Distinguished Seminar in Robotics, Systems & Control: From Swarm of Flying Robotics to Nanomedicine
CC Attribution - NonCommercial - NoDerivatives 2.5 Switzerland:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Hauert, Sabine
Nanoparticles for cancer applications are increasingly able to move, sense, and interact the body in a controlled fashion, an affordance that has led them to be called robots. The challenge is to discover how trillions of nanobots can work together to improve the detection and treatment of tumours. Towards this end, the field of swarm robotics offers tools and techniques for controlling large numbers of robots with limited capabilities. Our swarm strategies are designed in realistic simulators using bio-inspiration, machine learning, and crowdsourcing. Strategies are then translated to 1000-robot swarms or preliminary tissue-on-a-chip devices.
 Well, I really want to thank Walter for inviting me over here. It is nice to cross paths again, and it's been a wonderful day visiting all the labs that I admire and that are extremely relevant to the work that I'm doing. So I look forward to working more with ETH in the future. So my talk is going to be about going from swarms of flying robots to nanomedicine. Swarming is a little bit of the key word here. I work at the Bristol Robotics Laboratory, which is a joint venture between the University of Bristol and the University of West of England. And we're over in Bristol, UK. There's actually a direct flight with EasyJet, so it's really easy to come here. So I'm really interested in swarm systems. So if you look at these flocks of birds, they're able to do these beautiful complex behaviors. And there's a lot of features that are interesting for real world applications. So for example, you can keep adding birds to the flock, and the flock is going to continue to fly, so it's scalable. If a bird falls from the sky, the whole flock doesn't fall to the ground, so they're very robust. And together they can do more than the individual birds. For example, they're much better at avoiding predators than the individual birds. And so we want to take inspiration from these swarm systems and try to see if we can use these principles for real world applications. And there's really a puzzle behind that. And that's, you look at this flock of birds, but if you take a bird from the sky and look at it, there's no way you're going to understand how they flock. And so the puzzle is really, you have this swarm behavior, a group behavior, and how should you design the individual agent so that you get what you want to get for the collective. And so I've sort of been studying this question for a while, and the first thing that we tackled over in Dario Floriano's Labrì Piafell was to see if we could use swarms of flying robots to create communication networks in disaster areas. And so the idea was you would arrive on a disaster site and you'd have these rescuers and they should be able to throw these flying robots. And these flying robots should organize over the area so that this rescuer can communicate throughout the network to another rescuer to a victim in the environment. And so to look at this, we were using these simple flying robots that were fixed link platforms. There's just a propeller at the back there, fully autonomous. And we were assuming that our robots were quite simple. They could just talk to their neighbors using wireless communication and they had a compass. And so using just those basic signals, could we make them do something interesting from an application perspective? And that puzzle again was we wanted them to create communication networks, but how do you design the controller for the individual flying robots? And so one way we tackled this was to see if we could come up with those controllers automatically. And we were using a thing called artificial evolution to basically come up with the weights of an artificial neural network here. So in the input, you just had a compass in communication, so who you're talking with in your neighborhood. And as an output, all we were acting on was how much should you turn. And that makes sense if you have a fixed wing, because you don't want to be stopping them, because otherwise they'd fall from the sky. So we were just keeping constant speed and changing the way these robots turn. And so we ran our artificial evolution, which was to come up automatically with these controllers. And this is what it came up with. You have these two rescuers in simulation that have a communication range, that's the blue disk. And somehow we want to create a bridge between these two. And we're launching these 20 robots. And you can see what evolution came up with was to create these chains, which makes sense because communication is a rubber band. And so it's easy to kind of go away and come back if you break the connection. And then they synchronize, go from left to right, find the second rescuer, and then maintain that connection. So we found all those things through the evolution. And then we learned from it and then implemented it on the flying robots and threw those in the air. So here we have 10 flying robots. This is Sivrin who built these robots. And I'm basically behind the camera. And we could throw them, they were fully autonomous. They'd stay up in the air until we started our experiments. And they're about bird size. So the birds, sometimes you spot them following the flying robots. And we often wondered, actually if you can spot them, let me know. I've left this up quite a long time. But this is just 10 of them waiting for the experiment to start. And then what you're going to see are some of the GPS trajectories of our experiments. The first one is just going to be this communication bridge. So essentially launching robots, they grow a bridge, and then they connect these two rescuers. And so there you see sort of the GPS trajectories once they've created those bridges. And here you can see we're looking at pretty big areas around a kilometer. And here what you see is something else we learned from evolution, which is the synchronization. And here all the robots are moving a little bit like they're beating with the same heading at the same time. And then that was the evolutionary part. But there's also a different way in which we knew how to design swarms. And that was to take inspiration from nature. And so biologists have been studying different algorithms that can give you swarm-like behaviors. And one of them was flocking. And so this is just 10 robots flocking. These are actual GPS trajectories. And it starts very messy. But over time, they converge towards a circle. There you have your circle. And that's what we had predicted, because one of the key things with all of the experiments you're seeing is we did a lot of simulations, understood how we thought it was going to work in reality through the robots in the air, and then tried to see if we could actually get that group behavior that we wanted in reality. So that was a fun PhD. But it was really just 10 flying robots. And I always thought that swarms needed to work in the hundreds and the thousands and the millions. And so I went out looking for a platform that had those many zeros after the one. And that's around the time that I got interested in nanomedicine. So nanoparticles are very interesting because of their size in the nanometer range. And because of their size, they're very good at leaking into tumor environments. So here is an animation of a vessel. And vessels and tumors are very leaky. And these nanoparticles are just the right size that they're going to leak out of the vessels and tumors. And then they're going to arrive in this tumor tissue, which is a high-pressure environment. So they kind of diffuse throughout this tumor tissue. And these nanoparticles, they work in the 10 to the power 13. So yay, that was the number that I was looking for. And not only that, but they have interesting features. So you can change their size. You can change their shape. You can change their charge. That's going to change what they stick to in the environment. You can, some of them are energy receptive. So you can activate them using magnetic fields or using light. And some of these particles, you can decorate them with little molecules on their outside that allow them to, for example, bind to receptors on a cancer cell. And you can load them with drugs that you can release in a more or less controlled fashion. And so there's a lot of knobs that you can change on the design of these nanoparticles, a lot of different individual functionalities. And actually, if you go into the literature, we're starting, we're just starting to understand how really the design of a particle, in terms of its size and shape and charge and material and surface and cargo, impacts the way these particles move in a tumor environment. And one easy one is if you have a smaller particle, that's going to make the particle diffuse faster within a tumor tissue. So it's really just one of those parameters. And so the question is, well, how would you make nanoparticles swarm? Well the birds, maybe they had their brain that was telling them how to behave with respect to the neighboring birds. With the flying robot, we had a computer, and so we just had a program on board, each one of these flying robots that allowed them to do these flocking behaviors. And with the nanoparticles, we don't have either a brain or computational power, really. And so what we're doing is putting the intelligence and the design of the bodies of these nanoparticles. So how should you turn the knobs of these particles so that when you inject them, they behave as a collective in the way that you want them to behave? And the reason we'd want to do that is that these are really the key words in red from the swarm literature. So bees, for example, are very good at making decisions about a new nest. What if you could use those principles to make nanoparticles, that, you know, make a decision about the state of the tumor? What if you could have nanoparticles that synchronize the delivery of a drug, a little bit like bacteria, when they do their quorum sensing? What if you could do map mapping, you know, the ants that are creating those picnic, those trails to your picnic table? What if you could use that within a tumor environment to do interesting mapping and amplification? And it's not a crazy idea. So this is work, basically, once I got excited in nanomedicine, I went to a lab over in Boston, San Gita Bacchia's laboratory, and she was working in a wet lab environment, making these nanoparticles and testing them on living systems to see if they had a clinical impact. And so this is one example of a system that they designed and tested in a living system. Here you have these blue particles, which are made out of gold, and they arrive through the vessels, they leak out from the vessels into the tumor environment, and these nanoparticles, because they're made out of gold, you can shine near and for red light on them, and that causes them to vibrate, and because they vibrate, they heat up, and then they cause damage to the tumor tissue. So you're essentially causing damage to the tumor, and then they send in the second wave of nanoparticles, the pink ones, and these particles also leak out, but on their surface, they have little molecules that allow them to bind to the damage that you've just created in that tumor environment, and these nanoparticles are able to deliver, the pink ones are the ones carrying the drug, they're able to deliver 40 times more drug to the tumor than if you didn't have this communicating system. So a little bit like ants that deposit chemicals, here you have a particle that's impacting the tumor, and that's changing the way a second particle is interacting with it. So we're starting to see these cooperative systems being tested as a meaningful way to give you a better treatment. And there's a number of ways in which we're starting to see this happen. The first one is