The Chemical Basis of Morphogenesis

Hello and welcome to the rest is science. I'm Michael Stevens. And I'm Hannah Frye. And I've got a question for you, Michael. Okay. It's a bit of a, it's a bit of a weird one, right? Good. It's sort of a philosophical question in a way, but maybe also, maybe also deeply scientific. If you start out with an embryo, and it's just this perfectly symmetrical sphere of cells, how does it ever decide where the head goes? Right? Why doesn't it just, well, how does it ever end up with any structure? I mean, when it, when there's already a little bit of structure there, like I get it, maybe there's hormones that come from the head cells that let the neck start forming. But when you're a blastocyst, like just a ball, a symmetric ball of cells, how does it decide? All right, guys, final positions. You're the butt. You guys are the toes. You're going to be the brain get to work. Ready, steady, go. Rich ways up and down. Does it have to do with like local gravity or the parents body? I don't know. How does it seed those? How does it seed those exactly? But then also, I mean, I mean, you said if there's a little bit of structure there, maybe it makes sense. But at the same time, when you look at physics, if you take, I don't know, like a glass of water, for instance, and you put a little drop of ink in it, the process that happens there is diffusion and diffusion is like the destroyer of patterns. You don't get structure from physical processes. So what is it about biology that means that you end up with structure? It's sort of, it's a bit of a puzzle. It's a bit of a puzzle. Well, yeah, it's really blowing my mind because I've seen a blastocyst, a human one through a microscope when it's like eight cells. And you're like, wow, cool. But how in the world does it start assigning roles to each cell? And how do you make sure that the two cells on opposite ends don't both decide that they're going to start forming the brain tube? Right. Am I going to find out today? I think you are. I think you are. There's going to be an answer. There's going to be an answer. Maybe not to your, I mean, look, I'm going to have a slightly simpler animals. I'm not going to, I don't need to get too excited. I've only got the answer for slightly simpler animals. But I am going to talk about how you possibly end up with structure in biology and the answer to this incredibly difficult question, which I mean, was given by a really extraordinary person. This episode is brought to you by Cancer Research UK. Here's something strange. Your DNA contains more ancient viral fragments than genes. The genes that build our cells make up only 2% of our DNA. And for years, that is what scientists focused on. They treated the rest, the ancient viruses and stuff as junk. But now we know that that hidden majority, sometimes called the dark genome influences how our biology works. And how does he like cancer behave? It's a reminder that progress rarely comes as a single breakthrough. It builds gradually. Cancer Research UK plays a central role in that progress, supporting decades of research into over 200 types of cancer work that's helped double survival in the UK over the past 50 years. For more information about Cancer Research UK, their research breakthroughs and how you can support them, visit cancerresearchuk.org forward slash the rest is science. There's a kind of strange hero who enters the story, who ends up actually explaining a lot of destruction of all these not not somebody I think you would immediately expect is Alan Turing, who is best known for inventing the computer for cracking Nazi cryptography. He's the guy who came up with a lot of this stuff. No kidding, because I associate him with like, some sort of steam punky computers, metal circuits, definitely not flesh and and and love. Definitely not flesh and love. You're lucky, lucky wife, Michael. Well, flesh flesh and and even procreation I don't associate much with. No, totally. No, totally. But he was struggling with exactly this question. This is like 1952. Okay, so at this point in time, he is this absolute, you know, Godlike figure in the in the cryptography community in the sort of secret services. But the rest of the world doesn't know who he is. They just think he's this like crackpot old professor up in Manchester. And he's thinking about exactly this question. It's like, how is it possible that diffusion, which is the process that sort of governs how liquidy like things move? How is it possible that when you put that in a biological big, you know, you put that in a biological being, suddenly, there's that it's not following the same rules, you get these patterns, rather than being destroyed by destroyed by diffusion. And he was looking in particular at like the skin of animals. So leopards and spots, cows and their patches. So how does biology end up with these these kind of these features, right, not just a head and a tail, but also front and back and left and right and these complex patterns, you get like zebra stripes and and leopard spots. And then he had this genius idea, he was like, okay, well, what if it's not just one thing that's diffusing? Okay, what if you have two things that are diffusing simultaneously that are fighting against one another? What what would happen then? Okay, so I'm going to give you I'm going to give you a description. I'm going to give you an analogy of what he was describing. You kind of have to go with me a little bit on this analogy. I've got I've got a couple ready for you. But but just go with me on this analogy. So all right, imagine you've got this Petri dish of water. And I'm going to put a drop of ink in there. Normal diffusion, it would just spread out and it would all be gray. Okay, then it would be random. And it would be random. Exactly. But this is special ink. This is like a biological ink. Okay. And so it can copy itself. So ink makes more ink. All right. But the ink is like, it's slow, it's thick, it's gloopy, it diffuses, but it's really going to take its time about it. But the key thing is that it's like, it's making more ink as it goes, right? Like a bacteria would, for instance. Right. Now, if it was in in there on its own, if it was just that ink making more ink, then the whole thing would be black very quickly. But what if instead of just making new bits of ink, this ink also spits out some eraser, eraser, eraser, like eraser. Okay, so this is like a very strange type of ink. As I said, you'd have to go with me. All right. It's very strange to have ink. But it can spit out right with me. Okay, it spits out both versions of itself and the thing that can kill itself. So does this mean like it like cell division, like it splits into more ink and eraser in the same localized point? Yes, think of it that way. Yes, exactly. The same localized point, it gets more ink. So that the amount of total ink increases. But you also get eraser, which can delete the extra ink. Okay, this is like super theoretical. So if it didn't diffuse, if it just sat there and it was spitting out ink and eraser, ink and eraser, ink and eraser, the two would cancel each other out and you would just have this completely clear