1
00:00:04,000 --> 00:00:26,000
[MUSIC]

2
00:00:26,000 --> 00:00:31,000
So, in many ways a broken car is not so different from a disease,

3
00:00:31,000 --> 00:00:34,000
when the engine is smoking and the lights don't come up.

4
00:00:34,000 --> 00:00:38,000
There's a fundamental difference, however, between humans and cars.

5
00:00:38,000 --> 00:00:44,000
If I can get my car to a mechanic, I can be pretty certain that they can fix it,

6
00:00:44,000 --> 00:00:49,000
which is much more than we can say about many of our diseases today.

7
00:00:49,000 --> 00:00:56,000
So what can a mechanic, with much less education and much less bucks than a doctor,

8
00:00:56,000 --> 00:01:02,000
fix our car, while our doctors often let us go with diseases persisting in our body?

9
00:01:02,000 --> 00:01:08,000
Well, there are a number of things that actually a mechanic has that our doctor doesn't have right now.

10
00:01:08,000 --> 00:01:12,000
First of all, it's got a parts list.

11
00:01:12,000 --> 00:01:16,000
It has a blueprint telling us how the pieces connect together.

12
00:01:16,000 --> 00:01:21,000
It has diagnostics tools to figure out where the components, which is broken and which is healthy.

13
00:01:21,000 --> 00:01:25,000
It has the means, essentially, to replace the parts.

14
00:01:25,000 --> 00:01:29,000
Now let's think about it. Which of these components are available to our doctor today?

15
00:01:29,000 --> 00:01:34,000
Well, the good news is that they've finally got the parts list.

16
00:01:34,000 --> 00:01:38,000
That was the outcome of the human genome project.

17
00:01:38,000 --> 00:01:42,000
And when the human genome was actually mapped about ten years ago, we thought

18
00:01:42,000 --> 00:01:48,000
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.

19
00:01:48,000 --> 00:01:58,000
But of course reality sinks in. We also thought that these many pieces will eventually give us many drugs.

20
00:01:58,000 --> 00:02:05,000
In 2001, or 2000, the year before the genome project was unveiled, the FDA approved about a hundred drugs per year.

21
00:02:05,000 --> 00:02:09,000
We thought this number could only go up.

22
00:02:09,000 --> 00:02:12,000
It could only just increase.

23
00:02:12,000 --> 00:02:14,000
Yet the reality sinks in.

24
00:02:14,000 --> 00:02:19,000
The number of new drugs in just the last ten years, went from a hundred before the genome, to about twenty per year.

25
00:02:19,000 --> 00:02:24,000
In hindsight, the reason is pretty clear.

26
00:02:24,000 --> 00:02:28,000
It's not enough to have the parts list.

27
00:02:28,000 --> 00:02:32,000
We also need to actually figure out how the pieces fit together.

28
00:02:32,000 --> 00:02:40,000
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.

29
00:02:40,000 --> 00:02:44,000
How the wiring of ourselves actually look like.

30
00:02:44,000 --> 00:02:51,000
How the genes and the proteins and the metabolites link to each other, forming a conistent network.

31
00:02:51,000 --> 00:02:58,000
Because this network, with I am going to try to tell you today, is really the key to understanding human diseases.

32
00:02:58,000 --> 00:03:05,000
Now, the problem is that if you look at this map, you soon realize that it looks completely random.

33
00:03:05,000 --> 00:03:08,000
Randomness certainly has the upper hand.

34
00:03:08,000 --> 00:03:13,000
But down the line, it is not. I believe there is a deep order behind this wiring diagram.

35
00:03:13,000 --> 00:03:19,000
And understanding that order is the key to understand human diseases.

36
00:03:19,000 --> 00:03:25,000
Now, I am a physicist, and the conventional wisdom is that as a physicist, I should be studying very large objects:

37
00:03:25,000 --> 00:03:29,000
stars, quasars, or very tiny ones like the Higgs boson or quarks.

38
00:03:29,000 --> 00:03:36,000
Yet about a decade ago, my interest has turned to a completely different subject: Complex systems and networks.