just even if these particles aren't trying to work together, understanding what they do as a collective, so kind of implicitly working together, is really helpful, and that's something that's not necessarily done. So just understanding what they do as a group, even if they're not trying to work together is helpful. There's interesting work in terms of making particles that can self-assemble and disassemble, and that's really interesting when you're in a tumor because if you disassemble, you're becoming smaller and then you're better at going deep into the tumor tissue. If you're self-assemble, basically you're anchoring things in different environments, so if these are shiny things that you can see from the outside fluorescent markers, you're concentrating them. So there's lots of interesting dynamics just with self-assembly and disassembly, and then there's what we call stigmergy, so really communicating through the environment a little bit like the blue and the pink particles that you saw before. And there's different ways you could do that. You could do that by impacting the environment like we saw before, and then you interact with that environment. You could do that, there's particles actually that can heat up, and then they'll activate a heat-sensitive particle to release a drug, so you've got some form of communication with heat there. You can modify the tissue so they're better at moving in certain areas. So again, these are all things that we're starting to see in the literature that are being tested in living systems to treat an image cancer. All right. So that's the big picture, and then we need to do something concrete, and we really needed to do something that the bioengineers could understand would have an impact and could be potentially helpful for them today and not in years and years when we actually do have these swarming nanosystems. And so the first thing that we started to explore is this basic scenario. You have a vessel, you have these particles leaking out of the vessel, and then you have these cancer cells, and these cancer cells are over-expressing your receptor. Okay, that's the red part. And the particles combine to those receptors. And our goal was to make sure these particles would go deep into the tumor tissue and kill all the cells along the way. And that's really a group behavior because individual particles can't kill all the cells, so you need all your particles to do something as a collective that makes sense. And so we just had two parameters. One was the size of the particle, and the other was how sticky the particle was to the cancer cell. And so we had simulations, and we took a lot of numbers from the literature, and remember this was in a wet lab, so we had these numbers as well from my colleagues who were making them. And so we know things like how much drug is there per particle. If you inject that drug, a percentage ends up in the tumor, and then if you zoom into that tumor, you can basically take a slice out. And so we can't simulate the whole tumor. There's too much happening there. And so we thought, what's a really tricky scenario? Because if we tackle that one, maybe it can generalize to the remainder of the tumor. And so what we're looking for is the furthest point away from any vessel in a tumor, and that gives you around 200 microns, which is 20 cells. And so this is what we're simulating. We're simulating a vessel with 20 cells in a line, and we want to be able to accumulate in all these 20 cells at lethal levels so that we're killing the individual cells. And each one of these cell areas is a region, and in that region everything is well mixed, and we have a number of reactions that happen. So the particles can bind to the cancer cell, they can unbind from the cancer cell, and when they're bound, they can get eaten up by the cell, so they internalize. And then the particles diffuse from region to region. So it's quite a basic reaction diffusion model, and we solved that in two ways. One was to do it using a deterministic model, so here you have the partial differential equations, and the other one was to do use a stochastic model. And we wanted to do both. As a sanity check, it's always nice to have two models, and the stochastic one has more inherent noise, and we wanted to see if that had an impact. So this is what it looks like when you simulate it. And remember our goal was to kill 20 cells, and killing 20 cells is the dark red. And then we have two parameters. One parameter is how fast that particle is, that's the size of the particle, and the other parameter is how sticky that particle is. And so these ranges are taken from the literature, they pretty much encompass the whole range that you would get for nanoparticles, and it's quite interesting because first of all, most particles don't kill the 20 cells, so that was already an interesting bit of information. And then you can dive into the individual squares, and this is our stochastic simulation, by the way the deterministic model and the stochastic model give us exactly the same results, and so we were happy with that. And then if you look at A, A was a particle that was very sticky, and bioengineers think that's great because that means the cancer cell is going to eat up the particle, and so they're going to be able to deliver the drug in the cell. But if you look at what they do as a collective, well they leak out of the vessel, and then the first cell they encounter eats them up. And so all the particles end up in these first cells after they leave the vessel, and they never go deep into the tumor tissue. And so if you think of what the collective does, that's a poor solution, whereas at the individual level you might not have seen that. And even more interesting, you can see this whole range of different dynamics. So A, B, C, D are all different nanoparticle formulations just with these two parameters, and you get a whole range of different distributions within that tissue. So that was helpful, but we thought, you know, that's tricky. It's tricky with chemistry to really push those two parameters. And so what if we can come up with a different guideline? And we thought, well, why don't we encapsulate this particle in something that prevents them from binding? So essentially now you send them in, they diffuse throughout the tumor tissue, and then you take off that shell, and then you take off, there's different ways to do that. You could do that based on time, you could do that based on the chemicals in a tumor, there's a different pH, for example, you could do that using a light trigger at a certain time you shine a laser on that area of the tumor. And then they would take that yellow coating out and start sticking. And we used optimization to say, well, maybe you should wait 17 hours, that's what our simulations gave us. And then what we did is we tried lots of different scenarios. So that schematic where you saw the mouse, that was one set of parameters. But we can do this with lots of parameters, and it's always really hard to kill the 20 cells. So if we implement the strategy of just waiting for 17 hours and then unveiling, you get full cell death for all the different scenarios and for all the different nanoparticle formulations. So then you have something that you can potentially generalize in a way that's maybe more intuitive than actually tweaking individual particles. And it turns out that's a particle that we can make. So this is a particle that was made by Paula Hammond's lab. And so she had a particle where you had an outer layer which prevented it from binding. And then it would arrive in the tumor, it would take that outer layer off, which was PEG. And then that particle was positively charged. And cancer cells or cells in general like to eat up positively charged particles, so then they would get eaten up by the cells. And they showed in their paper, although that wasn't the intention of the paper, that you do get deeper tissue penetration than the ones that didn't have this dual functionality. So that was encouraging. But it was really, really just one scenario. And it was two parameters for the particles. And remember, all the knobs that I showed you that we could turn on those nanoparticles. And so we started, first of all, thinking about other scenarios. So this is something that we're exploring now with surgeons who are working with brain cancer because unlike tumors that are in the remainder of the body, particles are going to go to getting to those to the vasculature. But the brain is harder, essentially particles don't go to the brain. And so they inject the particles directly in the brain. And then you don't have diffusion, you actually have flow. And so we're interested in discovering new models that help us understand how deep the particles can go in that brain tissue. But even more, we're interested in opening this up to many more scenarios and many more particles. So what we did is we designed a game called Nanodoc that allows us to crowdsource the design of these nanoparticles. And so there's really two parts to this game. One part is here on the right and essentially there's scenarios. So you can have the bioengineers come and design the scenarios that are of interest to them. And then you've got your nanoparticle editor, which is what the crowd is going to interact with to tweak all these different parameter settings and help us explore the search space because you do end up with quite a big search space. And so this is what it looks like when you play. You've got Rob here, which we had medical doctors in our lab and one of them was called Rob. So that's ours. And then you have vessels and then you have cancer cells. And you can really just, we teach them how to do things step by step because obviously you can't learn all of this in one go. And then you can create your particles. And then when you inject them, that goes back to our scientific simulator. So it's using the parameters that are relevant to the bioengineers. And so we inject those and then you start to see these nanoparticles moving around and you can explore your space and see where these particles are. So this is a case where we hadn't injected enough. And so we basically explain a little bit more in terms of how that works. So there we go. And obviously it's not that simple in the real world, but at least it gives us some preliminary insights. And so this is the first level. And so we teach them the different skills as we go. So there's the cancer cells. There's the healthy cells. Do you know why we would need healthy cells in our scenarios? Yeah, exactly. Because basically the solution to everything, if you only had cancer cells, would be to dump loads of drug on it. And in the real world, you want to be specific. So we need to mix those so that we create thoughtful treatments. You can change the dosage. You can change when you inject these particles. You can combine these nanoparticles. So if you remember the blue and the pink particles, that's something I'm very interested in understanding how we should do. You can change the size of the particle. That changes how fast or slow they move. Some of these cancer cells have receptors on their surface and everything is coded. So this would be receptor red or receptor A. And you can make a particle with a matching coding and that's going to determine who they bind to. And you can have different strengths for how strongly they interact. And then you can load them with drugs. And you can create smart materials where something binds to the cancer cell and then they release something in the environment. So now you start to get the sensor actuation system that you'd see a little bit more in a robotic system. But what's missing is the ability to go left or right. Our robots are quite good at following directions and moving in a way that's controllable, whereas our particles are diffusive. But what we can do is act on how fast or how slow they diffuse. So through self-assembly and disassembly, you can start combining parameters and