1 99:59:59,999 --> 99:59:59,999 [MUSIC] 2 99:59:59,999 --> 99:59:59,999 So, in many ways a broken car is not so different from a disease, 3 99:59:59,999 --> 99:59:59,999 when the engine is smoking and the lights don't come up. 4 99:59:59,999 --> 99:59:59,999 There's a fundamental difference, however, between humans and cars. 5 99:59:59,999 --> 99:59:59,999 If I can get my car to a mechanic, I can be pretty certain that they can fix it, 6 99:59:59,999 --> 99:59:59,999 which is much more than we can say about many of our diseases today. 7 99:59:59,999 --> 99:59:59,999 So what can a mechanic, with much less education and much less bucks than a doctor, 8 99:59:59,999 --> 99:59:59,999 fix our car, while our doctors often let us go with diseases persisting in our body? 9 99:59:59,999 --> 99:59:59,999 Well, there are actually a number of things that a mechanic has that our doctor doesn't have right now. 10 99:59:59,999 --> 99:59:59,999 First of all, it's got a parts list. 11 99:59:59,999 --> 99:59:59,999 It has a blueprint telling us how the pieces connect together. 12 99:59:59,999 --> 99:59:59,999 It has diagnostics tools to figure out where the components, which is broken and which is healthy. 13 99:59:59,999 --> 99:59:59,999 It has the means, essentially, to replace the parts. 14 99:59:59,999 --> 99:59:59,999 Now let's think about it. Which of these components are available to our doctor today? 15 99:59:59,999 --> 99:59:59,999 Well, the good news is that they've finally got the parts list. 16 99:59:59,999 --> 99:59:59,999 That was the output of the human genome project. 17 99:59:59,999 --> 99:59:59,999 And when the human genome was actually mapped about ten years ago, we thought 18 99:59:59,999 --> 99:59:59,999 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. 19 99:59:59,999 --> 99:59:59,999 But of course reality sinks in. We also realize that these many pieces will eventually give us many drugs. 20 99:59:59,999 --> 99:59:59,999 In 2001, or 2000, the year before the genome project was unveiled, the FDA approved about a hundred drugs a year. 21 99:59:59,999 --> 99:59:59,999 We thought this number could only go up. 22 99:59:59,999 --> 99:59:59,999 It could only just increase. 23 99:59:59,999 --> 99:59:59,999 Yet the reality just sinks in. 24 99:59:59,999 --> 99:59:59,999 The number of new drugs in just the last ten years, went from a hundred before the genome, to about twenty per year. 25 99:59:59,999 --> 99:59:59,999 In hindsight, the reason is pretty clear. 26 99:59:59,999 --> 99:59:59,999 It's not enough to have the parts list. 27 99:59:59,999 --> 99:59:59,999 We also need to actually figure out how the pieces fit together. 28 99:59:59,999 --> 99:59:59,999 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. 29 99:59:59,999 --> 99:59:59,999 How the wiring of ourselves actually look like. 30 99:59:59,999 --> 99:59:59,999 How the genes and the proteins and the metabolites link to each other, forming a conistent network. 31 99:59:59,999 --> 99:59:59,999 Because this network, with I am going to try to tell you today, is really the key to understanding human diseases. 32 99:59:59,999 --> 99:59:59,999 Now, the problem is that if you look at this map, you soon realize that it looks completely random. 33 99:59:59,999 --> 99:59:59,999 Randomness certainly has the upper hand. 34 99:59:59,999 --> 99:59:59,999 But down the line, it is not. I believe there is a deep order behind this wiring diagram. 35 99:59:59,999 --> 99:59:59,999 And understanding that order is the key to understand human diseases. 36 99:59:59,999 --> 99:59:59,999 Now, I am a physicist, and the conventional wisdom is that as a physicist, I should be studying very large objects: 37 99:59:59,999 --> 99:59:59,999 stars, quasars, or very tiny ones like the Higgs boson or quarks. 38 99:59:59,999 --> 99:59:59,999 Yet about a decade ago, my interest has turned to a completely different subject: Complex systems and networks. 