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.

[MUSIC]

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 what 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 actually a number of things that 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 which 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 output 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 humans, us.

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

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

We thought this number could only go up.

It could only just increase.

Yet the reality just 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 should look 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, with 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 large objects:

stars, quasars, or very tiny ones like the Higgs boson or 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 [???] as behind quarks or quasars.

So I started looking at the structure of th 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 it's 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 cit.

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.

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.

[AUDIENCE LAUGHTER]

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

Where Washington brings the 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 now 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 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,