start to get things that allow you to control their motion. And so we had this game up and we started spreading the word. I run RoboHub.org. So it's basically a blog about robotics. And we did a lot of work to push the word out. And people could tweet little diplomas when they learned their skills and basically tell the world that they were playing this game. And so we've had around 100,000 simulations since we launched the game. And now we're going through the data that's been generated. And so this is just an example. Once we've taught them all the skills, we give them challenges to solve. So here what you have is a vessel. And you've got the goal of this task was you see this blue turquoise cell. Let's say that's a mutation or a stem cell, a rare event in the environment. And you want to accumulate your particles around there. And so the particles are leaving the vessel. And the first thing that was interesting is that the solutions the crowd came up with, they all had two types of nanoparticles. And essentially you had one particle that would bind to the blue cell, release something in the environment, and that would activate a second nanoparticle, which released something that we could accumulate in the surrounding cells. So you have one particle activating another particle. And that's something that we had never taught the crowd to do. So just in terms of thinking outside of the box and throwing challenges and not thinking too much about it and getting them to come up with ideas, was something that we wanted to see them do and it looks like they do do that. The other thing to notice is the dynamics. If you're a bioengineer, you have stuff leaving the vessel, but it looks like they're propagating from that rare event. And so you're playing around with dynamics in an interesting way that creates collective behaviors, which aren't the ones that you necessarily expect. And so now that we have this game, essentially what we're doing is that we're, I'm playing you in my own game, so that we can come up with new swarm type behaviors. And so this is one behavior, which we started out from the ants creating their trails and use those same rules and simplified and simplified. And here what you have is essentially a rare event, your vessel, and trails that you're creating in simulation between these two points using very, very simple mechanisms. So all the things I used to put on my flying robots to make them do ant-like behaviors, it looks like you can probably do something with much, much less. And so it makes you think a little bit different about how you need to control these swarms of very large, large numbers of robots. And it's quite fun to see the video. Basically they grow. So you have things leaving, and then these trails grow throughout the environment until they reach the vessel. And we're looking at how we could use this. One way would be you're concentrating stuff, right, by creating these trails. And so that could be interesting just in terms of imaging, because you have things that are in one location rather than being distributed. It could be interesting as well in terms of understanding where particles come through and how they move throughout tissue, because they're essentially you're looking at potentially shortest paths. And so that was really the simulation world. And just like the flying robots before that you saw us through, we really need to push these things to reality to be able to have an impact. And so a lot of the work has been on figuring out how are we going to push these to reality. And the first step that we were interested in taking was to use robots as a proxy for these nanoparticles. And simulations do have simplifications in them, and you do make assumptions about the physical world. And so what we do with these large swarms of robots is essentially in ten lines of code we can test our nanoparticle behavior, see what they do, and see if they teach us anything about the dynamics of these systems. So here what you see is an experiment that we did over with Radica Nagpau's lab in Harvard and Mike. And so imagine that these nanoparticles, these robots here, they're little quarter sized robots. They're called kilobots, because Mike over in Harvard made 1024. So instead of a kilobit, they're a kilobot. And each one of these particles has two vibrating motors, so you have some level of differential drive. We make them move randomly, because we're looking at nanoparticles. They also have a communication range within 10 centimeters, an LED, so we can see a little bit what they're thinking, and then they have an ambient light sensor. And so what you're going to see here are nanoparticle robots leaving a vessel, and then binding to those cancer cell robots in this robotic tissue. And so here essentially what's happening is that they're binding very strongly, and so they're not going deep within that robotic tissue. And very often we actually run these in the dark, because they look like fluorescent markers in the dark. And so this is what we show the bioengineers, and they'll say, why doesn't your robot go there, and I'll say, why doesn't your nanoparticle go there? So it does make you think about it. So there you have your cells, the green ones are the particles. When they bind or get eaten up, they turn purple, so you can see this sort of the outside of the cell. And there's lots of room between those two cells, but just by the nature of diffusion, they never stick to those two cells. Then what we did is we said, OK, let's tune down the binding. And if you tune down the binding, they should go deeper. They should go deeper in that environment. And so here what you have on the upper right, focus on that one. And you can see there's one particle that's going to zip by it, and two, and three, and four. And no one ever sticks to it, which makes sense, because we've tuned down the binding so much. And so eventually one does, because it actually bumps into it. And then we thought, OK, well, let's try our other strategy, where you don't let them stick, turn on the binding, and see if that works better. So essentially you're letting them go in, turning on the binding. And then they would start sticking. And so again, you can look at the one on the upper right. And OK, so the particles are able to diffuse through there. And then you have one of these, again, those robots at the edge that are coming by. And so now we're going to turn on the binding. And you can see that they all bind immediately to that robot. Some of them here are still moving. And so we thought, this is what we had predicted with the model. We didn't need that to understand. But what we thought is we can inject way fewer particles with this generalizable strategy than with the other strategy, which is just to tune down the binding, because then you have a lower probability of binding to the last cells. And so we went back to the computational models, and in fact you can inject less. So it's one of these things where literally you take 10 minutes to program the robots, throw this behavior on all the robots, see something, and I think it is powerful in terms of understanding dynamics in the physical world. We've since, at the Bristol Robotics Lab, where I've been for the past year and a half, built a thousand of these robots as well. And so that was hard work from the students who have spent essentially, spent two weeks in the sweatshop. Granted, I was there as well. It was really fun. If you get a chance to do this, it's well worth it. And so we've got these robots. And in our arena, we have a projector, and we can project environments dynamically, interact with these robots. And so something that we really cared about was to see if we could do experiments on the robots that were proportionally at scale to the experiments in the nano world. So if you take a nano particle, and you blow that up to a three centimeter sized robot, there's a scaling factor there. And if you do the same thing with time, there's also a scaling factor there. And so can you take a nano experiment, which is essentially what we were doing in Nanodoc, and putting them on a swarm of a thousand robots. And it turns out that you can work out numbers that give us proportionally relevant experiments. So here you have cancer cells. These particles would be more in the several hundred nanometer range if you were to scale them down. But we are starting to see interesting dynamics. And something that's come just through these first experiments is that a lot of our simulations are symmetric in how we build them. And just to skew slightly in terms of the projector and how close things are, do distribute your particles in very different ways. And just seeing that is quite interesting. And the other fun part is I think that as we're designing these algorithms that could work on nanoparticles that are in the tenth to the power of thirteen, we're removing a lot of the assumptions and functionalities that we typically put on robotic swarms. And I think that's going to allow us to create new algorithms for robot swarms that could be useful in real world applications. So rather than having a robot that you could, you know, that would be fancy enough to go through a forest on its own, could you have many, many very simple robots that could be able to find a way and then recruit the other robots? Or could you use them for things like cleaning up an oil spill? And so we're trying to figure out what algorithms work, work in the thousand robots, because it is a different, a different type of algorithm. And really just to prove this point, these are, I think they're thirty robots. And these are thirty robots in our swarm trying to go straight. And so, you know, as we simplify these robots and as we scale those numbers up, you're not going to be able to calibrate all these robots. They're going to work very differently when you put them in the real world than when you put them in the laboratory environment. And so making things or algorithms that are robust to noise like this in terms of the motion of the robots, I think, is going to be very helpful for bringing things to the real world. And part of learning how to do that is to go a little bit back to the roots on the evolutionary side. So this here is an experiment where essentially we're trying to evolve both, you know, the neural network or the program of the robot to do swarm tasks, but also the simulator, because we don't have good simulators to mimic what these robots do. If you just put regular noise like you would in a robotic simulator, it doesn't correspond to anything that we're seeing on these one thousand robot swarms. So we are having to learn really new tricks to make very large numbers of simple robots work together. All right. And then the last step is really to push this, push this to the nano-scale. And so what we've been looking at is to create microfluidic devices in which we can visualize the dynamics of these nanoparticles under the microscope. And so there's really two parts. So this one part is to be able to have a vessel layer, essentially where you could release these particles. And that's the one filled in red. This was done over in Boston with Sangita as well. And then the other part is these tissue layers where you can fill them up with gels and you can fill them up with cancer cells and start to monitor how these particles diffuse from the vessel into those chambers so that we can see some of these swarm behaviors. But even beyond that, we're really interested in being able to fast prototype some of these environments so that we can take the nanodoc world and put it on the robots and then put it under the microscope and very quickly do experiments where we might be able to see these chain formations or this decision making. And so one way we're doing that is to really just 3D print the scaffolds in which we would make our microfluidics and then we pour the PDMS and make our usual microfluidics. So we can very quickly iterate on the design of these devices. And having the