petri dish. Yeah. But what Jureung was thinking was, okay, well, what if the ink is really thick and gloopy and slow, but the eraser is thin and slippy and can diffuse really quickly. Is there a way that this eraser could diffuse faster than the ink, spread out across the petri dish and then end up creating this little moat around the ink as it forms effectively like, could the ink build its own cage, essentially, right? If that were the case, and he did this all mathematically, right? And he kind of demonstrated that you can have this where there is a moment where there's an equilibrium where the amount of ink being made exactly matches the rate at which the eraser is wiping out the borders. Okay, so it's not like a stalemate. It's not like, oh, it just stops. If you zoomed in, the ink still having like, still dividing into more ink and more eraser, still kind of reacting with what's going on around it, but it's like found this dynamic equilibrium. Now, okay, I accept that is the most mathematically accurate version of what Jureung was thinking of, but I accept it's a bit abstract. So I've got a slightly more human example for you if you like. Okay, well, first of all, tell me when, when in history was Jureung having these thoughts? This is 52, 1952. 1952. So not even that long ago, because I'm imagining these great ideas that someone can just have in an armchair. And I'm thinking of how Einstein did that too. He was like, yeah, what would it be like if I was riding on light, and he's just sitting, you know, in his chair thinking, and here's Jureung going, hmm, zebra stripes, let me think about this. Let me think about let me think about erasing around in a Petri dish. Alright, so very cool. But tell me this like, more human version, more human, biological. Alright, because you do actually get stuff quite often, that can make its own, make copies of itself, make versions of its own self, and also the thing that kills itself. I was going to say, there's got to be chemical reactions that are similar. Well, okay, at the time, nobody thought that they were everyone thought that you don't get, you know, it doesn't make sense with in terms of entropy. But there are analogies. So forest fires is a really good analogy of this. Because if you think about it, when you get a little bit of fire, actually, fire makes more fire. But also, if it's in a forest setting, the more fire there is, the more likely that you are to get the thing that kills the fire, which is helicopters carrying water. Okay, right. So that the existence of something creates more of that same thing, and also the thing that kills it. That's right. So the Incan Eraser analogy, if you imagine that you are looking at a forest, just sort of top down on a forest, okay, for some reason, you get a little bit of fire here and there. And it starts spreading and spreading and spreading. And then you get in the helicopters who can move much faster, the forest forest is spreading, but it's spreading quite slowly. And the helicopters can move much quicker, and they can encircle this forest fire, and basically create a moat around it so that in the end, you have patches of fire that are burning, that are being controlled from the outside by these helicopters, while the fire is burning inside. Yeah, okay, which is which is literally what happens, which is literally what happens fire, perimeters are set up. Yeah, exactly. So but Tyrion didn't have this more concrete analogy because there were helicopters. Well, when was the helicopter invented? Well, Da Vinci came up with a version of the helicopter if you want to go all the way back. Well, when were they used for firefighting? I guess. Yeah, good point. Okay, there were helicopters in Tyrion's time. I'm I don't think he was that I don't think I was going to it. I think he was happy to stay abstract. Yeah, one analogy that people did use around the time around the 50s was of rabbits and foxes. I saw I don't object to this analogy for other reasons, but but mainly because the ink is thick and gloopy and slow, and rabbits are quite fast, okay? Yeah. But imagine for a moment the rabbits are not fast. Okay, I'm doing that. I'm imagining that rabbits are slow. Okay, and rabbits sort of stay near the burrow. Okay, rabbits make more rabbits, but they also allow foxes to exist. Okay, so the more rabbits you get, the more rabbits you get, but also the more foxes you get. So rabbits are effectively in a sense, creating both more of themselves and the thing that kills them. Right. Okay, here is the idea if you can accept that the rabbits might be slow and stay near the burrow, but that the foxes can move around much faster, then what you end up with, what you can end up with is this dynamic equilibrium where you have the number of rabbits that are having more rabbit babies. The rabbit babies are sort of creating this balance with the amount of foxes that are eating them. So you end up with these stable populations, these little pockets of rabbits that are kind of surrounded by foxes. So a little pocket over there and a little pocket over there and a little pocket over there. Okay. Essentially, what you need for this system is called a reaction diffusion system. So diffusion because you've got the foxes or the helicopters or the eraser that's kind of diffusing through the system and the reaction because you've got, you know, the rabbits and the foxes are reacting together, the fire and the water, whatever it might be. But it relies on these two things, the activator, something that makes itself, but it's also slow to diffuse and makes the thing that also kills itself. So Turing was like, Oh, you know what, I reckon, let's just play around with this. Like, let's just see what happens with this. Let's just like write some equations, like see what happens. You know, he came up with this, this, this mathematical system. And it was like, it's sort of like a mathematical version of local love and long distance hate. Okay, so like the inhibitor spreads really fast and quickly. But in a, in a local setting, you can get a cluster where things are quite happy. And he did just scribble this on a chalkboard, by the way, he was like, because he had invented the computer, he's saving very many people, he had access at Manchester University to this really, really crude computer, it's called the Ferranti Mark 1. And so he wrote all of these computer programs to simulate what would happen in this environment, right? If you've got these two different chemicals, or two different processes that are fighting against each other in space. And what you would do is he would start off with like a soup of kind of random noise, like little fluctuations here and there. And then he would watch as the computer spat out what looked like the perfect image of the spots that you get on the leopard, or on the, or the stripes of a zebra, what the computer was spitting out was, I mean, qualitatively, identical to what you end up seeing in animal skin. Okay, wow. Which is funny, right? That it's like he's literally just having this in his brain. So even if you start with this perfectly uniform soup, a kind of gray embryo where nothing interesting is happening, if