39
00:03:36,000 --> 00:03:44,000
And that's because our very existence depends on the successful functioning of systems and networks behind us.

40
00:03:44,000 --> 00:03:51,000
And I also believe the scientific challenges behind complex systems and networks are just as deep as behind quarks or quasars.

41
00:03:51,000 --> 00:03:58,000
So I started looking at the structure of the Internet.

42
00:03:58,000 --> 00:04:04,000
Telling us how many, many computers are linked together by various cables.

43
00:04:04,000 --> 00:04:14,000
I looked at the structure of the social network, telling us how do societies wire together through many friendship and other linkages.

44
00:04:14,000 --> 00:04:18,000
And eventually I started looking at the structure of the cell.

45
00:04:18,000 --> 00:04:23,000
Telling us you our genes and proteins link to each other into a coherent network.

46
00:04:23,000 --> 00:04:27,000
And through that path, I arrived at human diseases.

47
00:04:27,000 --> 00:04:30,000
A path that is rarely taken by physicists.

48
00:04:30,000 --> 00:04:35,000
Now, the fundamental question that really comes up from that is:

49
00:04:35,000 --> 00:04:43,000
How do we think about diseases in the context of these of these very very complicated networks?

50
00:04:43,000 --> 00:04:51,000
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.

51
00:04:51,000 --> 00:04:57,000
Now, in many ways, Manhattan is structured different from a cell.

52
00:04:57,000 --> 00:05:04,000
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.

53
00:05:04,000 --> 00:05:09,000
Where the intersections showing us nodes are the genes and the proteins.

54
00:05:09,000 --> 00:05:14,000
And the street segments that connect them corresponds to the interactions between them.

55
00:05:14,000 --> 00:05:20,000
Now, down the line, this is not so different from what happens in our cells.

56
00:05:20,000 --> 00:05:27,000
The busy life of Manhattan very easily maps into the crowded life of the cell where molecules interact with each other,

57
00:05:27,000 --> 00:05:30,000
and recombine and transport and so on.

58
00:05:30,000 --> 00:05:35,000
So there's lots of similarities on the surface between them.

59
00:05:35,000 --> 00:05:39,000
And if we look at Manhattan, we also realize that action is not uniformly spread within the city.

60
00:05:39,000 --> 00:05:46,000
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.

61
00:05:46,000 --> 00:05:50,000
Because that's where most of the theaters are, that's where the shows are.

62
00:05:50,000 --> 00:05:57,000
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.

63
00:05:57,000 --> 00:06:04,000
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.

64
00:06:04,000 --> 00:06:06,000
The same is true in the cell.

65
00:06:06,000 --> 00:06:10,000
Its functions are not spread uniformly within the network.

66
00:06:10,000 --> 00:06:14,000
But there are other pockets within the network that are responsible for particular functions,

67
00:06:14,000 --> 00:06:18,000
and their breakdown potentially leads to disease.

68
00:06:18,000 --> 00:06:22,000
So the way to think about disease in the context of the network is to think that

69
00:06:22,000 --> 00:06:26,000
there are different regions that correspond to different diseases on this map.

70
00:06:26,000 --> 00:06:31,000
So, for example, you could say that cancer stays somewhere around Wall Street

71
00:06:31,000 --> 00:06:33,000
[AUDIENCE LAUGHTER]

72
00:06:33,000 --> 00:06:38,000
And bipolar disease would be somewhere around Times Square.

73
00:06:38,000 --> 00:06:39,000
[AUDIENCE LAUGHTER]

74
00:06:39,000 --> 00:06:46,000
And you know asthma, a respiratory disease, it would be somewhere up next to the Washington Bridge.

75
00:06:46,000 --> 00:06:51,000
Where Washington brings the people and cars into New Jersey and The Bronx.

76
00:06:51,000 --> 00:06:52,000
[AUDIENCE LAUGHTER]

77
00:06:52,000 --> 00:06:55,000
Now, under normal conditions

78
00:06:55,000 --> 00:06:58,000
Manhattan is full of traffic.