cells in there was quite interesting and relevant, but it is finicky to understand all the different parameters that are involved. And right now we're backing out just so that we can understand our environment better and have something that's well characterized so that we can put instead of cells cell-like structures, so these are microspheres that are cell-sized, that we can functionalize with different materials so it looks like the receptors that they have on their surface. We can inject the nanoparticles and we can make them diffusive or convective so we have the brain model or we have the more solid tumor model. And so this is just, these are really just experiments from very recently because we're finally up and running with our microfluidics. And the tricky thing is also, you know, you want your tissue to stay put, but you want your nanoparticles to be able to go through. And so you need to understand sort of how things move through tissue, how some of it stays put and how you're able to convect there. And so we're currently diving in to see a little bit more of how these things interact. We're really just at the first steps of the experimental side. But I'm hoping because we have a well-controlled environment that we're going to be able to, you know, to have the patterns that we see in the nanodoc world and start to see these interesting aspects. Another interesting aspect with the nanoparticles and the microfluidics is work that we did with Brad Nelson and just seeing how, you know, our particles aren't very controllable. They diffuse. Yes, you can do things to make them move differently. But Brad Nelson has a really beautiful ABF system which is much more controllable in how it interacts. And so here what you see is an ABF in one of our microfluidic chambers that's full of nanoparticles. And here what you're going to see is Simon and me just going through this channel. And there it's quite interesting because you see these plumes of nanoparticles around the artificial bacterial flagella, so this micro robot. And so we were thinking, how could you use these robots which are much more controllable to interact with these nanoparticle populations? So now you can essentially use these nanoparticles to maybe, you know, go deeper in a tumor or tissue to do something smart thanks to the help of the robot that's more controllable. So creating mixed populations of nanoparticles and these micro robotic systems. So a lot of what I'm working on is really this idea of swarm engineering or systems nanotechnology. And it's very often you know what your group behavior should be, right? You want to kill all the cancer cells in an environment. You want to create a communication network. You want to distribute it over a forest to be able to explore it. And these are really group behaviors. And so the tricky thing is solving that puzzle where you go from the group behavior and then you need to figure out how the individual agents should be designed. And to do that you need simulations that allow you to predict the emergent behaviors. And then really this push towards reality. So either with this large test bed of many robot swarms and then to the micro fluidic devices so that we can see some of these collective behaviors under the microscope. And we do work very closely with clinicians who are either doing the brain cancer or who are doing working on the solid tumors. So we can take some of these ideas and push them to reality. And there's really two parts. One is just, you know, we can answer questions that bio engineers have now. How does my particle distribute? And the other is what do we need to be able to make these swarming type behaviors? All right. And then just in terms of thank you, there's a lot of people on here. So obviously the laboratory, the Bristol Robotics Laboratory and the University of Bristol, Senghi DeBatia's laboratory over at MIT, the VEAS Institute in Harvard, which did the original KLO bots and everything is open hardware so that allowed us to create our own. And they've been a wonderful, wonderful help in building up this platform. The MIT lab, so a lot of the stochastic simulations were done thanks to a tool. And then we build a whole framework around it to be able to simulate nanoparticles in cancer. And then Brandon Nelson's lab for the artificial bacterial flagellas. All right. So I think I wanted to also thank the players, hopefully. And any questions that you may have? Do you have any questions? They just get there because of their size. So essentially it's EPR, enhanced permeability and retention. And they're leaking out from the vessels into the tumor environment. No, they're just getting there. They accumulate there because of their size. They were also bigger particles. And so they like to stick around in the tumors. Yeah. So it's really chemistry for most of them. Actually, a lot of what we do in terms of the wealth that functionalized or characterized devices, we buy them off the shelf. So you can buy fluorescent nanoparticles, which is why you can see them so nicely. There's a large variety of particles that you can make. So some of the ones that my collaborators make are liposomes, which are essentially fatty bubble. You can create gold particles, iron oxide nanoparticles, which are going to be magnetic. You can create polymer-based particles. So there's really a whole range of particles. And it's mainly chemistry. I just have a question. Could you explain again what specifically are these kilobots allowing you to see that simulation isn't allowing you? Yeah. So, yeah, that's a great question. I love the simulations. Most of my work is done in simulation before we push those to reality. I think what the robots allow us to see is really in 10 lines of code. That was to see how our rules work on a physical platform, with all the noise that entails and all the assumptions taken out, because our simulations do make assumptions. So for us, I think they're more part of our design toolbox than the answer to the question. So they're just another way to visualize dynamics. What I'm really interested in is taking those algorithms and pushing them to the robotics world, because I think that's going to teach us a different way of programming these very large swarms of robots. You could go back and make better simulations. I think that's also a very valid approach. The question is how long does it take you to make a better simulator versus throwing them on the robots once they're there? One thing, I mean, people don't know the answer to that question. Are the robots really helpful in this design process? And I think because we have this crowd sourcing tool, it would be really interesting to let the crowd design their behaviors on the robot swarm, or design them in simulation, and see just the trajectory of how they optimize these treatments. And that might give us more quantitative data, because you're right. Now it's not entirely know how helpful that is. I love them, but obviously it needs to be a little bit more scientific. Yeah, that's a good point. So it really depends on your particles. So some of these particles are made of materials that are already FDA approved and that are deemed to be safe. Even if they go elsewhere in the body, the assumption is they're going to be cut down and cleared through the natural processes. The reality is the percentage that accumulates in the tumor is not 100%. It's going to be 6% if you're lucky. And so the particles are going to go elsewhere. So you need to make sure that you've done the toxicology and you understand what happens to those particles if they're elsewhere. And that's what we call sort of the passive accumulation, just because of their size, a lot of them go to the tumor. But then you have active targeting. So this idea of having receptors on the surface or targeting ligands on the surface that bind to receptors. And that's a little bit more specific in that they need to bind for the cancer cells to eat them up in higher probability. So that gives you an additional handle to target the cells. But you do need to do the toxicology. I have an add on question. You showed the molecule that is able, with an extra coating, is able to release it. So when does the molecule know when to release that? Yeah. So there's two different types of particles that I know of that do that. One of them is pH-based. So essentially they arrive in the tumor environment and the tumor environment has a different pH. And so that allows the particles to shed the coating. And the other one is enzyme-based. So we draw enzymes as Pac-mans because they go and they cut stuff up. And so in the tumor environment, you also have enzymes that are a bit of a signature of those tumors and they can go and cut off those pegs. And can you really program them? Can you say, okay, I want this pH to release it or is it just luck that you might release it? No, so they spend a lot of time. So obviously once you have these ideas of how to make them, then the material scientists in the chemists spend a lot of time finding the particle that can do that. But they, yes, so they can engineer them to do that. Yeah. Just another question, has it ever been tried and invalidated or whatever to actually control these particles with electric or magnetic fields coming from the outside? Did you connect the decision where you guys... Yeah, yeah....by chemistry going? Right. So Brad Nelson's obviously the expert on the magnetic side. I think what I know more about is the triggers. So you can shine light on them and trigger them using light. So they're more of binary activate now or do something now than the very fine control of these nanoparticles. But there are definitely researchers who are working on that aspect. So I think that's from a robotics and control perspective is really interesting because now you have much more of a robotic system in terms of closed loop and being able to control what's happening in there. So yes, that's definitely an interesting way to go. What happens to the particles after they're destroyed? So well, it depends again on the particles. So the idea is for them to essentially kill those tumor cells and then have them deplete. And then, yeah, again, you need to do the toxicology to see what happens with the particles further down. So it depends on the type of particle, basically. If they cut down, then they're going to end up in the urine. So it's really different pathways for different types of particles. Okay. Aperol? Yeah. So thank you very much, Sabine. I'd like to, in the name of the mobile house systems labs, which I'm headed, I would like to know if you could please follow me up, which is where it always is. And let's thank Sabine again for the cameras and for the information.