you just get a tiny little variation, and you've got this process that's sitting there waiting to kick in, during basically proved mathematically that you can get this order from chaos just purely through the laws of physics and mathematics. Okay, wow. He also showed that the geometry makes a difference. So he demonstrated that if you have like a big space, that you've got this process going on in a big space, like the belly of a leopard, for example, then you end up with with spots. And if you make it a much narrower space, then you end up with stripes, like on the tail of a leopard. And if it's too small altogether, you don't get any patterns at all, right? So like on mice, for example, they're usually solid colored, whereas when you have like larger cats, they have like much more. Gosh, that's true. Yeah. Okay, so this is all like, you know, this is all like nice and nice and theoretical. Would you like to know how the biologists reacted to it? I would love to. I mean, poor Turing, right? Like, he's got this unbelievable glory from World War Two, and he can't tell anyone about it. Poor Turing. There's a lot of reasons we could say poor Turing. The guy died in 1954, born in 1912. Right. So I'm listening to this story, this guy uses a computer to simulate the form, the formation of what turns out to look just like the spots and stripes on animals. Why? Because he invented the computer. If he hadn't have died in the 50s, if he lived to be 100, this guy could have been watching chocolate rain on YouTube. Completely. In his one lifetime, he could have seen his invention become what it was by like 2012. Yeah. Yeah, absolutely. He could have been like cracking codes during World War Two with his newfangled computer and then lived long enough to have shared a Kony 2012 meme. What year was he born? 1912? 1912. I mean, if he'd lived to be 100, he would have seen that really famous moment in artificial intelligence where they started categorizing pictures of dogs and cats, which was really, I think that the beginning of the breakthrough of what we've seen what we've seen happen over the last 15 years. I mean, he could he could have been alive to see that. Imagine that. And here's the most unbelievable one. If he'd lived to be 100, he could have watched Vsauce videos. And we all know he would have. We all know he would have. Life is full of tragedy. He would have been there on YouTube saying first. First. And he was first. And he was first. He really, well, love lace, but he really was first. We will maybe do one on that another day. Thing is, right? So meanwhile, during no one knows any of this stuff, no one thinks he's a big deal. He publishes this paper and the biologists are like, yeah, whatever. Are you joking? Well done you with your little maths parlor trick. This is not serious science. Why did they not think it was serious science? Because it was too mathematical? Partly because it was too mathematical. But I think also partly this is about the same time that people are discovering the structure of DNA, you know, this is like the point where biology is is is absolutely obsessed with this idea that there is a genetic blueprint that, you know, for a leopard to have spots, there must be a spot gene that's telling telling the skin when to turn black and when to turn orange. It's like everyone was obsessed with this very mechanical kind of top down view of life. And chewing here is this mathematician, he's got no background in but he's never dissected a frog in his life, you know what I mean? And he barges in and he says, oh, no, it's just a puddle of chemicals. And then fluid dynamics does the rest, you know, it's just diffusion. There's nothing going on. So they were like, you know, this is this is not this is not proper science. This is like looking at a cloud and saying it looks like a dog. Yeah. Oh, for sure. You know what? Like, he was he was saying all of this at the time when, yeah, people, the paradigm was very much about, we're actually more robotic than we think, not just genetically, but even in the mind, right, the behavioralists were the key psychological field that we just learn things and then we're conditioned to behave in certain ways. And that's it. There was no room for creativity. There was no room for even free will, let alone chaos to bring about order. I agree. It was way ahead of his time. This is also in the shadow of World War Two, right? And I think that actually, I mean, we should definitely do some episodes at some point about the kind of the darkness of eugenics and and how people were seeing genetic differences to distinguish between us. But in this aftermath of all of the horror that had happened in the Second World War, scientists were actually sort of very keen to notice the universality of humans, right, that that actually we're all the same. This is sort of a really big trend at the time. Yeah, top down stuff, right? This like this one rule that binds us all the rule of your genes, this rather than there being chaos and messiness that can cut bubble up from from the bottom. In Turing's life as well. I mean, you mentioned that he died in 1954. And this paper he released in 1952, to a third. But what also happened in 1952 was the sequence of events that would lead to his lead to his death. So in January that year, he was kind of finalizing this exact paper, this exact like ink dots paper, he has this little relationship with a 19 year old working class man called Arnold Murray. And shortly after their relationship, Turing's house gets burgled. And Turing reports the crime to the police. And during the investigation, he just casually mentions that the burglar was an acquaintance of Murray, of the guy that he'd been seeing, and he admits to the police that he had been having a sexual relationship with Murray. So he's going to the police for help, right? And the police take this information and turn it against him because yeah, homosexuality was illegal in the UK at this point, literally illegal. Yeah, literally illegal. So it's that the law was gross in decency. And Turing didn't deny it, he doesn't apologize. He is like, I haven't done anything wrong. But he gets convicted in March 1952. His paper is published in August, by the way, the one I'm describing. Wow. And the state give him a choice, they say, okay, well, you can go to prison, or you can just have a year on probation, but on the condition that you undergo hormonal treatment to reduce your libido. But essentially, it's a chemical castration. Yeah, he is injected with synthetic estrogen. And there's this real horrible irony to, to what happens to him, considering the work that he's doing, you know, he's thinking about biology, he's thinking about how you get chemicals that dictate the physical shape and boundaries and the features of a living creature. And he's spending his evenings watching his own physical shape, his own body, be, you know, forcibly rewritten by the chemicals that are injected by the state. Because this, this synthetic estrogen that he's given, it fundamentally changes him, you know, he develops breasts, his weight changes, he gets loads of brain fog, he suffers really severe depression. By 