79
00:06:58,000 --> 00:07:01,000
And that's how the cell looks like normally.

80
00:07:01,000 --> 00:07:07,000
But if we had defects, some genes breaking down, that corresponds to some of the intersections not working, and

81
00:07:07,000 --> 00:07:12,000
soon enough we would get a very typical Manhattan disease: A traffic jam.

82
00:07:12,000 --> 00:07:16,000
This is not so different from what happens in our cells.

83
00:07:16,000 --> 00:07:19,000
Because there are many different ways you can get the same phenotype.

84
00:07:19,000 --> 00:07:24,000
In the same way, there are many different ways you can get a disease.

85
00:07:24,000 --> 00:07:33,000
For example, there was a famous study by Burt [???]'s group which sequenced about 300 individuals who all had colo-rectal cancer.

86
00:07:33,000 --> 00:07:34,000
They had the same phenotype.

87
00:07:34,000 --> 00:07:41,000
Therefore the expectation was that all of them would have probably the same mutations in the same genes.

88
00:07:41,000 --> 00:07:49,000
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.

89
00:07:49,000 --> 00:07:56,000
There were no two individuals who would actually have the same genes exactly the same group of genes' defect.

90
00:07:56,000 --> 00:08:03,000
The only way to understand how it's possible that many different genes broken down in different combinations linked to the same disease,

91
00:08:03,000 --> 00:08:06,000
is to think in terms of Manhattan.

92
00:08:06,000 --> 00:08:12,000
If you think in terms of disease module and to really have the wiring diagram of the disease module,

93
00:08:12,000 --> 00:08:15,000
to understand the breakdown modes of the particular system.

94
00:08:15,000 --> 00:08:26,000
Now, if we really believe that particular picture, the next step for us is to say, how do we proceed from here?

95
00:08:26,000 --> 00:08:31,000
It's very easy. Get the map, get the disease module, and drug it.

96
00:08:31,000 --> 00:08:34,000
Now of course, you do realize there's a catch here.

97
00:08:34,000 --> 00:08:40,000
And the catch of course is, unlike for Manhattan, we don't have yet a map for the cells.

98
00:08:40,000 --> 00:08:46,000
I mean, we do, but some of the maps we have are very incomplete.

99
00:08:46,000 --> 00:08:49,000
For example, the best protein interaction that we have right now,

100
00:08:49,000 --> 00:08:54,000
we believe it has only five percent of the links that are supposed to be in our cells.

101
00:08:54,000 --> 00:09:01,000
Now, having five percent of the links means that we are missing 95% of the links.

102
00:09:01,000 --> 00:09:04,000
And that has dramatic consequences on the system.

103
00:09:04,000 --> 00:09:07,000
Let me illustrate that on Manhattan.

104
00:09:07,000 --> 00:09:14,000
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.

105
00:09:14,000 --> 00:09:20,000
And the consequences are obvious. The network is broken into tiny pieces.

106
00:09:20,000 --> 00:09:25,000
And as a result, the modules, the Wall Street neighborhood and the Times Square neighborhood

107
00:09:25,000 --> 00:09:29,000
that were clearly distinguishable before would be all over the map.

108
00:09:29,000 --> 00:09:32,000
You don't know any more where your disease module is.

109
00:09:32,000 --> 00:09:34,000
So what can we do then?

110
00:09:34,000 --> 00:09:38,000
Well, first and foremost, we must improve on our maps.

111
00:09:38,000 --> 00:09:44,000
And that's what my colleague Mark Vidal does at Dana-Farber Cancer Institute,

112
00:09:44,000 --> 00:09:50,000
who in the last twenty years has developed a whole series of automatic tools to systematically map

113
00:09:50,000 --> 00:09:53,000
the protein interactions within the cell, one of the very important components of the cellular network.

114
00:09:53,000 --> 00:10:02,000
As a result of his work, a few years ago, we got what we call the 5% map, the one I referred to earlier.

115
00:10:02,000 --> 00:10:09,000
This year, he's about to unveil another landmark: the 20% map of the human cell.