39 99:59:59,999 --> 99:59:59,999 And that's because our very existence depends on the successful functioning of systems and networks behind us. 40 99:59:59,999 --> 99:59:59,999 And I also believe the scientific challenges behind complex systems and networks are just as [???] as behind quarks or quasars. 41 99:59:59,999 --> 99:59:59,999 So I started looking at the structure of th Internet. 42 99:59:59,999 --> 99:59:59,999 Telling us how many, many computers are linked together by various cables. 43 99:59:59,999 --> 99:59:59,999 I looked at the structure of the social network, telling us how do societies wire together through many friendship and other linkages. 44 99:59:59,999 --> 99:59:59,999 And eventually I started looking at the structure of the cell. 45 99:59:59,999 --> 99:59:59,999 Telling us you our genes and proteins link to each other into a coherent network. 46 99:59:59,999 --> 99:59:59,999 And through that path, I arrived at human diseases. 47 99:59:59,999 --> 99:59:59,999 A path that is rarely taken by physicists. 48 99:59:59,999 --> 99:59:59,999 Now, the fundamental question that really comes up from that is: 49 99:59:59,999 --> 99:59:59,999 How do we think about diseases in the context of these of these very very complicated networks? 50 99:59:59,999 --> 99:59:59,999 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. 51 99:59:59,999 --> 99:59:59,999 Now, in many ways, Manhattan is structured different from a cell. 52 99:59:59,999 --> 99:59:59,999 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. 53 99:59:59,999 --> 99:59:59,999 Where the intersections showing us nodes are the genes and the proteins. 54 99:59:59,999 --> 99:59:59,999 And the street segments that connect them corresponds to the interactions between them. 55 99:59:59,999 --> 99:59:59,999 Now, down the line, this is not so different from what happens in our cells. 56 99:59:59,999 --> 99:59:59,999 The busy life of Manhattan very easily maps into the crowded life of the cell where molecules interact with each other, 57 99:59:59,999 --> 99:59:59,999 and recombine and transport and so on. 58 99:59:59,999 --> 99:59:59,999 So there's lots of similarities on the surface between them. 59 99:59:59,999 --> 99:59:59,999 And if we look at Manhattan, we also realize that action is not uniformly spread within the cit. 60 99:59:59,999 --> 99:59:59,999 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. 61 99:59:59,999 --> 99:59:59,999 Because that's where most of the theaters are, that's where the shows are. 62 99:59:59,999 --> 99:59:59,999 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. 63 99:59:59,999 --> 99:59:59,999 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. 64 99:59:59,999 --> 99:59:59,999 The same is true in the cell. 65 99:59:59,999 --> 99:59:59,999 Its functions are not spread uniformly within the network. 66 99:59:59,999 --> 99:59:59,999 But there are other pockets within the network that are responsible for particular functions, 67 99:59:59,999 --> 99:59:59,999 and their breakdown potentially leads to disease. 68 99:59:59,999 --> 99:59:59,999 So the way to think about disease in the context of the network is to think that 69 99:59:59,999 --> 99:59:59,999 there are different regions that correspond to different diseases on this map. 70 99:59:59,999 --> 99:59:59,999 So, for example, you could say that cancer stays somewhere around Wall Street 71 99:59:59,999 --> 99:59:59,999 [AUDIENCE LAUGHTER] 72 99:59:59,999 --> 99:59:59,999 And bipolar disease would be somewhere around Times Square. 73 99:59:59,999 --> 99:59:59,999 [AUDIENCE LAUGHTER] 74 99:59:59,999 --> 99:59:59,999 And you know asthma, a respiratory disease, it would be somewhere up near the Washington Bridge. 75 99:59:59,999 --> 99:59:59,999 Where Washington brings the people and cars into New Jersey and The Bronx. 76 99:59:59,999 --> 99:59:59,999 [AUDIENCE LAUGHTER] 77 99:59:59,999 --> 99:59:59,999 Now, under normal conditions 78 99:59:59,999 --> 99:59:59,999 Manhattan is full of traffic. 79 99:59:59,999 --> 99:59:59,999 And that's how the cell looks like normally. 