1953, so a year later, his, his, you know, homosexuals are considered a security risk that are susceptible to Soviet blackmail. So his security is revoked. So the thing that, you know, that the world in which he is lauded as a hero has rejected him, that the scientific community aren't interested in any of his new ideas. He's, you know, isolated, he's like surveilled, he's like physically altered. And, you know, less than two years after publishing this paper that I'm describing, while he was working on a second paper on, on, on these biological ideas, he took his own life at the age of 41 by eating an apple that was laced with cyanide. It's such a tragedy. It's like gigantic, gigantic titan of computing and mathematical history that the state just treated so unbelievably badly. And we lost so much because of that. So much of his life. And what he could have done and what he was actively working on. Mm. Completely. Imagine what he could have done in another 40 years. Yeah. I mean, not least on, on the computing stuff, right? The computing stuff that he was right there at the beginning at that people still refer back to, not in some passe way, but that he formed the absolute foundation, you know, universal Turing machines is still the absolute pinnacle of, of what we are looking for and how we consider intelligence to be. That's right. It wasn't just the foundation, but it was. It was also like the beams that were still hanging on. Total Turing tests. And I mean, I knew all of that. I didn't know he'd done work that was so relevant to biology, though. Right. And this is the thing, because while the scientific community just dismissed it as absolute junk, it turns out he wasn't just like, onto something. He was absolutely phenomenally precise. He, in his own mind, managed to absolutely nail the precise mechanism that is going on behind the scenes in biological systems. So it wasn't until 1995, this is like 40 years after his death, okay, that a biologist was looking at the stripes on an angelfish and noticed that they, they, they matched Turing's equations. Okay, but this is like a little bit of a hint, but only really recently, okay, in 2006, have people found for real the ink and the eraser. Okay, we now know that this is like genuinely, genuinely legit. So it's called WNT and DKK. And one of the most famous confirmations of Turing's theory, it's about how mammals grow hair. Okay, so if you look really closely at skin, hair follicles, they're not they're not randomly placed that they're spaced out in this, in this dotted pattern. Okay, like imagine looking down at the forest, you've got these little patches of fire. Okay, imagine looking at like, you know, this landscape of foxes and rabbits, you've got these little pockets of rabbits, you know, and foxes roaming in between them. The activator is this protein called WNT and it's, it tells skin cells, okay, we'll start building a hair follicle here. And WNT is this really heavy, it's this really sticky protein, it doesn't travel very far, it stays really local, and it creates this build up of itself, right? It's got a more the more you get the more you get. The inhibitor is this protein called DKK. And WNT by the way triggers the production of DKK. So it's like it's making its own enemy. Yeah. And DKK tells the skin do not grow hair, right? It sort of says like no more air follicles. And DKK, exactly as during said, is this much smaller, much more mobile protein that diffuses out really quickly, rushes out into the surrounding tissue, much faster than WNT can spread. And so thus, you end up with hair follicles being in these little islands dotted around the landscape. I just have to tell you, right? To prove that this wasn't just a coincidence. Biologists, they decided to like tweak things, they got some mice, and they decided to tweak these two proteins just to see if it would make a difference. And Turing's equations predicted that if you weaken DKK, the inhibitor, then the hair follicles should kind of spread further that the the one that creates the hair follicles should spread further before being stopped. And you should get these hair spots that are much bigger, much more close together, much, much more kind of merged. And so they weakened the inhibitor. And as a result, these mice, they grew these kind of huge, merged clusters of hair follicles, exactly where the math said that they would. And then when they did the flip side, when they engineered mice with like stronger inhibitor, then these were like basically bald mice, right? The hair follicles were shrunk, they were spread much further apart. Like he nailed it. We now know for a fact, okay, and this is like studies that are going on in 2021, that leopard spots is exactly this mechanism, you've got the the WNT DKK4 in the case of of of leopards, it happens during fetal development, you get darker hair density, and then sort of a light hair density, it's happening as the leopard is sort of growing in the womb. We know that on the roof of your mouth, the shape of the ridges and the roof of your mouth is a Turing pattern. Really? I'm feeling them right now with my tongue. You're feeling them right now that it's Turing, Turing a Turing pattern right there. We know fingerprints, by the way, the ridges on your fingers. It's exactly the same thing. It's about week 10 of pregnancy, you get these chemical waves that follow the Turing patterns. I think fingerprints the whole time, by the way. Why are you? As soon as you mentioned that the geneticists were like, no, it's all according to the rules of DNA. I was like, ah, but you know what, not fingerprints, identical twins share DNA, but they do not have the same fingerprints because those are formed in this way that Turing discovered that's much more of an order out of chaos. Exactly. Exactly. If you have a twin, they can bleed to cover up your crimes, but their fingerprints will give it away. You cannot blame them unless you've got their blood. You know, even fingers and toes, right? If you see, if you think of your hand as like, it's almost like a wave of like, yes, no, yes, no, yes, no, yes, no. It's Turing pattern. No kidding. Yes, it's like this wave of proteins that oscillate across your embryonic hand that are telling, yes, they're telling you when to build bone, which is your activator, and when to sort of die off and create gaps between your fingers, which is the inhibitors. It's like, activator finger inhibitor gap. It's like it's all Turing. It's all Turing. They came up with this in his mind. You know, he didn't even do biology. What's sticking with me is that they were able to do this on mice. It's like, okay, we could do math on paper or on a computer and then the screen will output the image, but instead of a screen, they used a mouse's body. They do all the little like math. They create the things and then the mouse is printed out and it matches the equations that they have seeded. Exactly. Exactly. Which is it's it's wild to imagine and it's sort of like it's sort of which way around is it? Is it that your body is doing these equations or is it that the equations are just unreasonably good at describing what your body is doing? It's