116
00:10:09,000 --> 00:10:14,000
And if we left him on the same track, actually he would do the full network.

117
00:10:14,000 --> 00:10:21,000
It may take a decade or two to get to it, but eventually [???] we will get a map.

118
00:10:21,000 --> 00:10:23,000
But what until then?

119
00:10:23,000 --> 00:10:25,000
Shall we just wait for him to finish the work?

120
00:10:25,000 --> 00:10:27,000
And the answer is, well, not really.

121
00:10:27,000 --> 00:10:31,000
There's lots of things we can actually do using the existing maps.

122
00:10:31,000 --> 00:10:33,000
This is how the map looks like right now.

123
00:10:33,000 --> 00:10:36,000
This is all the interactions we believe should be in the cell.

124
00:10:36,000 --> 00:10:41,000
And in order to understand where diseases lie in that, what I'm going to do next is

125
00:10:41,000 --> 00:10:46,000
I will go ahead and place on this map a particular disease, in this case asthma.

126
00:10:46,000 --> 00:10:52,000
Asthma is a respiratory disease that leads to coughing, shortness of breath, and many other symptoms, and

127
00:10:52,000 --> 00:10:57,000
there has been tremendous amount of research on the [???] genetic origins of asthma.

128
00:10:57,000 --> 00:11:01,000
Therefore, we about a hundred genes that are known to be associated with asthma.

129
00:11:01,000 --> 00:11:09,000
So if we put them on the map, and I'm showing them now here as purple nodes,

130
00:11:09,000 --> 00:11:10,000
then we would expect them to be all together.

131
00:11:10,000 --> 00:11:12,000
Because they really should correspond to our disease model.

132
00:11:12,000 --> 00:11:14,000
But they're not. They're all over the map.

133
00:11:14,000 --> 00:11:20,000
And the reason they're all over the map is because we're missing 95% of the interactions.

134
00:11:20,000 --> 00:11:25,000
So the critical links that would really hold them all together in one module are all gone, they are not there yet.

135
00:11:25,000 --> 00:11:29,000
So what is it we can do next?

136
00:11:29,000 --> 00:11:31,000
We can use the power of the network.

137
00:11:31,000 --> 00:11:39,000
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.

138
00:11:39,000 --> 00:11:41,000
And that's exactly what we do next.

139
00:11:41,000 --> 00:11:48,000
We took this map and we run algorithm through that, which really extract the information from this map,

140
00:11:48,000 --> 00:11:50,000
and identify what you see in front of your eyes.

141
00:11:50,000 --> 00:11:53,000
The asthma module within the cell.

142
00:11:53,000 --> 00:12:00,000
Now if we know the asthma module, from there we can understand the disease's mechanism, the disease's pathways,

143
00:12:00,000 --> 00:12:04,000
and one day can actually help us understand the drugs.

144
00:12:04,000 --> 00:12:07,000
But this is not only true for asthma.

145
00:12:07,000 --> 00:12:10,000
Not only asthma is located well in the network.

146
00:12:10,000 --> 00:12:15,000
You can take some other diseases, for example COPD, and try to do the same thing.

147
00:12:15,000 --> 00:12:18,000
COPD is often called the smokers' disease,

148
00:12:18,000 --> 00:12:20,000
because smokers are at a very high chance of getting it,

149
00:12:20,000 --> 00:12:23,000
and has somewhat similar symptoms to asthma.

150
00:12:23,000 --> 00:12:29,000
Not surprisingly, it seems to be that the two modules are significantly overlapping,

151
00:12:29,000 --> 00:12:32,000
and are certainly in the same region of the network.

152
00:12:32,000 --> 00:12:38,000
We do expect, however, to have other diseases that would be in a completely different part of the network.

153
00:12:38,000 --> 00:12:43,000
And what is crucial here is to understand that the relationship between these two diseases,

154
00:12:43,000 --> 00:12:48,000
to what degree they overlap, and how they relate to each other is really crucial to understand

155
00:12:48,000 --> 00:12:52,000
how they relate to each other.