 Well, I really want to thank Walter for inviting me over here.  It is nice to cross paths again, and it's been a wonderful day visiting all the labs  that I admire and that are extremely relevant to the work that I'm doing.  So I look forward to working more with ETH in the future.  So my talk is going to be about going from swarms of flying robots to nanomedicine.  Swarming is a little bit of the key word here.  I work at the Bristol Robotics Laboratory, which is a joint venture between the University  of Bristol and the University of West of England.  And we're over in Bristol, UK.  There's actually a direct flight with EasyJet, so it's really easy to come here.  So I'm really interested in swarm systems.  So if you look at these flocks of birds, they're able to do these beautiful complex behaviors.  And there's a lot of features that are interesting for real world applications.  So for example, you can keep adding birds to the flock, and the flock is going to continue  to fly, so it's scalable.  If a bird falls from the sky, the whole flock doesn't fall to the ground, so they're very  robust.  And together they can do more than the individual birds.  For example, they're much better at avoiding predators than the individual birds.  And so we want to take inspiration from these swarm systems and try to see if we can use  these principles for real world applications.  And there's really a puzzle behind that.  And that's, you look at this flock of birds, but if you take a bird from the sky and look  at it, there's no way you're going to understand how they flock.  And so the puzzle is really, you have this swarm behavior, a group behavior, and how  should you design the individual agent so that you get what you want to get for the  collective.  And so I've sort of been studying this question for a while, and the first thing that we tackled  over in Dario Floriano's Labrì Piafell was to see if we could use swarms of flying robots  to create communication networks in disaster areas.  And so the idea was you would arrive on a disaster site and you'd have these rescuers  and they should be able to throw these flying robots.  And these flying robots should organize over the area so that this rescuer can communicate  throughout the network to another rescuer to a victim in the environment.  And so to look at this, we were using these simple flying robots that were fixed link  platforms.  There's just a propeller at the back there, fully autonomous.  And we were assuming that our robots were quite simple.  They could just talk to their neighbors using wireless communication and they had a compass.  And so using just those basic signals, could we make them do something interesting from  an application perspective?  And that puzzle again was we wanted them to create communication networks, but how do  you design the controller for the individual flying robots?  And so one way we tackled this was to see if we could come up with those controllers  automatically.  And we were using a thing called artificial evolution to basically come up with the weights  of an artificial neural network here.  So in the input, you just had a compass in communication, so who you're talking with  in your neighborhood.  And as an output, all we were acting on was how much should you turn.  And that makes sense if you have a fixed wing, because you don't want to be stopping them,  because otherwise they'd fall from the sky.  So we were just keeping constant speed and changing the way these robots turn.  And so we ran our artificial evolution, which was to come up automatically with these controllers.  And this is what it came up with.  You have these two rescuers in simulation that have a communication range, that's the  blue disk.  And somehow we want to create a bridge between these two.  And we're launching these 20 robots.  And you can see what evolution came up with was to create these chains, which makes sense  because communication is a rubber band.  And so it's easy to kind of go away and come back if you break the connection.  And then they synchronize, go from left to right, find the second rescuer, and then maintain  that connection.  So we found all those things through the evolution.  And then we learned from it and then implemented it on the flying robots and threw those in  the air.  So here we have 10 flying robots.  This is Sivrin who built these robots.  And I'm basically behind the camera.  And we could throw them, they were fully autonomous.  They'd stay up in the air until we started our experiments.  And they're about bird size.  So the birds, sometimes you spot them following the flying robots.  And we often wondered, actually if you can spot them, let me know.  I've left this up quite a long time.  But this is just 10 of them waiting for the experiment to start.  And then what you're going to see are some of the GPS trajectories of our experiments.  The first one is just going to be this communication bridge.  So essentially launching robots, they grow a bridge, and then they connect these two  rescuers.  And so there you see sort of the GPS trajectories once they've created those bridges.  And here you can see we're looking at pretty big areas around a kilometer.  And here what you see is something else we learned from evolution, which is the synchronization.  And here all the robots are moving a little bit like they're beating with the same heading  at the same time.  And then that was the evolutionary part.  But there's also a different way in which we knew how to design swarms.  And that was to take inspiration from nature.  And so biologists have been studying different algorithms that can give you swarm-like behaviors.  And one of them was flocking.  And so this is just 10 robots flocking.  These are actual GPS trajectories.  And it starts very messy.  But over time, they converge towards a circle.  There you have your circle.  And that's what we had predicted, because one of the key things with all of the experiments  you're seeing is we did a lot of simulations, understood how we thought it was going to  work in reality through the robots in the air, and then tried to see if we could actually  get that group behavior that we wanted in reality.  So that was a fun PhD.  But it was really just 10 flying robots.  And I always thought that swarms needed to work in the hundreds and the thousands and  the millions.  And so I went out looking for a platform that had those many zeros after the one.  And that's around the time that I got interested in nanomedicine.  So nanoparticles are very interesting because of their size in the nanometer range.  And because of their size, they're very good at leaking into tumor environments.  So here is an animation of a vessel.  And vessels and tumors are very leaky.  And these nanoparticles are just the right size that they're going to leak out of the  vessels and tumors.  And then they're going to arrive in this tumor tissue, which is a high-pressure environment.  So they kind of diffuse throughout this tumor tissue.  And these nanoparticles, they work in the 10 to the power 13.  So yay, that was the number that I was looking for.  And not only that, but they have interesting features.  So you can change their size.  You can change their shape.  You can change their charge.  That's going to change what they stick to in the environment.  You can, some of them are energy receptive.  So you can activate them using magnetic fields or using light.  And some of these particles, you can decorate them with little molecules on their outside  that allow them to, for example, bind to receptors on a cancer cell.  And you can load them with drugs that you can release in a more or less controlled fashion.  And so there's a lot of knobs that you can change on the design of these nanoparticles,  a lot of different individual functionalities.  And actually, if you go into the literature, we're starting, we're just starting to understand  how really the design of a particle, in terms of its size and shape and charge and material  and surface and cargo, impacts the way these particles move in a tumor environment.  And one easy one is if you have a smaller particle, that's going to make the particle  diffuse faster within a tumor tissue.  So it's really just one of those parameters.  And so the question is, well, how would you make nanoparticles swarm?  Well the birds, maybe they had their brain that was telling them how to behave with respect  to the neighboring birds.  With the flying robot, we had a computer, and so we just had a program on board, each  one of these flying robots that allowed them to do these flocking behaviors.  And with the nanoparticles, we don't have either a brain or computational power, really.  And so what we're doing is putting the intelligence and the design of the bodies of these nanoparticles.  So how should you turn the knobs of these particles so that when you inject them, they  behave as a collective in the way that you want them to behave?  And the reason we'd want to do that is that these are really the key words in red from  the swarm literature.  So bees, for example, are very good at making decisions about a new nest.  What if you could use those principles to make nanoparticles, that, you know, make a  decision about the state of the tumor?  What if you could have nanoparticles that synchronize the delivery of a drug, a little  bit like bacteria, when they do their quorum sensing?  What if you could do map mapping, you know, the ants that are creating those picnic, those  trails to your picnic table?  What if you could use that within a tumor environment to do interesting mapping and  amplification?  And it's not a crazy idea.  So this is work, basically, once I got excited in nanomedicine, I went to a lab over in Boston,  San Gita Bacchia's laboratory, and she was working in a wet lab environment, making these  nanoparticles and testing them on living systems to see if they had a clinical impact.  And so this is one example of a system that they designed and tested in a living system.  Here you have these blue particles, which are made out of gold, and they arrive through  the vessels, they leak out from the vessels into the tumor environment, and these nanoparticles,  because they're made out of gold, you can shine near and for red light on them, and  that causes them to vibrate, and because they vibrate, they heat up, and then they cause  damage to the tumor tissue.  So you're essentially causing damage to the tumor, and then they send in the second wave  of nanoparticles, the pink ones, and these particles also leak out, but on their surface,  they have little molecules that allow them to bind to the damage that you've just created  in that tumor environment, and these nanoparticles are able to deliver, the pink ones are the  ones carrying the drug, they're able to deliver 40 times more drug to the tumor than if you  didn't have this communicating system.  So a little bit like ants that deposit chemicals, here you have a particle that's impacting  the tumor, and that's changing the way a second particle is interacting with it.  So we're starting to see these cooperative systems being tested as a meaningful way to  give you a better treatment.  And there's a number of ways in which we're starting to see this happen.  The first one is just even if these particles aren't trying to work together, understanding  what they do as a collective, so kind of implicitly working together, is really helpful, and that's  something that's not necessarily done.  So just understanding what they do as a group, even if they're not trying to work together  is helpful.  There's interesting work in terms of making particles that can self-assemble and disassemble,  and that's really interesting when you're in a tumor because if you disassemble, you're  becoming smaller and then you're better at going deep into the tumor tissue.  If you're self-assemble, basically you're anchoring things in different environments,  so if these are shiny things that you can see from the outside fluorescent markers,  you're concentrating them.  So there's lots of interesting dynamics just with self-assembly and disassembly, and then  there's what we call stigmergy, so really communicating through the environment a little  bit like the blue and the pink particles that you saw before.  And there's different ways you could do that.  You could do that by impacting the environment like we saw before, and then you interact with  that environment.  You could do that, there's particles actually that can heat up, and then they'll activate  a heat-sensitive particle to release a drug, so you've got some form of communication with  heat there.  You can modify the tissue so they're better at moving in certain areas.  So again, these are all things that we're starting to see in the literature that are  being tested in living systems to treat an image cancer.  All right.  So that's the big picture, and then we need to do something concrete, and we really needed  to do something that the bioengineers could understand would have an impact and could  be potentially helpful for them today and not in years and years when we actually do  have these swarming nanosystems.  And so the first thing that we started to explore is this basic scenario.  