What ants teach us about the brain, cancer and the Internet

Ecologist Deborah Gordon studies ants wherever she can find them -- in the desert, in the tropics, in her kitchen ... In this fascinating talk, she explains her obsession with insects most of us would happily swat away without a second thought. She argues that ant life provides a useful model for learning about many other topics, including disease, technology and the human brain.

I study ants

in the desert, in the tropical forest

and in my kitchen,

and in the hills around Silicon Valley where I live.

I've recently realized that ants

are using interactions differently

in different environments,

and that got me thinking
that we could learn from this

about other systems,

like brains and data networks that we engineer,

and even cancer.

So what all these systems have in common

is that there's no central control.

An ant colony consists of sterile female workers --

those are the ants you see walking around —

and then one or more reproductive females

who just lay the eggs.

They don't give any instructions.

Even though they're called queens,

they don't tell anybody what to do.

So in an ant colony, there's no one in charge,

and all systems like this without central control

are regulated using very simple interactions.

Ants interact using smell.

They smell with their antennae,

and they interact with their antennae,

so when one ant touches another with its antennae,

it can tell, for example, if the other ant

is a nestmate

and what task that other ant has been doing.

So here you see a lot of ants moving around

and interacting in a lab arena

that's connected by tubes to two other arenas.

So when one ant meets another,

it doesn't matter which ant it meets,

and they're actually not transmitting

any kind of complicated signal or message.

All that matters to the ant is the rate

at which it meets other ants.

And all of these interactions, taken together,

produce a network.

So this is the network of the ants

that you just saw moving around in the arena,

and it's this constantly shifting network

that produces the behavior of the colony,

like whether all the ants are hiding inside the nest,

or how many are going out to forage.

A brain actually works in the same way,

but what's great about ants is

that you can see the whole network as it happens.

There are more than 12,000 species of ants,

in every conceivable environment,

and they're using interactions differently

to meet different environmental challenges.

So one important environmental challenge

that every system has to deal with

is operating costs, just what it takes

to run the system.

And another environmental challenge is resources,

finding them and collecting them.

In the desert, operating costs are high

because water is scarce,

and the seed-eating ants that I study in the desert

have to spend water to get water.

So an ant outside foraging,

searching for seeds in the hot sun,

just loses water into the air.

But the colony gets its water

by metabolizing the fats out of the seeds

that they eat.

So in this environment, interactions are used

to activate foraging.

An outgoing forager doesn't go out unless

it gets enough interactions with returning foragers,

and what you see are the returning foragers

going into the tunnel, into the nest,

and meeting outgoing foragers on their way out.

This makes sense for the ant colony,

because the more food there is out there,

the more quickly the foragers find it,

the faster they come back,

and the more foragers they send out.

The system works to stay stopped,

unless something positive happens.

So interactions function to activate foragers.

And we've been studying
the evolution of this system.

First of all, there's variation.

It turns out that colonies are different.

On dry days, some colonies forage less,

so colonies are different in how

they manage this trade-off

between spending water to search for seeds

and getting water back in the form of seeds.

And we're trying to understand why

some colonies forage less than others

by thinking about ants as neurons,

using models from neuroscience.

So just as a neuron adds up its stimulation

from other neurons to decide whether to fire,

an ant adds up its stimulation from other ants

to decide whether to forage.

And what we're looking for is whether there might be

small differences among colonies

in how many interactions each ant needs

before it's willing to go out and forage,

because a colony like that would forage less.

And this raises an analogous question about brains.

We talk about the brain,

but of course every brain is slightly different,

and maybe there are some individuals

or some conditions

in which the electrical properties of neurons are such

that they require more stimulus to fire,

and that would lead to differences in brain function.

So in order to ask evolutionary questions,

we need to know about reproductive success.

This is a map of the study site

where I have been tracking this population

of harvester ant colonies for 28 years,

which is about as long as a colony lives.

Each symbol is a colony,

and the size of the symbol is
how many offspring it had,

because we were able to use genetic variation

to match up parent and offspring colonies,

that is, to figure out which colonies

were founded by a daughter queen

produced by which parent colony.

And this was amazing for me, after all these years,

to find out, for example, that colony 154,

whom I've known well for many years,

is a great-grandmother.

Here's her daughter colony,

here's her granddaughter colony,

and these are her great-granddaughter colonies.

And by doing this, I was able to learn

that offspring colonies resemble parent colonies

in their decisions about which days are so hot

that they don't forage,

and the offspring of parent colonies

live so far from each other that the ants never meet,

so the ants of the offspring colony

can't be learning this from the parent colony.

And so our next step is to look

for the genetic variation
underlying this resemblance.

So then I was able to ask, okay, who's doing better?

Over the time of the study,

and especially in the past 10 years,

there's been a very severe and deepening drought

in the Southwestern U.S.,

and it turns out that the
colonies that conserve water,

that stay in when it's really hot outside,

and thus sacrifice getting as much food as possible,

are the ones more likely to have offspring colonies.

So all this time, I thought that colony 154

was a loser, because on really dry days,

there'd be just this trickle of foraging,

while the other colonies were out

foraging, getting lots of food,

but in fact, colony 154 is a huge success.

She's a matriarch.

She's one of the rare great-grandmothers on the site.

