I want to talk today about bioelectricity, but more specifically than simply yet more diverse biophysics that you have to add in order to understand development and things like that. I want to paint bioelectricity as a really important link that allows us to take the insights of cognitive neuroscience and apply them far outside of brains and neurons. In other words, what I think is really special about bioelectricity is not just the mechanisms, but the role that it plays in scaling up processes and properties that we typically associate with cognition, with learning and memory and things like that. So that is what we're going to talk about today. If you want to see any of the details, all the papers, the datasets, the software, everything is available here at this website. And then this is my own personal blog around what I think some of these things mean. So I want to start out by thinking about how we talk about machines and organisms.
So what's going on here is that this is a shot of — and don't worry, the vast majority of my talk does not look like this. But you see what's going on here is this material is being really micromanaged. In other words, this is the kind of thing you would do in carpentry. You've got some chisels and some hammers and some screws and you're putting everything where it needs to go and then eventually it's going to look like this. So some part of this biomedical approach here is very much micromanagement in terms of we're going to treat this as a machine. We know what all the parts do. We're going to assemble it exactly how we want it. But then the patient is sent home to heal, and what is that part, right? That is really interesting. What happens after that when we really rely on the autonomy of the material. In other words, we're going to let the system do what the system does. We're
not going to try to micromanage it. And in fact, we don't even know a lot of what it does, but we have some degree of trust that it's going to do what it needs to do. And this is actually — I won't have time to talk about this today — but there's very interesting work in the study of placebo effects. And Fabrizio Benedetti has this amazing quote where his work shows that words and drugs have the same mechanism of action. And this reminds us that high-level information flows eventually have to impact the physics of whatever system you're talking about. And that interface between information and physics is where I think some really interesting and deep questions lie. And bioelectricity can help us. So let's think about what the endgame of developmental biology, bioengineering — what does all of that look like? You know, when can we assume we're done? We've done our job and we can all sort of rest easy. Well, the way that I envision it is something I call an anatomical compiler. So someday you will sit in front of a computer and you will be able to draw the plant, animal, organ, biobot, whatever the living construct that you want. You won't be describing it at the level of molecular pathways. You will be describing it at the level of large-scale form and function. You're simply going to draw what you want. And if we knew what we were doing and if we had a system like this, what it would be able to do is to compile that description into a set of stimuli that would have to be given to individual cells to get them to build exactly what you want. Now if we had the ability to do that — that is, communicate large-scale anatomical goals to groups of cells — we would solve birth defects, traumatic injury, cancer, aging, degenerative disease. All of these things would go away if we knew how to give new anatomical goals to groups of cells. And I think it's important to note that I don't think this kind of a thing is something like a 3D printer where you simply put the cells where you want them to be. This is not that. This is a communications device. It is a translator from your goals as the engineer or the worker in regenerative medicine to that of the collective — of the cellular collective. It's how do you get them to build the thing you want them to build. And I want to just remind us that — and I'll raise this theme a couple times in this talk — is that the typical information that we all focus on, genetics and biochemistry, is not enough. So here's a very simple example. Here's the larvae of an axolotl. Baby axolotls have little four legs. Here's a tadpole of a frog. They do not have legs. In our lab, we make something called a frogolotl, which is basically a chimeric combination of these two creatures. So I ask you a question: will frogolotls have legs or not? You've got the genomes. You've got the axolotl genome. You've got the frog genome. Can you tell me if frogolotls are going to have legs? And the answer is no. There's currently no model that exists that allows you to look at this genetic information and know what's going to happen in this chimeric decision. Because while we are very good and getting increasingly better at manipulating cells and molecules, we are really a long way away from understanding large-scale decision-making. What that means is not only can we not predict outcomes in various novel scenarios — to be fair, we can't even predict outcomes in the standard scenario. You know, if you didn't already know what a Xenopus tadpole looked like, or if you didn't compare that genome with some other genome where you do know what it looks like, you would have no idea how to derive the actual anatomy from the genetic information. And one of the consequences of that for medicine is that with the exception of antibiotics and surgery and a couple of other things — you know, a couple of other recent technologies — we really don't have anything that fixes things. You