You have a vessel, you have these particles leaking out of the vessel, and then you have  these cancer cells, and these cancer cells are over-expressing your receptor.  Okay, that's the red part.  And the particles combine to those receptors.  And our goal was to make sure these particles would go deep into the tumor tissue and kill  all the cells along the way.  And that's really a group behavior because individual particles can't kill all the cells,  so you need all your particles to do something as a collective that makes sense.  And so we just had two parameters.  One was the size of the particle, and the other was how sticky the particle was to the  cancer cell.  And so we had simulations, and we took a lot of numbers from the literature, and remember  this was in a wet lab, so we had these numbers as well from my colleagues who were making  them.  And so we know things like how much drug is there per particle.  If you inject that drug, a percentage ends up in the tumor, and then if you zoom into  that tumor, you can basically take a slice out.  And so we can't simulate the whole tumor.  There's too much happening there.  And so we thought, what's a really tricky scenario?  Because if we tackle that one, maybe it can generalize to the remainder of the tumor.  And so what we're looking for is the furthest point away from any vessel in a tumor, and  that gives you around 200 microns, which is 20 cells.  And so this is what we're simulating.  We're simulating a vessel with 20 cells in a line, and we want to be able to accumulate  in all these 20 cells at lethal levels so that we're killing the individual cells.  And each one of these cell areas is a region, and in that region everything is well mixed,  and we have a number of reactions that happen.  So the particles can bind to the cancer cell, they can unbind from the cancer cell, and when  they're bound, they can get eaten up by the cell, so they internalize.  And then the particles diffuse from region to region.  So it's quite a basic reaction diffusion model, and we solved that in two ways.  One was to do it using a deterministic model, so here you have the partial differential  equations, and the other one was to do use a stochastic model.  And we wanted to do both.  As a sanity check, it's always nice to have two models, and the stochastic one has more  inherent noise, and we wanted to see if that had an impact.  So this is what it looks like when you simulate it.  And remember our goal was to kill 20 cells, and killing 20 cells is the dark red.  And then we have two parameters.  One parameter is how fast that particle is, that's the size of the particle, and the other  parameter is how sticky that particle is.  And so these ranges are taken from the literature, they pretty much encompass the whole range  that you would get for nanoparticles, and it's quite interesting because first of all, most  particles don't kill the 20 cells, so that was already an interesting bit of information.  And then you can dive into the individual squares, and this is our stochastic simulation,  by the way the deterministic model and the stochastic model give us exactly the same  results, and so we were happy with that.  And then if you look at A, A was a particle that was very sticky, and bioengineers think  that's great because that means the cancer cell is going to eat up the particle, and  so they're going to be able to deliver the drug in the cell.  But if you look at what they do as a collective, well they leak out of the vessel, and then  the first cell they encounter eats them up.  And so all the particles end up in these first cells after they leave the vessel, and they  never go deep into the tumor tissue.  And so if you think of what the collective does, that's a poor solution, whereas at the  individual level you might not have seen that.  And even more interesting, you can see this whole range of different dynamics.  So A, B, C, D are all different nanoparticle formulations just with these two parameters,  and you get a whole range of different distributions within that tissue.  So that was helpful, but we thought, you know, that's tricky.  It's tricky with chemistry to really push those two parameters.  And so what if we can come up with a different guideline?  And we thought, well, why don't we encapsulate this particle in something that prevents them  from binding?  So essentially now you send them in, they diffuse throughout the tumor tissue, and then you take  off that shell, and then you take off, there's different ways to do that.  You could do that based on time, you could do that based on the chemicals in a tumor,  there's a different pH, for example, you could do that using a light trigger at a certain  time you shine a laser on that area of the tumor.  And then they would take that yellow coating out and start sticking.  And we used optimization to say, well, maybe you should wait 17 hours, that's what our  simulations gave us.  And then what we did is we tried lots of different scenarios.  So that schematic where you saw the mouse, that was one set of parameters.  But we can do this with lots of parameters, and it's always really hard to kill the 20  cells.  So if we implement the strategy of just waiting for 17 hours and then unveiling, you get full  cell death for all the different scenarios and for all the different nanoparticle formulations.  So then you have something that you can potentially generalize in a way that's maybe more intuitive  than actually tweaking individual particles.  And it turns out that's a particle that we can make.  So this is a particle that was made by Paula Hammond's lab.  And so she had a particle where you had an outer layer which prevented it from binding.  And then it would arrive in the tumor, it would take that outer layer off, which was  PEG.  And then that particle was positively charged.  And cancer cells or cells in general like to eat up positively charged particles, so  then they would get eaten up by the cells.  And they showed in their paper, although that wasn't the intention of the paper, that you  do get deeper tissue penetration than the ones that didn't have this dual functionality.  So that was encouraging.  But it was really, really just one scenario.  And it was two parameters for the particles.  And remember, all the knobs that I showed you that we could turn on those nanoparticles.  And so we started, first of all, thinking about other scenarios.  So this is something that we're exploring now with surgeons who are working with brain  cancer because unlike tumors that are in the remainder of the body, particles are going  to go to getting to those to the vasculature.  But the brain is harder, essentially particles don't go to the brain.  And so they inject the particles directly in the brain.  And then you don't have diffusion, you actually have flow.  And so we're interested in discovering new models that help us understand how deep the  particles can go in that brain tissue.  But even more, we're interested in opening this up to many more scenarios and many more  particles.  So what we did is we designed a game called Nanodoc that allows us to crowdsource the  design of these nanoparticles.  And so there's really two parts to this game.  One part is here on the right and essentially there's scenarios.  So you can have the bioengineers come and design the scenarios that are of interest to them.  And then you've got your nanoparticle editor, which is what the crowd is going to interact  with to tweak all these different parameter settings and help us explore the search space  because you do end up with quite a big search space.  And so this is what it looks like when you play.  You've got Rob here, which we had medical doctors in our lab and one of them was called  Rob.  So that's ours.  And then you have vessels and then you have cancer cells.  And you can really just, we teach them how to do things step by step because obviously  you can't learn all of this in one go.  And then you can create your particles.  And then when you inject them, that goes back to our scientific simulator.  So it's using the parameters that are relevant to the bioengineers.  And so we inject those and then you start to see these nanoparticles moving around and  you can explore your space and see where these particles are.  So this is a case where we hadn't injected enough.  And so we basically explain a little bit more in terms of how that works.  So there we go.  And obviously it's not that simple in the real world, but at least it gives us some  preliminary insights.  And so this is the first level.  And so we teach them the different skills as we go.  So there's the cancer cells.  There's the healthy cells.  Do you know why we would need healthy cells in our scenarios?  Yeah, exactly.  Because basically the solution to everything, if you only had cancer cells, would be to  dump loads of drug on it.  And in the real world, you want to be specific.  So we need to mix those so that we create thoughtful treatments.  You can change the dosage.  You can change when you inject these particles.  You can combine these nanoparticles.  So if you remember the blue and the pink particles, that's something I'm very interested in understanding  how we should do.  You can change the size of the particle.  That changes how fast or slow they move.  Some of these cancer cells have receptors on their surface and everything is coded.  So this would be receptor red or receptor A. And you can make a particle with a matching  coding and that's going to determine who they bind to.  And you can have different strengths for how strongly they interact.  And then you can load them with drugs.  And you can create smart materials where something binds to the cancer cell and then  they release something in the environment.  So now you start to get the sensor actuation system that you'd see a little bit more in  a robotic system.  But what's missing is the ability to go left or right.  Our robots are quite good at following directions and moving in a way that's controllable, whereas  our particles are diffusive.  But what we can do is act on how fast or how slow they diffuse.  So through self-assembly and disassembly, you can start combining parameters and start  to get things that allow you to control their motion.  And so we had this game up and we started spreading the word.  I run RoboHub.org.  So it's basically a blog about robotics.  And we did a lot of work to push the word out.  And people could tweet little diplomas when they learned their skills and basically tell  the world that they were playing this game.  And so we've had around 100,000 simulations since we launched the game.  And now we're going through the data that's been generated.  And so this is just an example.  Once we've taught them all the skills, we give them challenges to solve.  So here what you have is a vessel.  And you've got the goal of this task was you see this blue turquoise cell.  Let's say that's a mutation or a stem cell, a rare event in the environment.  And you want to accumulate your particles around there.  And so the particles are leaving the vessel.  And the first thing that was interesting is that the solutions the crowd came up with,  they all had two types of nanoparticles.  And essentially you had one particle that would bind to the blue cell, release something  in the environment, and that would activate a second nanoparticle, which released something  that we could accumulate in the surrounding cells.  So you have one particle activating another particle.  And that's something that we had never taught the crowd to do.  So just in terms of thinking outside of the box and throwing challenges and not thinking  too much about it and getting them to come up with ideas, was something that we wanted  to see them do and it looks like they do do that.  The other thing to notice is the dynamics.  If you're a bioengineer, you have stuff leaving the vessel, but it looks like they're propagating  from that rare event.  