probably the latter. But but I mean, there are so few of these like by the time the 1950s comes along, you know, physics is sort of like Einstein's been along and done all of the space stuff. You know, chemistry's got all the periodic table. There are so few of these unbelievably beautiful, elegant descriptions of reality that are left to find during had one. He had only done this, he would have been one of the most important scientists of the last of the last century. Well, yeah, because after the the periodic table is filled out and we've got relativity, then yeah, we really moved into order chaos complexity. And that's right where it was. Exactly. So this this idea then of of of how does the egg know what to do? How does the embryo know what to do? This is what it comes down to essentially is that if you have a normal physical system, then you get a tiny fluctuation like a little ripple, then it just diffuses it fades away, the water goes flat again. But when you have a Turing system, a tiny instability, just a random bump of the activator chemical ends up hitting this fault, this positive feedback loop. And because the activator's job is to make more of itself, you know, you end up starting this this process that that that ends up building these biological creatures. And when the sperm pierces the egg, right, it physically snaps the surface tension of the cell. And that is enough of a fluctuation in some creatures to end up dictating the main axis of the creature that the axis of head to butt, essentially. Wow. So there is a worm. We know this humans is a little bit more complicated, right? There's a bit more going on. But there is a worm where just that fluctuation of where the sperm enters is enough to say here's the head, here's the here's the butt, I should tell you the sperm enters. And that's where the butt is. In case you're interested, that's going to be the butt. In the frog, it's the belly where the sperm enters, it's the belly. It's a shame. I looked this up. I was looking this up yesterday. It is a shame because I really wanted it to be that you could say in a human that you know that the sperm from your dad entered and it turned out to be your ear. I really wanted that to be the case for this. Not quite that. No, because human eggs, you can split them and they still, it's not like this half becomes the butt and this becomes the head, you know? Like, I guess that's true. They still have the potential. You couldn't take a human egg, rip it in half, reverse it and make a butt head person. I think that's the plot of human caterpillar, isn't it? No centipede, dammit, I'm going rough. Yeah. And also, it's not the plot of human centipede, but it could be the plot of human centipede four. I have to confess, I've never watched a single one of them, not even a trailer. I watched the first one. Did you? Well, yeah, with the premise that it has, how could you not? You know? I didn't need that mental image in my life, frankly. Hey, there hasn't been a four. I nailed it. You guys! There's been two and three. Quick! Just kidding, I'm a huge human centipede fan and I've watched them all. We own that now. If anyone does make that butt. It's got to be about in in utero, like cell changing in order to create like a donut human, a human centipede with one person. Well, you all do your own butt, like perpetual motion, but... But it sounds like you couldn't create these... You couldn't create the human centipede for people using just genetic changes. You would need to also alter the properties of these chemicals that create the tiering patterns and interact with each other in these ways that produce order. Yeah, I mean, I think that humans are just so much more complicated, right? There's a lot going on when it comes to the sort of complexity of a human. Not for the scientists in our movie. They're going to figure it out. Could you have, instead of human centipedes, could you have would worm centipede be interesting? Or not really. What? Oh, how about this? How about centipede centipede? I would rather watch that, frankly. I wouldn't rather watch that. What is the snake that eats itself the Ouroboros? That's what I'm imagining. If you do happen to be in charge of mega Hollywood budgets, give us a call. You know, we've got, we've got many more ideas where this came from. OK, I think that's gonna, that's a good place for us to take a break. But when we come back, I'm going to be talking about people who have found tiering patterns in other unexpected places and what they have done with them. And I'll be honest with you, it ends up going in quite a dark direction. Oh my goodness. All right, I'll be there. You better be. I'm not doing it on my own. I better be. I'm contractually obligated to be there, but you out there listening, you're in it, you're in it for the kicks. See you after the break. This episode is brought to you by Cancer Research UK. In the UK, nearly one in two people will face cancer in their lifetime. The question is, could science stop cancer before it begins? In over the past 50 years, Cancer Research UK has helped double cancer survival in the UK. And that's proof of what research can achieve, like take cervical cancer. Almost every case is caused by HPV, the human papillomavirus. And when scientists uncovered that link, prevention became possible. Indeed, it did by a vaccine. And it's protection that works way before the cancer itself can actually grow. After the vaccine was introduced, cervical cancer rates in England were nearly 90 percent lower than expected in women in their twenties. I mean, we're now genuinely at a point where this is a disease that is disappearing in young women in the UK. This is something that I really hope my daughters will never have to deal with. For more information about Cancer Research UK, their research, breakthroughs and how you can support them, visit cancerresearchuk.org forward slash rest is science. Ready to launch your business? Get started with the commerce platform made for entrepreneurs. Shopify is specially designed to help you start, run and grow your business with easy customizable themes that let you build your brand. Marketing tools that get your products out there. Integrated shipping solutions that actually save you time from startups to scale ups online, in person and on the go. Shopify is made for entrepreneurs like you. Sign up for your one dollar a month trial at shopify.com slash setup. Welcome back. So far, we have talked about allenturing and the stripes of a zebra, the spots of a leopard and human centipedes, but there's even more, even more directions that this idea has taken us. And Hannah, I want to hear it. If you go back to that analogy of the forest, right? And just seeing that you get these patches of fires, it's a little bit of a challenge. I mean, I think it's a little bit of a challenge. These patches of fires, it's a little like hotspot as it were of fires. For a really long time, you know, probably since the 1970s, people have looked at the mathematics of Turing patterns and been like, wonder if you see that in human systems too? Wonder if you get that in urban settings? And probably I think the clearest example of this, there