80 99:59:59,999 --> 99:59:59,999 But if we had defects, some genes breaking down, that corresponds to some of the intersections now working, and 81 99:59:59,999 --> 99:59:59,999 soon enough we would get a very typical Manhattan disease: A traffic jam. 82 99:59:59,999 --> 99:59:59,999 This is not so different from what happens in our cells. 83 99:59:59,999 --> 99:59:59,999 Because there are many different ways you can get the same phenotype. 84 99:59:59,999 --> 99:59:59,999 In the same way, there are many different ways you can get a disease. 85 99:59:59,999 --> 99:59:59,999 For example, there was a famous study by Burt [???]'s group which sequenced about 300 individuals who all had colo-rectal cancer. 86 99:59:59,999 --> 99:59:59,999 They had the same phenotype. 87 99:59:59,999 --> 99:59:59,999 Therefore the expectation was that all of them would have probably the same mutations in the same genes. 88 99:59:59,999 --> 99:59:59,999 Yet, the study showed that not only did they not have the same set of mutations, but the mutations were all in different genes. 89 99:59:59,999 --> 99:59:59,999 There were no two individuals who would actually have the same genes exactly the same group of genes' defect. 90 99:59:59,999 --> 99:59:59,999 The only way to understand how it's possible that many different genes broken down in different combinations linked to the same disease, 91 99:59:59,999 --> 99:59:59,999 is to think in terms of Manhattan. 92 99:59:59,999 --> 99:59:59,999 If you think in terms of disease module and to really have the wiring diagram of the disease module, 93 99:59:59,999 --> 99:59:59,999 to understand the breakdown modes of the particular system. 94 99:59:59,999 --> 99:59:59,999 Now, if we really believe that particular picture, the next step for us is to say, how do we proceed from here? 95 99:59:59,999 --> 99:59:59,999 It's very easy. Get the map, get the disease module, and drug it. 96 99:59:59,999 --> 99:59:59,999 Now of course, you do realize there's a catch here. 97 99:59:59,999 --> 99:59:59,999 And the catch of course is, unlike for Manhattan, we don't have yet a map for the cells. 98 99:59:59,999 --> 99:59:59,999 I mean, we do, but some of the maps we have are very incomplete. 99 99:59:59,999 --> 99:59:59,999 For example, the best protein interaction that we have right now, 100 99:59:59,999 --> 99:59:59,999 we believe it has only five percent of the links that are supposed to be in our cells. 101 99:59:59,999 --> 99:59:59,999 Now, having five percent of the links means that we are missing 95% of the links. 102 99:59:59,999 --> 99:59:59,999 And that has dramatic consequences on the system. 103 99:59:59,999 --> 99:59:59,999 Let me illustrate that on Manhattan. 104 99:59:59,999 --> 99:59:59,999 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. 105 99:59:59,999 --> 99:59:59,999 And the consequences are obvious. The network is broken into tiny pieces. 106 99:59:59,999 --> 99:59:59,999 And as a result, the modules, the Wall Street neighborhood and the Times Square neighborhood 107 99:59:59,999 --> 99:59:59,999 that were clearly distinguishable before would be all over the map. 108 99:59:59,999 --> 99:59:59,999 You don't know any more where your disease module is. 109 99:59:59,999 --> 99:59:59,999 So what can we do then? 110 99:59:59,999 --> 99:59:59,999 Well, first and foremost, we must improve on our maps. 111 99:59:59,999 --> 99:59:59,999 And that's what my colleague Mark [???] does at Dana-Farber Cancer Institute. 112 99:59:59,999 --> 99:59:59,999 who in the last twenty years has developed an automatic series of tools to systematically map 113 99:59:59,999 --> 99:59:59,999 the protein interactions within the cell, one of the very important components of the cellular network. 114 99:59:59,999 --> 99:59:59,999 As a result of his work, a few years ago, we got what we call the 5% map, the one I referred to earlier. 