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.
Ja proučavam mrave u pustinji, u tropskoj šumi i u svojoj kuhinji kao i u brdima oko Silikonske doline, gde živim. Nedavno sam shvatila da mravi različito koriste interakcije u različitim okruženjima, i to me je navelo na razmišljanje da bismo mogli da iz ovoga učimo o drugim sistemima, kao što su mozgovi i mreže podataka kojima upravljamo, pa čak i rak.
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.
Ono što svi ovi sistemi imaju zajedničko jeste to da ne postoji centralna kontrola. Kolonija mrava se sastoji od sterilnih radilica - to su mravi koje viđate kako šetaju okolo - i od jedne ili više reproduktivnih ženki koje samo polažu jaja. One ne daju nikakve instrukcije. Iako se nazivaju kraljicama, one ne govore nikome šta da radi. U koloniji mrava, niko nije nadležan, i svi sistemi bez centralne kontrole kao što je ovaj se regulišu koristeći vrlo jednostavne interakcije. Mravi komuniciraju koristeći miris. Oni mirišu svojim antenama, i komuniciraju svojim antenama, tako da kada jedan mrav dodirne drugog svojim antenama, može zaključiti, na primer, da li je drugi mrav iz iste kolonije i kakav zadatak taj drugi mrav obavlja. Ovde vidite mnogo mrava koji se kreću okolo i komuniciraju u laboratorijskoj areni koja je povezana cevima sa druge dve arene. Dakle, kada jedan mrav sretne drugog, nije bitno kog mrava sretne, i oni zapravo ne šalju nikakav komplikovani signal ili poruku. Sve što je bitno za mrava jeste tempo kojim sreće drugog mrava. I sve ove interakcije, uzete zajedno, stvaraju mrežu. Ovo je mreža mrava koje ste malopre videli kako se kreću po areni, i ova stalno promenljiva mreža je ta koja proizvodi ponašanje kolonije, kao što je to da li su svi mravi skriveni u gnezdu, ili koliko njih izlazi u nabavku hrane. Mozak zapravo radi na isti način, ali ono što je sjajno kod mrava je to što možete videti čitavu mrežu dok se to dešava.
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.
Postoji više od 12 000 vrsta mrava, u svakoj sredini koju možete zamisliti, i različito koriste interakcije kako bi se suočile sa različitim izazovima sredine. Jedan značajan sredinski izazov sa kojim svaki sistem mora da se suoči jesu operativni troškovi, ono što je potrebno za pokretanje sistema. A drugi sredinski izazov su resursi, njihovo pronalaženje i prikupljanje. U pustinji, operativni troškovi su veliki jer nema dovoljno vode, i mravi koje izučavam u pustinji koji jedu semenje moraju da troše vodu da bi došli do vode. Dakle, mrav u potrazi za hranom, tragajući sa semenjem po vrućini, samo gubi vodu u vazduh. Ali kolonija dobija svoju vodu metabolišući masnoće iz semenki koje jedu. Dakle, u ovoj sredini, interakcije se koriste za aktiviranje potrage za hranom. Tragač ne kreće u potragu ako ne dobije dovoljno interakcije sa tragačima koji se vraćaju, i ono što vidite su tragači u povratku koji idu u tunel, u gnezdo, i sreću tragače pri polasku napolje. Ovo ima smisla za koloniju mrava, jer što više hrane ima napolju, tragači će je brže naći, brže će se vratiti, i više će tragača poslati napolje. Sistem radi tako da ostaje zaustavljen, dok se nešto pozitivno ne dogodi.
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.
Tako interakcije funkcionišu da bi aktivirale tragače za hranom. Izučavali smo evoluciju ovog sistema. Najpre, postoje varijacije. Ispostavilo se da su kolonije različite. Sušnim danima, neke kolonije manje tragaju za hranom, dakle, kolonije se razlikuju po tome kako rukovode ovom razmenom trošenja vode da bi se tragalo za semenjem i ponovnog dobijanja vode u obliku semenja. Pokušavamo da razumemo zašto neke kolonije tragaju za hranom manje od drugih razmišljajući o mravima kao o neuronima, koristeći modele iz neurologije. Kao što neuron sabira stimulaciju drugih neurona da bi odlučio da li da se upali, mrav sabira stimulaciju drugih mrava da bi odlučio da li da traga za hranom. A ono što tražimo je da li bi moglo biti malih razlika među kolonijama u pogledu toga koliko je interakcija svakom mravu potrebno pre nego što postane voljan da izađe u potragu za hranom, jer bi takva kolonija manje tragala za hranom.
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.
Ovo pokreće analogno pitanje o mozgovima. Pričamo o mozgu, ali naravno, svaki mozak je nešto drugačiji, i možda ima nekih pojedinaca ili nekih uslova u kojima su elektronska svojstva neurona takva da zahtevaju više podsticaja da bi opalili, a to bi dovelo do razlika u funkciji mozga.