was a paper that actually wasn't that long ago, 2019. And there was an urban planner called Peter Pells. And he was looking at slums in the global south. OK. And his idea was, look, I don't think these things end up forming randomly. They actually have this really distinctive spatial pattern. And so he applied Turing's equations and then found that you get the same kind of dynamics. Because if you think about it, right, you, why do you end up getting slums in the global stuff where they do? And part of it is because of local attraction. So you get a little bit of a demand for low income workers. So they settle maybe near an industrial area or like a transit hub. And then once they're there, they create a network. And then it means that, you know, they open food stalls, they, you know, offer informal labor, whatever it might be. They sort of pulling other people in towards them. It's local activation, like an attraction for other people. But as you get more people who crowd into that area, then the land becomes really scarce and then the living conditions, you know, maybe get a bit worse because these residents don't necessarily have like mobility. They can't, you know, pick up and move and go somewhere else. You end up with sort of a moat effectively that appears around, around these, these areas. And meanwhile, the wealthy population who can commute, they're kind of pushing back, they're driving up land masses and surrounding rings. So you end up with like a city that organizes itself into a Turing pattern where you get these intense, highly localized clusters of poverty. There's sort of spots, as it were, that are surrounded by these much wider rings of, you know, high income or commercial areas outside of it. That's so fascinating. So the spots on a leopard, the rosettes on a leopard for those pettens out there, they're arranged in the same way that poverty spots the earth. So that's the theory, right? But it's definitely more descriptive. I mean, there's no chemistry going on here. There's no sort of like, there's no. But instead of, instead of atoms, it's people. Where does it possibly fall apart though, this, this similarity? Well, it's quite the question that you've just asked, because some people think that it doesn't or certainly thought that it didn't. I think if you're looking for kind of localized hotspots in an urban environment, then especially some that have a dynamic equilibrium that sort of like stay there over time, then actually crime looks like a really sensible place to look for turing patterns. Because, you know, let's take burglary as an example. You've got burglars who are wandering around the city, who are looking for opportunities, which is like diffusion in a way. It's like the foxes that you had before. You've also got police who are patrolling, trying to prevent crime. So you've got that reaction between the two groups. The difference slightly to other systems is that you know that burglars are also communicating with each other about high value targets. We've known, the criminologists have known this for a really long time, by the way, that point at which you are most likely to be burgled is when you've just been burgled. Okay. Because people replace their valuables, because burglars know the layout of your house, maybe there's something there that they want to come back for. But also often in a street, you get houses that are similar structure to one another. So burglars will repeatedly target the same area until people kind of notice. So you get this like spike, this like this moment of attractiveness of a particular area where burglars are all drawn to that area, the hotspot essentially. So in 2008, there was this group of mathematicians who released this absolutely gorgeous paper. And I've used this paper with my students loads, because it's just like the mathematics in it is really lovely. It's based loosely around the Turing idea of reaction and diffusion. And it's on this idealized street network. It's like it's a grid structure. It's kind of this perfect mathematical model. And they show that if you have this system, this kind of reaction and diffusion between burglars and police, you end up seeing hotspots pop up and disappear in the same way that you do across a real city. It's like you look at it and you're like, oh, that looks a lot like what you see in the city, kind of in the same way that Turing looked at zebra patterns. I was like, well, that's sort of what you end up seeing in real life. Yeah. The thing is in 2008, this was an era when people were very excited about data becoming available. You know, we hadn't really had really, really good data on people before this point. And now you had mobile phones, you had like city reports, you had cameras, etc. And so people were wondering, well, maybe there is a way that you can do physics with people. Maybe there is a way that there are underlying equations that dictate how people moving. Sure. Yeah. Model them as a bottled gas. Exactly. Let's see what happens. Exactly. And the police agreed that maybe this was something that could work. So the Los Angeles police department, they looked at this paper and they were like, oh, there might be something in this. Maybe, maybe maths can tell us where these hotspots are going to be next, you know, not just where they are. Now we're predicting crime. Now we're predicting crime. Future crime. Future crime. Or at the very least, how are these hotspots moving and changing and how might they move in future? So one of the people on that original paper, this guy called Jeffrey Brantigam, he took this, this like what was a really beautiful theoretical paper and turned it into a company which was called Predpol, predictive policing. And then in the early 2010s, what they would do is they were working with police officers in LA and they would print out these maps at the beginning of their shifts and the maps had like a little 500 by 500 foot boxes on them. And that box would say, this is where there's likely to be crime this evening. This is where we expect crime to be. Right. The maths by this point had like, I mean, I just want to make sure that I'm really accurate. This is like, it's moved on a couple of steps from Turing's reaction diffusion in order to get it to work for that setting. But that like right at the heart of it, then kind of the nugget of the idea is the same thing that you have these two systems that are diffusing across the space. And I have to say, right, they did a randomized control trial on this in Kent in the UK. And it's the closest that anyone had been to like double checking if it worked. And to be honest, it actually did work. It did work. This Kent study. But it didn't work in Los Angeles. Well, they didn't do a proper test of it in Los Angeles or at the very least they didn't publish a proper test of it in Los Angeles. OK. But in Kent they did, they published a proper trial of it. So they had, and it was double blind as well. So they had two teams, two sets of patrol maps. One of them was like professional crime analysts who were doing humans using their intuition and the other was the was this this program, this software. And what they did, the