156
00:12:52,000 --> 00:12:55,000
and whether they are similar or whether they are different from each other.

157
00:12:55,000 --> 00:13:01,000
So one way to look at it is to let's look at the relationship of all diseases.

158
00:13:01,000 --> 00:13:03,000
And that's what I'm showing you here.

159
00:13:03,000 --> 00:13:09,000
Here in the next slide, every node corresponds to a particular disease,

160
00:13:09,000 --> 00:13:13,000
and two diseases are connected to each other if they share a gene.

161
00:13:13,000 --> 00:13:18,000
Why would you do that? Because if they share a gene, then very likely their disease module overlaps,

162
00:13:18,000 --> 00:13:22,000
and therefore they must be in the same region of the network.

163
00:13:22,000 --> 00:13:28,000
And what is amazing about this map is that there are links between apparently unrelated diseases,

164
00:13:28,000 --> 00:13:35,000
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,

165
00:13:35,000 --> 00:13:37,000
different hospitals, different floors.

166
00:13:37,000 --> 00:13:41,000
But down at the level of the cell, they are not independent of each other.

167
00:13:41,000 --> 00:13:45,000
They're connected because they're rooted in somewhat the same neighborhood.

168
00:13:45,000 --> 00:13:52,000
So what this is telling us, this "diseasedom" as I will call it, is that if we want to understand disease,

169
00:13:52,000 --> 00:14:01,000
we should not be looking really at what we normally look at, but we should be looking at the network within our cells.

170
00:14:01,000 --> 00:14:10,000
This is the one that really matters. This is the one that really should tell us how to classify diseases.

171
00:14:10,000 --> 00:14:10,000
You know, we probably got it fundamentally wrong.

172
00:14:10,000 --> 00:14:12,000
It's not heart, it's not brains, it's not kidneys.

173
00:14:12,000 --> 00:14:20,000
Sooner or later we must abandon this organ-based description of disease and turn to what really matter.

174
00:14:20,000 --> 00:14:31,000
We should stop training cardiologists and neurologists, and rather the doctor of the future needs to become a bit of networkologist,

175
00:14:31,000 --> 00:14:36,000
to understand where diseases are lying within that network and how they relate to each other.

176
00:14:36,000 --> 00:14:45,000
So I personally believe we need a new medicine, to truly execute the paradigm change that genomics allowed us to achieve.

177
00:14:45,000 --> 00:14:52,000
I would call it network medicine, and I think it's really within our footstep to go out and achieve that.

178
00:14:52,000 --> 00:14:59,000
I also think that network medicine will not only help us understand the mechanism of disease,

179
00:14:59,000 --> 00:15:10,000
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.

180
00:15:10,000 --> 00:15:23,000
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.

181
00:15:23,000 --> 00:15:27,000
If you think about it, the genomics provides the parts list,

182
00:15:27,000 --> 00:15:30,000
metabiomics and proteomics provide the diagnostic tools,

183
00:15:30,000 --> 00:15:37,000
and gene therapies really giving us the way one day to replace the components, with the pieces that are not broken.

184
00:15:37,000 --> 00:15:42,000
But a car mechanic would be useless without a blueprint.

185
00:15:42,000 --> 00:15:49,000
And in the same way I believe that to truly understand diseases, we need to give into the hands of our doctors the map.

186
00:15:49,000 --> 00:15:56,000
Now I'm a physicist, and a network scientist. I am not a medical doctor.

187
00:15:56,000 --> 00:16:00,000
Hence, I will never cure any of your diseases.

188
00:16:00,000 --> 00:16:04,000
I can help, however, decipher the map:

189
00:16:04,000 --> 00:16:09,000
The real book of life, the book that is currently missing most of its pages.

190
00:16:09,000 --> 00:16:16,000
But once we learn to read it, we'll get much closer to the secret of life and curing disease.

191
00:16:16,000 --> 00:16:18,000
Thank you very much.

192
00:16:18,000 --> 99:59:59,999
[APPLAUSE]