115 99:59:59,999 --> 99:59:59,999 This year, he's about to unveil another landmark: the 20% map of the human cell. 116 99:59:59,999 --> 99:59:59,999 And if we left him on the same track, actually he would do the full network. 117 99:59:59,999 --> 99:59:59,999 It may take a decade or two to get to it, but eventually [???] we will get a map. 118 99:59:59,999 --> 99:59:59,999 But what until then? 119 99:59:59,999 --> 99:59:59,999 Shall we just wait for him to finish the work? 120 99:59:59,999 --> 99:59:59,999 And the answer is, well, not really. 121 99:59:59,999 --> 99:59:59,999 There's lots of things we can actually do using the existing maps. 122 99:59:59,999 --> 99:59:59,999 This is how the map looks like right now. 123 99:59:59,999 --> 99:59:59,999 This is all the interactions we believe should be in the cell. 124 99:59:59,999 --> 99:59:59,999 And in order to understand where diseases lie in that, what I'm going to do next is 125 99:59:59,999 --> 99:59:59,999 I will go ahead and place on this map a particular disease, in this case asthma. 126 99:59:59,999 --> 99:59:59,999 Asthma is a respiratory disease that leads to coughing, shortness of breath, and many other symptoms, and 127 99:59:59,999 --> 99:59:59,999 there has been tremendous amount of research on the [???] origins of asthma. 128 99:59:59,999 --> 99:59:59,999 Therefore, we about a hundred genes that are known to be associated with asthma. 129 99:59:59,999 --> 99:59:59,999 So if we put them on the map, and I'm showing them now here as purple notes, 130 99:59:59,999 --> 99:59:59,999 then we would expect them to be all together. 131 99:59:59,999 --> 99:59:59,999 Because they really should correspond to our disease model. 132 99:59:59,999 --> 99:59:59,999 But they're not. They're all over the map. 133 99:59:59,999 --> 99:59:59,999 And the reason they're all over the map is because we're missing 95% of the interactions. 134 99:59:59,999 --> 99:59:59,999 So the missing links that would really hold them all together in one module are all gone, they are not there yet. 135 99:59:59,999 --> 99:59:59,999 So what is it we can do next? 136 99:59:59,999 --> 99:59:59,999 We can use the power of the network. 137 99:59:59,999 --> 99:59:59,999 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. 138 99:59:59,999 --> 99:59:59,999 And that's exactly what we do next. 139 99:59:59,999 --> 99:59:59,999 We took this map and we run algorithm through that, which really extract the information from this map, 140 99:59:59,999 --> 99:59:59,999 and identify what you see in front of your eyes. 141 99:59:59,999 --> 99:59:59,999 The asthma module within the cell. 142 99:59:59,999 --> 99:59:59,999 Now if we know the asthma module, from there we can understand the disease's mechanism, the disease's pathways, 143 99:59:59,999 --> 99:59:59,999 and one day can actually help us understand the drugs. 144 99:59:59,999 --> 99:59:59,999 But this is not only true for asthma. 145 99:59:59,999 --> 99:59:59,999 Not only asthma is located well in the network. 146 99:59:59,999 --> 99:59:59,999 You can take some other diseases, for example COPD, and try to do the same thing. 147 99:59:59,999 --> 99:59:59,999 COPD is often called the smokers' disease, 148 99:59:59,999 --> 99:59:59,999 because smokers are at a very high chance of getting it, 149 99:59:59,999 --> 99:59:59,999 and has somewhat similar symptoms to asthma. 150 99:59:59,999 --> 99:59:59,999 Not surprisingly, it seems to be that the two modules are significantly overlapping, 151 99:59:59,999 --> 99:59:59,999 and are certainly in the same region of the network. 152 99:59:59,999 --> 99:59:59,999 We do expect to have however other diseases that would be in a completely different part of the network. 153 99:59:59,999 --> 99:59:59,999 And what is crucial here is to understand that the relationship between these two diseases, 154 99:59:59,999 --> 99:59:59,999 to what degree they overlap, and how they relate to each other is really crucial to understand 155 99:59:59,999 --> 99:59:59,999 how they relate to each other. 