And so you're playing around with dynamics in an interesting way that creates collective  behaviors, which aren't the ones that you necessarily expect.  And so now that we have this game, essentially what we're doing is that we're, I'm playing  you in my own game, so that we can come up with new swarm type behaviors.  And so this is one behavior, which we started out from the ants creating their trails and  use those same rules and simplified and simplified.  And here what you have is essentially a rare event, your vessel, and trails that you're  creating in simulation between these two points using very, very simple mechanisms.  So all the things I used to put on my flying robots to make them do ant-like behaviors,  it looks like you can probably do something with much, much less.  And so it makes you think a little bit different about how you need to control these swarms  of very large, large numbers of robots.  And it's quite fun to see the video.  Basically they grow.  So you have things leaving, and then these trails grow throughout the environment until  they reach the vessel.  And we're looking at how we could use this.  One way would be you're concentrating stuff, right, by creating these trails.  And so that could be interesting just in terms of imaging, because you have things that are  in one location rather than being distributed.  It could be interesting as well in terms of understanding where particles come through  and how they move throughout tissue, because they're essentially you're looking at potentially  shortest paths.  And so that was really the simulation world.  And just like the flying robots before that you saw us through, we really need to push  these things to reality to be able to have an impact.  And so a lot of the work has been on figuring out how are we going to push these to reality.  And the first step that we were interested in taking was to use robots as a proxy for  these nanoparticles.  And simulations do have simplifications in them, and you do make assumptions about the  physical world.  And so what we do with these large swarms of robots is essentially in ten lines of code  we can test our nanoparticle behavior, see what they do, and see if they teach us anything  about the dynamics of these systems.  So here what you see is an experiment that we did over with Radica Nagpau's lab in Harvard  and Mike.  And so imagine that these nanoparticles, these robots here, they're little quarter sized  robots.  They're called kilobots, because Mike over in Harvard made 1024.  So instead of a kilobit, they're a kilobot.  And each one of these particles has two vibrating motors, so you have some level of differential  drive.  We make them move randomly, because we're looking at nanoparticles.  They also have a communication range within 10 centimeters, an LED, so we can see a little  bit what they're thinking, and then they have an ambient light sensor.  And so what you're going to see here are nanoparticle robots leaving a vessel, and then binding  to those cancer cell robots in this robotic tissue.  And so here essentially what's happening is that they're binding very strongly, and so  they're not going deep within that robotic tissue.  And very often we actually run these in the dark, because they look like fluorescent markers  in the dark.  And so this is what we show the bioengineers, and they'll say, why doesn't your robot go  there, and I'll say, why doesn't your nanoparticle go there?  So it does make you think about it.  So there you have your cells, the green ones are the particles.  When they bind or get eaten up, they turn purple, so you can see this sort of the outside  of the cell.  And there's lots of room between those two cells, but just by the nature of diffusion,  they never stick to those two cells.  Then what we did is we said, OK, let's tune down the binding.  And if you tune down the binding, they should go deeper.  They should go deeper in that environment.  And so here what you have on the upper right, focus on that one.  And you can see there's one particle that's going to zip by it, and two, and three, and  four.  And no one ever sticks to it, which makes sense, because we've tuned down the binding  so much.  And so eventually one does, because it actually bumps into it.  And then we thought, OK, well, let's try our other strategy, where you don't let them stick,  turn on the binding, and see if that works better.  So essentially you're letting them go in, turning on the binding.  And then they would start sticking.  And so again, you can look at the one on the upper right.  And OK, so the particles are able to diffuse through there.  And then you have one of these, again, those robots at the edge that are coming by.  And so now we're going to turn on the binding.  And you can see that they all bind immediately to that robot.  Some of them here are still moving.  And so we thought, this is what we had predicted with the model.  We didn't need that to understand.  But what we thought is we can inject way fewer particles with this generalizable strategy  than with the other strategy, which is just to tune down the binding, because then you  have a lower probability of binding to the last cells.  And so we went back to the computational models, and in fact you can inject less.  So it's one of these things where literally you take 10 minutes to program the robots,  throw this behavior on all the robots, see something, and I think it is powerful in terms  of understanding dynamics in the physical world.  We've since, at the Bristol Robotics Lab, where I've been for the past year and a half,  built a thousand of these robots as well.  And so that was hard work from the students who have spent essentially, spent two weeks  in the sweatshop.  Granted, I was there as well.  It was really fun.  If you get a chance to do this, it's well worth it.  And so we've got these robots.  And in our arena, we have a projector, and we can project environments dynamically, interact  with these robots.  And so something that we really cared about was to see if we could do experiments on the  robots that were proportionally at scale to the experiments in the nano world.  So if you take a nano particle, and you blow that up to a three centimeter sized robot,  there's a scaling factor there.  And if you do the same thing with time, there's also a scaling factor there.  And so can you take a nano experiment, which is essentially what we were doing in Nanodoc,  and putting them on a swarm of a thousand robots.  And it turns out that you can work out numbers that give us proportionally relevant experiments.  So here you have cancer cells.  These particles would be more in the several hundred nanometer range if you were to scale  them down.  But we are starting to see interesting dynamics.  And something that's come just through these first experiments is that a lot of our simulations  are symmetric in how we build them.  And just to skew slightly in terms of the projector and how close things are, do distribute  your particles in very different ways.  And just seeing that is quite interesting.  And the other fun part is I think that as we're designing these algorithms that could  work on nanoparticles that are in the tenth to the power of thirteen, we're removing a  lot of the assumptions and functionalities that we typically put on robotic swarms.  And I think that's going to allow us to create new algorithms for robot swarms that could  be useful in real world applications.  So rather than having a robot that you could, you know, that would be fancy enough to go  through a forest on its own, could you have many, many very simple robots that could be  able to find a way and then recruit the other robots?  Or could you use them for things like cleaning up an oil spill?  And so we're trying to figure out what algorithms work, work in the thousand robots, because  it is a different, a different type of algorithm.  And really just to prove this point, these are, I think they're thirty robots.  And these are thirty robots in our swarm trying to go straight.  And so, you know, as we simplify these robots and as we scale those numbers up, you're not  going to be able to calibrate all these robots.  They're going to work very differently when you put them in the real world than when you  put them in the laboratory environment.  And so making things or algorithms that are robust to noise like this in terms of the  motion of the robots, I think, is going to be very helpful for bringing things to the  real world.  And part of learning how to do that is to go a little bit back to the roots on the evolutionary  side.  So this here is an experiment where essentially we're trying to evolve both, you know, the  neural network or the program of the robot to do swarm tasks, but also the simulator,  because we don't have good simulators to mimic what these robots do.  If you just put regular noise like you would in a robotic simulator, it doesn't correspond  to anything that we're seeing on these one thousand robot swarms.  So we are having to learn really new tricks to make very large numbers of simple robots  work together.  All right.  And then the last step is really to push this, push this to the nano-scale.  And so what we've been looking at is to create microfluidic devices in which we can visualize  the dynamics of these nanoparticles under the microscope.  And so there's really two parts.  So this one part is to be able to have a vessel layer, essentially where you could release  these particles.  And that's the one filled in red.  This was done over in Boston with Sangita as well.  And then the other part is these tissue layers where you can fill them up with gels and you  can fill them up with cancer cells and start to monitor how these particles diffuse from  the vessel into those chambers so that we can see some of these swarm behaviors.  But even beyond that, we're really interested in being able to fast prototype some of these  environments so that we can take the nanodoc world and put it on the robots and then put  it under the microscope and very quickly do experiments where we might be able to see  these chain formations or this decision making.  And so one way we're doing that is to really just 3D print the scaffolds in which we would  make our microfluidics and then we pour the PDMS and make our usual microfluidics.  So we can very quickly iterate on the design of these devices.  And having the cells in there was quite interesting and relevant, but it is finicky to understand  all the different parameters that are involved.  And right now we're backing out just so that we can understand our environment better and  have something that's well characterized so that we can put instead of cells cell-like  structures, so these are microspheres that are cell-sized, that we can functionalize  with different materials so it looks like the receptors that they have on their surface.  We can inject the nanoparticles and we can make them diffusive or convective so we have  the brain model or we have the more solid tumor model.  And so this is just, these are really just experiments from very recently because we're  finally up and running with our microfluidics.  And the tricky thing is also, you know, you want your tissue to stay put, but you want  your nanoparticles to be able to go through.  And so you need to understand sort of how things move through tissue, how some of it  stays put and how you're able to convect there.  And so we're currently diving in to see a little bit more of how these things interact.  We're really just at the first steps of the experimental side.  But I'm hoping because we have a well-controlled environment that we're going to be able to,  you know, to have the patterns that we see in the nanodoc world and start to see these  interesting aspects.  Another interesting aspect with the nanoparticles and the microfluidics is work that we did with  Brad Nelson and just seeing how, you know, our particles aren't very controllable.  They diffuse.  Yes, you can do things to make them move differently.  