know, typically the treatments we have target the symptoms. They suppress the symptoms, ideally for as long as you're taking the drug, and then if you stop, everything sort of comes right back. So here's what I think is going on. I think that molecular medicine is still stuck where computer science was in the 1940s and 50s. This is what it looked like to program back then. She had to interact with the hardware. She's literally rewiring this thing because everything was about the hardware. And now this is where we are. We have genomic editing and pathway engineering and all these kinds of things. But mostly all the exciting advances are at the level of hardware. We have barely begun to understand the high-level information processing and, in particular, aspects of problem solving, aka intelligence, in the material. So what I want to point out first and foremost is that, you know, this notion that we all learn in our biology classes that good models are cashed out in terms of chemistry — at that level, nothing knows anything. The system does not know where it's going. It doesn't have any kind of a goal. It just sort of does what chemistry does, and then it's up to us to figure out what emergent outcomes are going to come. So these are kind of a class of feed-forward, open-loop models. And what I want to point out is that this is not a result — that is not an experimental result. It is not a necessary axiom. It is an assumption, and that assumption needs to be tested. In other words, is it really the case that when we deal with cells and tissues, we are only going to be using models of devices that don't know anything and don't have set points and goals and things like that? Or maybe that doesn't fit the data very well, and I'm going to argue that it doesn't fit the data at all. And so we actually have a spectrum here. I call it the axis of persuadability that reminds us that we have a wide range of tools, right? Starting from rewiring, of course, but also cybernetics and control theory and the tools of behavioral science, where we're dealing not with magic, not with impenetrable mysteries, but with kinds of processes that, since at least the 1940s — and for behavioral science long before that — we've had ways of addressing mechanisms that actually have goals, have memories, know things, and so on. And so the question is how much of that applies to the biology that we care about? And you can see this worked out carefully in that paper. And so, you know, all of us take this journey, right? We start out being the subject of chemistry, and then so we're a little unfertilized oocyte, and eventually there's some developmental physiology, and then some behavioral science, and maybe psychology and psychoanalysis, and maybe we take a detour into oncology or bioengineering. I'll talk about this momentarily. And so the disciplines are distinct, and the journals and the departments and the funding bodies are sort of all different, but the substrate is continuous. And so what we really have here is a kind of scaling of competencies. And if you think that the final product — so let's say an adult human or an adult animal — has certain properties, what we're looking for is a story of transformation. How did we get there from a little blob of chemicals? And if you think that there are great transitions, so sharp phase transitions where something critically new happens, you have to argue for that. You have to say how exactly does that happen? Because the substrate is actually a slow, continuous developmental process. And so for this reason, I think developmental biology is kind of unique among the sciences because here you see the journey from matter to mind. You start with chemistry and you end up in psychology. And this is slow and gradual with no magic lightning flash in the middle that converts chemistry to mind. So what I'm going to do today is try to make the following points: that one thing that's interesting about bodies is that they consist of a multi-scale competency architecture. There's actually problem solving at every level, all the way from molecular networks up. That definitive regenerative medicine is going to require us to understand these systems as cybernetic goal-seeking systems. In other words, they have set points and they have various degrees of ingenuity of meeting those set points when circumstances change. I'm going to show you how we use bioelectrics to read and write those set points. So these are actually pattern memories in that living medium, and we're going to be able to read and write them. And of course, applications in all of these kinds of things. And so my argument overall — if you remember nothing else from today, I hope you remember the claim that bioelectricity does exactly what it does in the brain. It is a cognitive glue. It's a set of mechanisms that enables the collective to know things that the parts don't know. Just like you know things your individual neurons don't know because of electrophysiology, that is what developmental bioelectricity does, but it's more ancient and it does it in the rest of the body. So this is the kind of thing we're made of — individual cells. So here's a unicellular organism called a Lacrymaria. No brain, no nervous system, but, you know, very significant competency in its own local environment. So that's the kind of thing that we have to sort of tame if you're going to make a multicellular body that does interesting things. Actually, even this is not where things start, because what it's made of is a set of chemical networks. And so these might be gene regulatory networks, they might be molecular pathways. But one of the interesting things about these networks is that they exhibit six different kinds of learning. You don't need a