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.
Dakle, da bismo postavili evoluciona pitanja, potrebno je da saznamo o reproduktivnom uspehu. Ovo je mapa mesta istraživanja gde sam pratila ovu populaciju kolonija mrava žetelaca kroz 28 godina, što je otprilike onoliko koliko kolonija živi. Svaki simbol je kolonija, a veličina simbola označava koliko je imala potomaka, jer smo mogli da koristimo genetske varijacije da bismo spojili kolonije roditelja i potomaka, odnosno, da bismo otkrili koje kolonije je osnovala ćerka kraljica i koja roditeljska kolonija ju je stvorila. Ovo je bilo neverovatno za mene, otkriti posle svih tih godina, na primer, da kolonija 154, koju sam dobro znala mnogo godina, je prababa. Ovo je njena ćerka kolonija, ovo je njena unuka kolonija, a ovo su njene praunuke kolonije. Radeći ovo, mogla sam da saznam da kolonije potomaka podsećaju na kolonije roditelja po svojim odlukama o tome koji dani su toliko vreli da neće ići u potragu za hranom, a potomci roditeljskih kolonija žive međusobno toliko daleko da se mravi nikada ne sreću, tako da mravi kolonije potomka ne mogu da ovo nauče od kolonije roditelja. Naš sledeći korak je bio da potražimo genetsku varijaciju koja se nalazi u osnovi ove sličnosti.
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.
Tada sam mogla da pitam, okej, kome ide bolje? Vremenom kroz istraživanje, naročito u poslednjih 10 godina, došlo je do veoma teške i zaoštravajuće suše u jugozapadu SAD-a, i ispostavilo se da kolonije koje čuvaju vodu, koje ostaju unutra kada je zaista vruće napolju, i tako žrtvuju pribavljanje što više moguće hrane, su one koje će verovatnije imati kolonije potomaka. Sve ovo vreme sam mislila da je kolonija 154 gubitnik, jer bi veoma vrelih dana bilo samo mrvica prikupljene hrane, dok su druge kolonije bile napolju u traganju, pribavljajući dosta hrane, ali u stvari, kolonija 154 je veoma uspešna. Ona je matrijarh. Ona je jedna od retkih prababa na lokaciji. Koliko ja znam, ovo je prvi put da smo mogli da pratimo evoluciju kolektivnog ponašanja dok se ona odvija u prirodnoj populaciji životinja i saznamo šta zaista najbolje funkcioniše.
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.
Internet koristi algoritam da bi regulisao protok podataka koji je veoma sličan onome koji mravi žeteoci koriste za regulaciju kretanja tragača za hranom. Pogodite kako zovemo ovu analogiju? Anternet stiže. (Aplauz) Podaci ne napuštaju izvorni računar dok ne dobiju signal da je širina opsega dovoljna da bi putovali. Ranih dana interneta, kada su operativni troškovi bili vrlo visoki i bilo je jako važno ne gubiti nimalo podataka, sistem je bio podešen za interakcije da bi aktivirao protok podataka. Interesantno je da mravi koriste algoritam koji je toliko sličan onome koji smo skoro pronašli, ali je to samo jedan u mnoštvu mravljih algoritama za koje znamo, a mravi su imali 130 miliona godina da razviju dosta onih dobrih, i mislim da je veoma verovatno da će neke od ostalih 12 000 vrsta imati zanimljive algoritme za mreže podataka o kojima mi još nismo ni razmišljali.
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.
Šta se dešava kada su operativni troškovi niski? Operativni troškovi su niski u tropskim krajevima, jer je veoma vlažno, i mravima je lako da budu napolju krećući se unaokolo. Ali mravi su toliko rasprostranjeni i raznovrsni u tropskim krajevima da ima mnogo konkurencije. Koji god resurs jedna vrsta koristi, druga vrsta ga verovatno istovremeno koristi. Stoga se u ovoj sredini interakcije koriste na suprotan način. Sistem nastavlja da radi dok se nešto negativno ne desi, i jedna vrsta koju izučavam pravi kružne putanje u drveću mrava sakupljača idući od gnezda do izvora hrane i nazad, samo ukrug i ukrug, dok se nešto negativno ne desi, kao što je interakcija sa mravima druge vrste. Evo primera mravljeg obezbeđenja. U sredini se nalazi mrav koji zapušava ulaz u gnezdo svojom glavom kao odgovor na interakcije sa drugim vrstama. To su malecki koji trče okolo sa svojim trbusima izbačenim u vazduhu. Ali čim pretnja prođe, ulaz je ponovo otvoren, i možda ima situacija u kompjuterskoj bezbednosti gde su operativni troškovi dovoljno niski da možemo da samo blokiramo pristup privremeno, kao odgovor na trenutnu pretnju, i zatim ga ponovo otvorimo, umesto pokušaja da izgradimo trajni zaštitni zid ili tvrđavu.