156 99:59:59,999 --> 99:59:59,999 and whether they are similar or whether they are different from each other. 157 99:59:59,999 --> 99:59:59,999 So one way to look at it is to let's look at the relationship of all diseases. 158 99:59:59,999 --> 99:59:59,999 And that's what I'm showing you here. 159 99:59:59,999 --> 99:59:59,999 Here in the next slide, every node corresponds to a particular disease, 160 99:59:59,999 --> 99:59:59,999 and two diseases are connected to each other if they share a gene. 161 99:59:59,999 --> 99:59:59,999 Why would you do that? Because if they share a gene, then very likely their disease module overlaps, 162 99:59:59,999 --> 99:59:59,999 and therefore they must be in the same region of the network. 163 99:59:59,999 --> 99:59:59,999 And what is amazing about this map is that there are links between apparently unrelated diseases, 164 99:59:59,999 --> 99:59:59,999 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, 165 99:59:59,999 --> 99:59:59,999 different hospitals, different floors. 166 99:59:59,999 --> 99:59:59,999 But down at the level of the cell, they are not independent of each other. 167 99:59:59,999 --> 99:59:59,999 They're independent because they're rooted in somewhat the same neighborhood. 168 99:59:59,999 --> 99:59:59,999 So what this is telling us, this "diseasedom" as I will call it, is that if we want to understand disease, 169 99:59:59,999 --> 99:59:59,999 we should not be looking really at what we normally look at, but we should be looking at the network within our cells. 170 99:59:59,999 --> 99:59:59,999 This is the one that really matters. This is the one that really tells us how to classify diseases. 171 99:59:59,999 --> 99:59:59,999 You know, we probably got it fundamentally wrong. 172 99:59:59,999 --> 99:59:59,999 It's not heart, it's not brains, it's not kidneys. 173 99:59:59,999 --> 99:59:59,999 Sooner or later we must abandon this organ-based description of disease and turn to what really matter. 174 99:59:59,999 --> 99:59:59,999 We should stop training cardiologists and neurologists, and rather the doctor of the future needs to become a bit of networkologist, 175 99:59:59,999 --> 99:59:59,999 to understand where diseases are lying within that network and how they relate to each other. 176 99:59:59,999 --> 99:59:59,999 So I personally believe we need a new medicine, to truly execute the paradigm change that genomics allowed us to achieve. 177 99:59:59,999 --> 99:59:59,999 I would call it network medicine, and I think it's really within our footstep to go out and achieve that. 178 99:59:59,999 --> 99:59:59,999 I also think that network medicine will not only help us understand the mechanism of disease, 179 99:59:59,999 --> 99:59:59,999 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. 180 99:59:59,999 --> 99:59:59,999 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. 181 99:59:59,999 --> 99:59:59,999 If you think about it, the genomics provides the parts list, 182 99:59:59,999 --> 99:59:59,999 metabiomics and proteomics provide the diagnostic tools, 183 99:59:59,999 --> 99:59:59,999 and gene therapies really giving us the way one day to replace the components, with the pieces that are not broken. 184 99:59:59,999 --> 99:59:59,999 But a car mechanic would be useless without a blueprint. 185 99:59:59,999 --> 99:59:59,999 And in the same way I believe that to truly understand diseases, we need to give into the hands of our doctors the map. 186 99:59:59,999 --> 99:59:59,999 Now I'm a physicist, and a network scientist. I am not a medical doctor. 187 99:59:59,999 --> 99:59:59,999 Hence, I will never cure any of your diseases. 188 99:59:59,999 --> 99:59:59,999 I can help, however, decipher the map. 189 99:59:59,999 --> 99:59:59,999 The real book of life, the book that is currently missing most of its pages. 190 99:59:59,999 --> 99:59:59,999 But once we learn to read it, we'll get much closer to the secret of life and curing disease. 191 99:59:59,999 --> 99:59:59,999 Thank you very much. 192 99:59:59,999 --> 99:59:59,999 [APPLAUSE]