neuron, you don't need a brain, and you don't even need a cell. Just the chemical networks alone, and in fact very small ones — you only need about five nodes in certain cases to do it. They do habituation, sensitization, associative conditioning. You can find all of that here. So even the material already is capable of some very interesting behaviors. We are currently taking advantage of that to try to train the pathways. So applications in drug conditioning and things like this, where you can condition very powerful drugs to inert triggers and so on, because the material itself is able to associate different kinds of history of stimuli with specific responses. And so this goes all the way down. And so that means that we have to extend — or at least we have the opportunity to extend — some of the tools of behavioral science to systems that operate in very unusual spaces. So typically we use those kinds of tools to study things that move around in three-dimensional space. So this is conventional behavior. So you see birds or mammals or, you know, maybe an octopus or something doing interesting things in 3D space, and we know we have tools to recognize when they're solving problems, when they're pursuing goals, and so on. But biology has been doing this long before nerve and muscle appeared. So cells navigate a high-dimensional transcriptional space. They navigate a physiological state space, and my favorite, they navigate anatomical morph space. So what I'm going to claim is basically that morphogenesis — whether it be in development, in regeneration, and cancer suppression — morphogenesis is a kind of behavior, and it is the behavior of a collective intelligence, just like you and I are, where groups of cells and neurons and other things. So it is the behavior of a collective intelligence as it navigates anatomical space. And anatomical morphospace is simply the space of all possible anatomical layouts that a system can have. So let's talk about where anatomies come from. So we all start life like this, a collection of embryonic blastomeres. Eventually, through a cross-section, you see something like this — this incredible order. Everything is the right size, the right shape, next to the right thing. Where does this actually come from? So most people will immediately say, well, it's in the DNA, it's in the genome. But we all know now that the genome specifies proteins and it specifies some timing information about how they appear. But actually none of this is directly in the genome. The genome doesn't say anything directly about your anatomical layout. So you still have to ask how do these cells, given the molecular hardware that the genome does encode — so the genome tells you what proteins and different types of computational machinery you get to have. But after that you have to start asking what the software looks like. How does it know what to build? How does it know when to stop? If something is missing, how do we get it to rebuild? As engineers — and I'll only talk about this briefly — we might ask it to build something completely different, right? Can the same genome build something totally different? And so we just have to remember that the standard anatomy is no more in the genome than the shape of termite colonies or the precise shape of spiderwebs is in these animals' genomes. Right? These are all outcomes of the physiological software that rides on the genetically specified hardware. And so now we have to ask what are the properties of that kind of computational layer. And I'm going to use a definition of intelligence by William James to simply focus on goal-directed activity, meaning the ability to reach some sort of set point. Now you can think about this at the lowest level as simple homeostasis, right? Your thermostat has the very basis of goal-directedness. It has a set point. It represents that set point physically and it tries to manage a set of variables to try and reduce the error to that set point. So let's ask the question of is that something that we see in cell and developmental biology, or really is just kind of open-loop emergence something that's going to explain everything we need to know? Well, the first thing we know is that development is very reliable, right? So you start off like this. You almost always end up with the right species-specific target morphology. But that is not why I'm using the word intelligence. It is not because it's reliable and it is not because there's a rise of complexity. That is not my point. My point is that — which I'm going to show you momentarily — about the high competency of navigating anatomical morphospace in unexpected conditions. See, this normal development really, I think, hides a lot of interesting capabilities. So the first thing we know is that if we cut embryos into pieces, you don't get half bodies. You get perfectly normal monozygotic twins and triplets and things like that. And so we know that you can start off in different regions of that anatomical morph space. You can avoid various local minima and you will eventually get to this correct ensemble of goal states. So that's one thing. Same anatomy from different starting states. Of course it's not just for embryos. Many animals, such as this axolotl, can do this throughout their lifespan. So you can cut the limb anywhere along this axis. The cells very quickly detect that there's been a deviation from the correct goal state. They will reduce that error and eventually they stop. When do they stop? They stop when the correct salamander limb has been completed. And so again, you can do this from different starting positions, but what it does is it's basically anatomical homeostasis and an error minimization scheme. But there's something very interesting here. This