algorithm one, basically, the algorithm was ten times more accurate, predicting the exact 500 square foot box. Where crime would occur. And Kent, they reported an 8.5 percent drop in street crime during this trial period. I'm assuming what you do is you look at this grid that the software has filled in with potential hotspots tonight and you put officers there. Exactly. Or cameras, you know, here in the States, we've got these big, like portable poles with blinking blue lights covered in cameras that just tell you we're watching here. I mean, it could get really complicated because if you dissuade the criminals from where they would have been operating that night, they're just going to go somewhere else that same night. You have very smartly landed on the real vulnerability of this. Because when you're observing leopard spots or retrospectively looking at where slums have appeared in the global south, you are not in there interfering with the system as it happens. The real problem with PredPol was that if you are sending cars into a particular neighborhood and you're saying this is where crime is going to occur, you've got people in those cars, right? You've got like real police, real humans. And if they are expecting to find crime in a particular area of the city, they're going to find crime. They're going to find it. Yeah. And maybe it's not the crime that the algorithm was talking about. Maybe it's not burglary, but maybe it's, I don't know, like even someone jaywalking, whatever it might be. But then the issue also is that the algorithm relies on knowing where the crime was already. Right. And so the more crime that you find, the more crime you're putting into the system, the more that the algorithm thinks that that place is already a hotspot, the more it's going to send police back into that area. And the more and more and more you end up finding more crimes. And it will not surprise you when I tell you that in LA in particular, the neighborhoods which were disproportionately flagged as hotspots by this algorithm were neighborhoods that disproportionately contained African American residents. And so what ended up happening essentially is that this opened the door to what was automated harassment of particular communities. Right. There's a big difference between watching how spots form or hair follicles on a mouse versus actually putting law enforcement officers in a particular part of the city where they're looking for and interacting with the very chemical reaction you're trying to predict. They're going to, they're going to mess up the results by being there and noticing, oh, I saw some jaywalking. I saw a car without plates. Things that normally would not have been noticed, wouldn't have been reported, are now suddenly getting fed into the algorithm. And that's changing up not only what they think will happen, but how they're treating everyone who lives in this whole city. Right. And this, and thus you come to a kind of a conundrum because actually I have to confess that I sort of have like a bit of a, a bit of a connection to this type of work. Right. So I have published papers on mathematics of burglary. I have like these mathematicians who've published that initial paper. You know, I said that I've like used it for my students. I've like met and worked with them. And in the early days, I, especially after the Kent randomized control trial, it was like, you know, I, I really didn't immediately see what the problem was. I didn't immediately see the potential ethical concerns of this stuff because here is the conundrum. You can, to a certain extent, better than random chance, predict where crime is going to happen. You can, right? Right. The algorithms work. The problem is what on earth do you do with that information? Yes. What do you do with it? Because a crime that might happen isn't a crime yet. If you through surveillance and presence cause it to not happen, then what does the algorithm do for the next night? You're dealing with probabilities here, you know? Yeah. I mean, in the same in biology, the same in forest fires, the same in all of this, you're not saying 100% definitely this area is going to be part of the hot spot or not. You are handling with uncertainty front and center. And so it's one thing to say this area, this individual, this group of people are going to be implicated in a crime in future. It's another thing altogether to say, maybe they will, maybe they won't be should we or should we not intervene? Right. I should tell you now the way that the UK have dealt with this. I imagine it sounds like it's something similar in the US is that if there has been a burglary in your neighborhood and your chances of being burgled have increased, they will put a lethal through your door saying there is a, an increased likelihood that, that your house may be targeted, make sure that you keep your security up. And that does that again has had a randomized control trial and that again has ended up with like a, with a really positive impact. But it's interesting anyway. I just, this is one of those areas where I feel like I was really there as it was all going on with the academics as people were trying to apply mathematics to areas of policing and really there when the backlash and the knotty repercussions of it came through. I mean, I should also say one of the things about Preble, the reason why it was dropped in particular by the LAPD was that actually internal reports said it just didn't work. It just didn't work very well. Well, part of it could be the name too. Predpole, it sounds like a dark organization in a movie. You know, it's a little bit too powerful sounding. It does a little bit. It's a little bit minority report, isn't it? Yes. Yeah. Not that the name was the biggest problem, but hindsight being 2020, you can go, you know what? If I was a screenplay writer, I would call the organization that doesn't quite work right, Predpole. I mean, absolutely. Has anyone thought of calling it bread bowl? Because that sounds delicious. See, this is this is the kind of commentary I'm here to add, Hannah. And I appreciate every moment. I think there's a better bread bowl joke there than what I came up with. But, you know, you guys in the comments can give it a better setup. This is fascinating. It's so it's so amazing that you worked on this mathematics and worked with the mathematicians. So what's the state of this field now? I have to be honest with you. I mean, there's like, there's like seven different directions I could go in with this and I was thinking about this last night and I still haven't made up my mind, which I probably should have done in advance of this conversation. There are all sorts of examples of this, right? The Birdry one is not alone. The one piece of work that I particularly did, actually, that really got me out of this whole space was I was working with the police in, this is 2011 and there had been these big riots across the UK, really, really out of control, right? Things there was looting, there was arson, there was all kinds of assaults. It was really the police were very shocked by how quickly things had descended, how out of hand things had got over the course of