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,