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.
Drugi sredinski izazov kojim svi sistemi moraju da se bave jesu resursi, njihovo pronalaženje i prikupljanje. A da bi to činili, mravi rešavaju problem kolektivne potrage, i to je problem koji je sada od velikog značaja u robotici, jer smo shvatili da, umesto slanja jednog, sofisticiranog, skupog robota da istraži drugu planetu ili da pretraži zgradu koja gori, da umesto toga, može biti efektivnije uzeti grupu jeftinijih robota koji će razmenjivati samo minimum informacija, a to je način na koji mravi to čine. Tako invazivni argentinski mrav pravi proširive mreže pretrage. Oni su dobri u bavljenju glavnim problemom kolektivne potrage, a to je kompromis između vrlo temeljnog traganja i pokrivanja mnogo zemlje. A ono što rade jeste, kada ima mnogo mrava na malom prostoru, onda svaki od njih može da traži vrlo temeljno jer će biti drugi mrav u blizini koji traga tamo, ali kada ima malo mrava na velikom prostoru, onda treba da rašire svoje putanje da bi pokrili više terena. Mislim da koriste interakcije da procene gustinu, pa kada je stvarno gužva, češće se sreću, i tragaju temeljnije. Različite vrste mrava moraju koristiti različite algoritme, jer su evoluirali da se bave različitim resursima, i može biti korisno znati, nedavno smo tražili od mrava da reše problem kolektivne potrage u ekstremnoj sredini mikrogravitacije u Međunarodnoj svemirskoj stanici. Kada sam prvi put videla ovu sliku, pomislila sam: O ne, montirali su stanište vertikalno, ali onda sam shvatila da, naravno, to nije bitno. Dakle, ideja je da mravi rade tako vredno da vise na zidu ili podu ili kako god ga nazovete, da je manje verovatno da će komunicirati, i tako bi se poremetio odnos između toga kolika je gužva i koliko se često sreću. Još uvek analiziramo podatke. Još uvek nemam rezultate. Ali bi bilo zanimljivo znati kako druge vrste rešavaju ovaj problem u različitim sredinama na Zemlji, i stoga uspostavljamo program da bismo podstakli decu širom sveta da probaju ovaj eksperiment sa različitim vrstama. Veoma je jednostavno. Može se uraditi sa jeftinim materijalima. I na taj način možemo napraviti globalnu mapu mravljih algoritama kolektivne potrage. I mislim da je prilično verovatno da će invazivne vrste, one koje dolaze u naše zgrade, biti jako dobre u ovome, zato što su u našoj kuhinji jer su jako dobri u pronalaženju vode i hrane.
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.
Najpoznatiji resurs za mrave je piknik, a to je resurs na jednom mestu. Kada je tu jedan komad voća, verovatno će tu biti i drugo parče voća u blizini, i mravi koji su specijalizovani za grupisane resurse koriste interakcije za regrutovanje. Kada jedan mrav sretne drugog, ili kada naiđe na hemijske nanose na zemlji od drugog, onda menja pravac da bi išao u pravcu interakcije, i tako dobijate putanju mrava koji dele vaš piknik.
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.
Ovde mislim da možemo naučiti nešto od mrava o raku. Mislim, prvo, očigledno je da bismo mogli učiniti puno toga da sprečimo rak ne dozvoljavajući ljudima da šire okolo ili prodaju toksine koji podstiču razvoj raka u našim telima, ali ne mislim da nam mravi mogu mnogo pomoći sa ovim jer mravi nikada ne truju sopstvene kolonije. Ali možda možemo naučiti nešto od mrava o lečenju raka. Postoji mnogo različitih vrsta raka. Svaki od njih potiče iz određenog dela tela, i zatim, neke vrste raka će se raširiti ili metastazirati do drugih određenih tkiva gde moraju pribaviti sredstva koja su im potrebna. Ako razmišljate iz perspektive ranih metastazirajućih ćelija raka dok tragaju unaokolo za resursima koji su im potrebni, ako su ti resursi grupisani, verovatno je da će koristiti interakcije za regrutaciju, a ako možemo da otkrijemo kako se ćelije raka okupljaju, onda bismo možda mogli da postavimo zamke da ih uhvatimo pre nego se uspostave.
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.
Dakle, mravi koriste interakcije na različite načine u mnogim različitim sredinama, i možemo naučiti iz ovoga o drugim sistemima koji rade bez centralne kontrole. Koristeći samo proste interakcije, kolonije mrava vrše neverovatne podvige više od 130 miliona godina. Imamo mnogo da naučimo od njih.
Thank you.
Hvala vam.
(Applause)
(Aplauz)