Albert-László Barabási at TEDMED 2012

Networks guru and author Albert-László Barabási says diseases are the results of system breakdowns within the body, and mapping intracellular protein networks will help us discover cures.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

That was the outcome of the Human Genome Project.

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

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.

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

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

We thought this number could only go up.

It could only just increase.

Yet the reality sinks in.

The number of new drugs in just the last ten years, went from a hundred before the genome, to about twenty per year.

In hindsight, the reason is pretty clear.

It's not enough to have the parts list.

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

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.

How the wiring of ourselves actually look like.

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

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

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

Randomness certainly has the upper hand.

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

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

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

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

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

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

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

So I started looking at the structure of the Internet.

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

I looked at the structure of the social network, telling us how do societies wire together through many friendship and other linkages.

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

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

And through that path, I arrived at human diseases.

A path that is rarely taken by physicists.

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

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

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.

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

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.

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

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

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

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

and recombine and transport and so on.

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

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

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.

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

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.

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.

The same is true in the cell.

Its functions are not spread uniformly within the network.

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

and their breakdown potentially leads to disease.

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

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

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

[AUDIENCE LAUGHTER]

And bipolar disease would be somewhere around Times Square.

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

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

[AUDIENCE LAUGHTER]

Now, under normal conditions

Manhattan is full of traffic.

And that's how the cell looks like normally.

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

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

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

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

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

For example, there was a famous study by Burt [???]'s group which sequenced about 300 individuals who all had colo-rectal cancer.

They had the same phenotype.

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

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.

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

The only way to understand how it's possible that many different genes broken down in different combinations linked to the same disease,

is to think in terms of Manhattan.

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

to understand the breakdown modes of the particular system.

Now, if we really believe that particular picture, the next step for us is to say, how do we proceed from here?

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

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

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

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

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

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

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

And that has dramatic consequences on the system.

Let me illustrate that on Manhattan.

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.

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

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

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

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

So what can we do then?

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

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

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

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

As a result of his work, a few years ago, we got what we call the 5% map, the one I referred to earlier.

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

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

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

But what until then?

Shall we just wait for him to finish the work?

And the answer is, well, not really.

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

This is how the map looks like right now.

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

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

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

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

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

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

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

then we would expect them to be all together.

Because they really should correspond to our disease model.

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

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

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

So what is it we can do next?

We can use the power of the network.

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.

And that's exactly what we did next.

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

and identify what you see in front of your eyes.

The asthma module within the cell.

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

and one day can actually help us understand the drugs.

But this is not only true for asthma.

Not only asthma is located well in the network.

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

COPD is often called the smokers' disease,

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

and has somewhat similar symptoms to asthma.

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

and are certainly in the same region of the network.

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

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

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

how they relate to each other.

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

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

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

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

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

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

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

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

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,

different hospitals, different floors.

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

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

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

we should not be looking really at what we normally look at, but we should be looking at the network within our cells.

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

You know, we probably got it fundamentally wrong.

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

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

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

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

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

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

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

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.

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.

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

metabiomics and proteomics provide the diagnostic tools,

and gene therapy is really giving us the way one day to replace the components, with the pieces that are not broken.

But a car mechanic would be useless without a blueprint.

And in the same way I believe that to truly understand diseases, we need to give into the hands of our doctors the map.

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

Hence, I will never cure any of your diseases.

I can help, however, decipher the map:

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

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

Thank you very much.

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