But Brad Nelson has a really beautiful ABF system which is much more controllable in  how it interacts.  And so here what you see is an ABF in one of our microfluidic chambers that's full of  nanoparticles.  And here what you're going to see is Simon and me just going through this channel.  And there it's quite interesting because you see these plumes of nanoparticles around the  artificial bacterial flagella, so this micro robot.  And so we were thinking, how could you use these robots which are much more controllable  to interact with these nanoparticle populations?  So now you can essentially use these nanoparticles to maybe, you know, go deeper in a tumor or  tissue to do something smart thanks to the help of the robot that's more controllable.  So creating mixed populations of nanoparticles and these micro robotic systems.  So a lot of what I'm working on is really this idea of swarm engineering or systems  nanotechnology.  And it's very often you know what your group behavior should be, right?  You want to kill all the cancer cells in an environment.  You want to create a communication network.  You want to distribute it over a forest to be able to explore it.  And these are really group behaviors.  And so the tricky thing is solving that puzzle where you go from the group behavior and then  you need to figure out how the individual agents should be designed.  And to do that you need simulations that allow you to predict the emergent behaviors.  And then really this push towards reality.  So either with this large test bed of many robot swarms and then to the micro fluidic  devices so that we can see some of these collective behaviors under the microscope.  And we do work very closely with clinicians who are either doing the brain cancer or who  are doing working on the solid tumors.  So we can take some of these ideas and push them to reality.  And there's really two parts.  One is just, you know, we can answer questions that bio engineers have now.  How does my particle distribute?  And the other is what do we need to be able to make these swarming type behaviors?  All right.  And then just in terms of thank you, there's a lot of people on here.  So obviously the laboratory, the Bristol Robotics Laboratory and the University of Bristol,  Senghi DeBatia's laboratory over at MIT, the VEAS Institute in Harvard, which did the  original KLO bots and everything is open hardware so that allowed us to create our own.  And they've been a wonderful, wonderful help in building up this platform.  The MIT lab, so a lot of the stochastic simulations were done thanks to a tool.  And then we build a whole framework around it to be able to simulate nanoparticles in  cancer.  And then Brandon Nelson's lab for the artificial bacterial flagellas.  All right.  So I think I wanted to also thank the players, hopefully.  And any questions that you may have?  Do you have any questions?  They just get there because of their size.  So essentially it's EPR, enhanced permeability and retention.  And they're leaking out from the vessels into the tumor environment.  No, they're just getting there.  They accumulate there because of their size.  They were also bigger particles.  And so they like to stick around in the tumors.  Yeah.  So it's really chemistry for most of them.  Actually, a lot of what we do in terms of the wealth that functionalized or characterized  devices, we buy them off the shelf.  So you can buy fluorescent nanoparticles, which is why you can see them so nicely.  There's a large variety of particles that you can make.  So some of the ones that my collaborators make are liposomes, which are essentially fatty  bubble.  You can create gold particles, iron oxide nanoparticles, which are going to be magnetic.  You can create polymer-based particles.  So there's really a whole range of particles.  And it's mainly chemistry.  I just have a question.  Could you explain again what specifically are these kilobots allowing you to see that  simulation isn't allowing you?  Yeah.  So, yeah, that's a great question.  I love the simulations.  Most of my work is done in simulation before we push those to reality.  I think what the robots allow us to see is really in 10 lines of code.  That was to see how our rules work on a physical platform, with all the noise that entails  and all the assumptions taken out, because our simulations do make assumptions.  So for us, I think they're more part of our design toolbox than the answer to the question.  So they're just another way to visualize dynamics.  What I'm really interested in is taking those algorithms and pushing them to the robotics  world, because I think that's going to teach us a different way of programming these very  large swarms of robots.  You could go back and make better simulations.  I think that's also a very valid approach.  The question is how long does it take you to make a better simulator versus throwing  them on the robots once they're there?  One thing, I mean, people don't know the answer to that question.  Are the robots really helpful in this design process?  And I think because we have this crowd sourcing tool, it would be really interesting to let  the crowd design their behaviors on the robot swarm, or design them in simulation, and see  just the trajectory of how they optimize these treatments.  And that might give us more quantitative data, because you're right.  Now it's not entirely know how helpful that is.  I love them, but obviously it needs to be a little bit more scientific.  Yeah, that's a good point.  So it really depends on your particles.  So some of these particles are made of materials that are already FDA approved and that are  deemed to be safe.  Even if they go elsewhere in the body, the assumption is they're going to be cut down  and cleared through the natural processes.  The reality is the percentage that accumulates in the tumor is not 100%.  It's going to be 6% if you're lucky.  And so the particles are going to go elsewhere.  So you need to make sure that you've done the toxicology and you understand what happens  to those particles if they're elsewhere.  And that's what we call sort of the passive accumulation, just because of their size,  a lot of them go to the tumor.  But then you have active targeting.  So this idea of having receptors on the surface or targeting ligands on the surface that bind  to receptors.  And that's a little bit more specific in that they need to bind for the cancer cells to  eat them up in higher probability.  So that gives you an additional handle to target the cells.  But you do need to do the toxicology.  I have an add on question.  You showed the molecule that is able, with an extra coating, is able to release it.  So when does the molecule know when to release that?  Yeah.  So there's two different types of particles that I know of that do that.  One of them is pH-based.  So essentially they arrive in the tumor environment and the tumor environment has a different  pH.  And so that allows the particles to shed the coating.  And the other one is enzyme-based.  So we draw enzymes as Pac-mans because they go and they cut stuff up.  And so in the tumor environment, you also have enzymes that are a bit of a signature  of those tumors and they can go and cut off those pegs.  And can you really program them?  Can you say, okay, I want this pH to release it or is it just luck that you might release  it?  No, so they spend a lot of time.  So obviously once you have these ideas of how to make them, then the material scientists  in the chemists spend a lot of time finding the particle that can do that.  But they, yes, so they can engineer them to do that.  Yeah.  Just another question, has it ever been tried and invalidated or whatever to actually control  these particles with electric or magnetic fields coming from the outside?  Did you connect the decision where you guys...  Yeah, yeah. ...by chemistry going?  Right.  So Brad Nelson's obviously the expert on the magnetic side.  I think what I know more about is the triggers.  So you can shine light on them and trigger them using light.  So they're more of binary activate now or do something now than the very fine control  of these nanoparticles.  But there are definitely researchers who are working on that aspect.  So I think that's from a robotics and control perspective is really interesting because  now you have much more of a robotic system in terms of closed loop and being able to  control what's happening in there.  So yes, that's definitely an interesting way to go.  What happens to the particles after they're destroyed?  So well, it depends again on the particles.  So the idea is for them to essentially kill those tumor cells and then have them deplete.  And then, yeah, again, you need to do the toxicology to see what happens with the particles  further down.  So it depends on the type of particle, basically.  If they cut down, then they're going to end up in the urine.  So it's really different pathways for different types of particles.  Okay.  Aperol?  Yeah.  So thank you very much, Sabine.  I'd like to, in the name of the mobile house systems labs, which I'm headed, I would like  to know if you could please follow me up, which is where it always is.  And let's thank Sabine again for the cameras and for the information.
From_Swarm_Robotics_to_Nanomedicine_0_1427.png From_Swarm_Robotics_to_Nanomedicine_1428_1751.png From_Swarm_Robotics_to_Nanomedicine_1752_2759.png From_Swarm_Robotics_to_Nanomedicine_2760_3071.png From_Swarm_Robotics_to_Nanomedicine_3072_3707.png From_Swarm_Robotics_to_Nanomedicine_3708_4691.png From_Swarm_Robotics_to_Nanomedicine_4692_4847.png From_Swarm_Robotics_to_Nanomedicine_4848_5231.png From_Swarm_Robotics_to_Nanomedicine_5232_5375.png From_Swarm_Robotics_to_Nanomedicine_5376_5471.png From_Swarm_Robotics_to_Nanomedicine_5472_6767.png From_Swarm_Robotics_to_Nanomedicine_6768_7103.png From_Swarm_Robotics_to_Nanomedicine_7104_7379.png From_Swarm_Robotics_to_Nanomedicine_7380_10799.png From_Swarm_Robotics_to_Nanomedicine_10800_11207.png From_Swarm_Robotics_to_Nanomedicine_11208_12527.png From_Swarm_Robotics_to_Nanomedicine_12528_13319.png From_Swarm_Robotics_to_Nanomedicine_13320_15731.png From_Swarm_Robotics_to_Nanomedicine_15732_16187.png From_Swarm_Robotics_to_Nanomedicine_16188_16607.png From_Swarm_Robotics_to_Nanomedicine_16608_19091.png From_Swarm_Robotics_to_Nanomedicine_19092_19559.png From_Swarm_Robotics_to_Nanomedicine_19560_19991.png From_Swarm_Robotics_to_Nanomedicine_19992_20915.png From_Swarm_Robotics_to_Nanomedicine_20916_22463.png From_Swarm_Robotics_to_Nanomedicine_22464_23243.png From_Swarm_Robotics_to_Nanomedicine_23244_23531.png From_Swarm_Robotics_to_Nanomedicine_23532_24503.png From_Swarm_Robotics_to_Nanomedicine_24504_24659.png From_Swarm_Robotics_to_Nanomedicine_24660_26435.png From_Swarm_Robotics_to_Nanomedicine_26436_27875.png From_Swarm_Robotics_to_Nanomedicine_27876_29567.png From_Swarm_Robotics_to_Nanomedicine_29568_29651.png From_Swarm_Robotics_to_Nanomedicine_29652_29759.png From_Swarm_Robotics_to_Nanomedicine_29760_30239.png From_Swarm_Robotics_to_Nanomedicine_30240_30683.png From_Swarm_Robotics_to_Nanomedicine_30684_35663.png From_Swarm_Robotics_to_Nanomedicine_35664_38903.png From_Swarm_Robotics_to_Nanomedicine_38904_40487.png From_Swarm_Robotics_to_Nanomedicine_40488_43355.png From_Swarm_Robotics_to_Nanomedicine_43356_45203.png From_Swarm_Robotics_to_Nanomedicine_45204_47135.png From_Swarm_Robotics_to_Nanomedicine_47136_47807.png From_Swarm_Robotics_to_Nanomedicine_47808_48383.png From_Swarm_Robotics_to_Nanomedicine_48384_49379.png From_Swarm_Robotics_to_Nanomedicine_49380_55931.png From_Swarm_Robotics_to_Nanomedicine_55932_56339.png From_Swarm_Robotics_to_Nanomedicine_56340_58631.png From_Swarm_Robotics_to_Nanomedicine_58632_60407.png From_Swarm_Robotics_to_Nanomedicine_60408_61187.png From_Swarm_Robotics_to_Nanomedicine_61188_61487.png From_Swarm_Robotics_to_Nanomedicine_61488_61811.png From_Swarm_Robotics_to_Nanomedicine_61812_64183.png