however, decipher the map. 9:59:59.000,9:59:59.000 The real book of life, the book that is currently missing most of its pages. 9:59:59.000,9:59:59.000 But once we learn to read it, we'll get much closer to the secret of life and curing disease. 9:59:59.000,9:59:59.000 Thank you very much. 9:59:59.000,9:59:59.000 [APPLAUSE]