five days. This is when I was, you know, just finished my PhD. This is like the first thing that I was working on this collaboration with the police and what they did was they collected all the data of everybody who'd been arrested in connection with these riots and they handed it over to this group of mathematicians and criminologists and said, okay, see what patterns you can find, what we did and didn't do and what we could have done differently so that things didn't get quite so out of hand. In the UK, our police are not perfect by any stretch of the imagination, but I get the impression that we have a slightly better relationship with them than perhaps some people do in the States, right? There's sort of more, you get, there's more of a kind of feeling of community. Well, yours are called Bobbies. They're called Bobbies, yeah. They're not without flaws. It's important for me to say that, but on the whole, they tend to be, you know, they do a lot of really good work. So we did that. We published this paper and as part of the paper, as well as all of the analysis that we did, we kind of constructed this algorithm that, I mean, it was very, very crude, right? Very, very proof of concept. But the idea was that you could look at it if something like this happened again in future so that police could bring about a swifter resolution to unrest. Anyway, a few years later, we published this paper, the academic community were like, great, you know, this is like 2013, something like this. A couple of years later, I went off to go and give a talk about this in Berlin. And I was standing on stage at like this, this audience, which involved lots of the public. And I was giving this really enthusiastic presentation about how great it was that we now had all of these tools, right? Now we were in a situation, I think I was very naive at the time, I got to be honest with you. But we were now in a situation where, you know, we could support the police, control the city's worth of people, essentially, right? I mean, I was very young and very naive. And I think it just genuinely didn't occur to me that if there's one city in the world where people are a bit scared about the police having too much power and control, it's probably going to be Berlin. Yeah. Yeah. So what was their reaction? I mean, they were not happy. There was like, they tore me apart in the Q&A, which I mean, actually, it was a really important moment for me, like a really, really significant moment for me in my career. Because the thing is up until that point, when you are a mathematician, right? When you are a physicist or whatever, and you're coming up with these like mathematical ideas, you don't have to worry about the sort of, Turing wasn't worried about the ethics of changing molecules in mice's skin, you know? No. He wasn't sort of like thinking about the moral implications of like testing the fingerprints of twins, you know? He was just like having fun with his equations, right? Yeah. And I think that that was the moment when I really realized that a lot of the people who are designing our collective future, a lot of people who are working in artificial intelligence, who are working with algorithms, who are working with data, they've gone through with a very technical training that hasn't said to them, you need to be careful in what you're doing. You need to think about the wider implications of it. You need to not see this stuff as though it's just like a cute little mathematical model that kind of sits on a shelf and you can stand back and like admire it as though it exists in isolation of the world around it. You have to think very deeply about the way your stuff can be used and the impact that it will have on the world. And so that really was the moment when I switched course, I started writing about the ethics of algorithms, an incredible amount of work ever since. I think it really accelerated my work in public communication of science and talking about human issues as well as just technical ones. That's so cool to hear because it's so true, isn't it? I was thinking, as we talked about Turing, that he wound up being punished by being injected with all these chemicals because he was homosexual. And yet to prove his work right, we had to pump a bunch of rats full of changed chemicals in order to figure out how their hair would grow. Not because they were homosexual, but because they were not humans. That was their punishment for just being a mouse that we could test on them. The way that in a way Turing was tested on. But of course, what's the alternative that we don't test any of this at all? Because from this research, so much good can come too. So so much hinges upon the responsibility of those who pay attention to the ethics of what they're doing. We need the knowledge and we shouldn't stop gaining the knowledge. But there's a different thing called wisdom that we need even more. And that's how you use the knowledge. Yeah, because I'm also sitting here thinking, well, gosh, now that computing power is just more and more democratized every day, I should start doing this predpulse stuff. But in reverse, where should I be committing my crimes? Where should I hide a body based on the expected intuitions and search patterns of the authorities? Right. I could turn the tables right on them. It's a game of cat and mouse. Is this is this body going to be a head stitched to about Michael? Because I think that's going to be a giveaway telltale that it was you. All right. When they do finally find it. Hannah, I'm not going to snitch on myself, but the point is that math and science can help us do good and they can also help us do anti good. Yeah, absolutely. And it's still such a such a living conversation. Absolutely. Absolutely. And I don't think it's going to be one with a finish line. I don't think this is a finish line we get to cross. No, no, it isn't. It's a moving finish line. Let's see how far can we push this analogy? Like the muscles we're running with our knowledge, but the the path we take is wisdom and the finish line doesn't exist. It's is it a loop? Is it a spiral? Wow. See, we'll do we'll do an episode. Is it a human stitched into a torus? Is it a human stitched into a torus creating its own individual human centipede? Why do we always come back to human centipede? It's the ultimate circle, Michael. It's the ultimate circle. It's the circle of life, the circle of one life. I think we should leave it there to you. I think we should leave it there. Yes. Thank you all for listening, Hannah. And thank you. I I loved hearing all of this. If you're out there and you've got some questions you want us to answer from yourself, we do that every Thursday on Field Notes. And you can send your questions to the rest is science at goalhanger.com. Thank you so much for watching, listening to us. If you are following us on YouTube, please do write comments below. We read all of them, maybe not all of them, sometimes quite a lot, but a lot of them, especially the ones that say first. Likewise on Spotify. If you want to leave us any comments or send us any emails, we'd love to hear from you. Thank you very much. See you next time. See you next time.