To my knowledge, this is the first time

that we've been able to track

the ongoing evolution of collective behavior

in a natural population of animals

and find out what's actually working best.

Now, the Internet uses an algorithm

to regulate the flow of data

that's very similar to the one

that the harvester ants are using to regulate

the flow of foragers.

And guess what we call this analogy?

The anternet is coming.

(Applause)

So data doesn't leave the source computer

unless it gets a signal that there's enough bandwidth

for it to travel on.

In the early days of the Internet,

when operating costs were really high

and it was really important not to lose any data,

then the system was set up for interactions

to activate the flow of data.

It's interesting that the ants are using an algorithm

that's so similar to the one that we recently invented,

but this is only one of a handful of ant algorithms

that we know about,

and ants have had 130 million years

to evolve a lot of good ones,

and I think it's very likely

that some of the other 12,000 species

are going to have interesting algorithms

for data networks

that we haven't even thought of yet.

So what happens when operating costs are low?

Operating costs are low in the tropics,

because it's very humid, and it's easy for the ants

to be outside walking around.

But the ants are so abundant

and diverse in the tropics

that there's a lot of competition.

Whatever resource one species is using,

another species is likely to be using that

at the same time.

So in this environment, interactions are used

in the opposite way.

The system keeps going

unless something negative happens,

and one species that I study makes circuits

in the trees of foraging ants

going from the nest to a food source and back,

just round and round,

unless something negative happens,

like an interaction

with ants of another species.

So here's an example of ant security.

In the middle, there's an ant

plugging the nest entrance with its head

in response to interactions with another species.

Those are the little ones running around

with their abdomens up in the air.

But as soon as the threat is passed,

the entrance is open again,

and maybe there are situations

in computer security

where operating costs are low enough

that we could just block access temporarily

in response to an immediate threat,

and then open it again,

instead of trying to build

a permanent firewall or fortress.

So another environmental challenge

that all systems have to deal with

is resources, finding and collecting them.

And to do this, ants solve the problem

of collective search,

and this is a problem that's of great interest

right now in robotics,

because we've understood that,

rather than sending a single,

sophisticated, expensive robot out

to explore another planet

or to search a burning building,

that instead, it may be more effective

to get a group of cheaper robots

exchanging only minimal information,

and that's the way that ants do it.

So the invasive Argentine ant

makes expandable search networks.

They're good at dealing with the main problem

of collective search,

which is the trade-off between

searching very thoroughly

and covering a lot of ground.

And what they do is,

when there are many ants in a small space,

then each one can search very thoroughly

because there will be another ant nearby

searching over there,

but when there are a few ants

in a large space,

then they need to stretch out their paths

to cover more ground.

I think they use interactions to assess density,

so when they're really crowded,

they meet more often,

and they search more thoroughly.

Different ant species must use different algorithms,

because they've evolved to deal with

different resources,

and it could be really useful to know about this,

and so we recently asked ants

to solve the collective search problem

in the extreme environment

of microgravity

in the International Space Station.

When I first saw this picture, I thought,

Oh no, they've mounted the habitat vertically,

but then I realized that, of course, it doesn't matter.

So the idea here is that the ants

are working so hard to hang on

to the wall or the floor or whatever you call it

that they're less likely to interact,

and so the relationship between

how crowded they are and how often they meet

would be messed up.

We're still analyzing the data.

I don't have the results yet.

But it would be interesting to know

how other species solve this problem

in different environments on Earth,

and so we're setting up a program

to encourage kids around the world

to try this experiment with different species.

It's very simple.

It can be done with cheap materials.

And that way, we could make a global map

of ant collective search algorithms.

And I think it's pretty likely that the invasive species,

the ones that come into our buildings,

are going to be really good at this,

because they're in your kitchen

because they're really good
at finding food and water.

So the most familiar resource for ants

is a picnic,

and this is a clustered resource.

When there's one piece of fruit,

there's likely to be another piece of fruit nearby,

and the ants that specialize on clustered resources

use interactions for recruitment.

So when one ant meets another,

or when it meets a chemical deposited

on the ground by another,

then it changes direction to follow

in the direction of the interaction,

and that's how you get the trail of ants

sharing your picnic.

Now this is a place where I think we might be able

to learn something from ants about cancer.

I mean, first, it's obvious that we could do a lot

to prevent cancer

by not allowing people to spread around

or sell the toxins that promote

the evolution of cancer in our bodies,

but I don't think the ants can help us much with this

because ants never poison their own colonies.

But we might be able to learn something from ants

about treating cancer.

There are many different kinds of cancer.

Each one originates in a particular part of the body,

and then some kinds of cancer will spread

or metastasize to particular other tissues

where they must be getting
resources that they need.

So if you think from the perspective

of early metastatic cancer cells

as they're out searching around

for the resources that they need,

if those resources are clustered,

they're likely to use interactions for recruitment,

and if we can figure out how
cancer cells are recruiting,

then maybe we could set traps

to catch them before they become established.

So ants are using interactions in different ways

in a huge variety of environments,

and we could learn from this

about other systems that operate

without central control.

Using only simple interactions,

ant colonies have been performing

amazing feats for more than 130 million years.

We have a lot to learn from them.

Thank you.

(Applause)