is not just about damage, okay? This is not a story of simply minimizing damage. In fact, you can even think of development as just a kind of regeneration, right? You're just restoring the entire body from one cell. The whole thing is about — you might think the whole thing is about error minimization. But there's one other aspect to this that's super, super cool and needs more attention. This is an old experiment from the 50s where they took a tail and surgically grafted it to the flank of an amphibian. And what you'll see is that over time that tail remodels into a limb with fingers. Now take the perspective of these cells at the tip of the tail here. They are tail-end cells sitting at the tip of the tail. There's nothing locally wrong with them. There is no damage. There's no injury. Why are they turning into fingers? No individual cell knows what a finger is. But in some way they are responding to something that at their level doesn't exist. In other words, the body plan of an entire animal. What's happening here is that a large-scale error detection system that recognizes that actually having a tail here is not what is supposed to be happening. All of that error then propagates down into the molecular steps that are needed to turn this structure now into this structure. Where have we seen this before? Well, we've seen it before in human — or in fact in any kind of behavior — when you wake up in the morning and you have very abstract goals. You might have social goals, financial goals, research goals. In order for you to get up out of bed and do those things, ions have to cross your muscle cell membranes. In other words, the chemistry of your body cells has to be literally affected, functionally changed by these incredibly abstract, high-level goals that the large-scale system has that the cells don't know anything about. And one of the most interesting things about bodies is that they form this transduction network that allows this incredibly high-level abstract kinds of things to serve as drivers of chemistry underneath. And that is what's happening here. The fact that an amphibian is supposed to have a limb and not a tail here is what ultimately drives the molecular biology of this transformation. So the local order obeys a global plan. That's what's important here. Now, there are lots of examples like this that are not in our developmental biology textbook, mostly because they're very difficult to explain with current tools. Here's an example. This is called trophic memory in deer antlers. So deer, large adult mammal that every year has this incredible structure of bone and vasculature and innervation. And they lose these every year and then they regrow them from scratch. And so what you can do is cause an injury here, or you cut into the bone, it makes a little callus, it heals, no problem. This whole thing falls off. Well, for the next five years, in certain species, that location will have an ectopic branch, an ectopic tine, and then eventually it kind of goes away and it goes back to normal. Now, think about what this means. The information as to the location of the damage has to be stored somewhere in the body because this whole thing's going to fall off. So it's not known where it is, but it has to be stored somewhere in the body. And then when the bone is growing, by the time you get to this, there has to be a new signal that says, 'Oh, by the way, take an extra branch point here because there was damage last year. We had some damage here. Take a branch point.' So just think about what kind of molecular pathway model you would draw for something like this. You know, we're all used to making these arrow diagrams that are like figure seven in your Cell paper. Like this is just — we don't have really good tools to describe things like this. And this is what the material is capable of. It has a pattern memory. It can store and recall these memories from distant locations and it can guide morphogenesis in accordance to that pattern memory that does not have to be the default that you see from the species most of the time. It can be changed, and I'm going to show you some examples of this. And we have in our lab the guy who discovered this, Bubenik, sent us — you know, a 35-year — this is a 35-year-old, year-long experiment. Imagine trying to tell your PhD advisor you want to work on a herd of deer for 35 years. But he sent us all these antlers and just an amazing, amazing dataset. So just one more example in a much more tractable model system that we identified. This is a tadpole of the frog Xenopus laevis. So here are some eyes, here's some nostrils, the mouth, the brain is here, the gut. And when these guys become frogs, they have to rearrange their face. So all these different organs have to move to look like this. Now, you might think — and it was thought for a long time — that what's somehow genetically encoded is basically a set of hardwired movements. Everything moves in the right direction, the right amount, and you go from a tadpole to a frog. Well, we decided to test that. And the way you test it is you perturb the system and see what it does. And so we created these so-called Picasso tadpoles where we scrambled the face. Everything's in the wrong place. The eye's on the back of the head, the mouth is off to the side, and everything's in the wrong place. But it turns out they make perfectly normal frogs. And so all of these things move in novel, sort of unnatural paths. In fact, sometimes they go too far and have to come backwards until you get to a correct frog and then everything stops. So what the genetics actually specifies is some hardware that can execute a very flexible error minimization scheme, right? That takes corrective action as needed to get to a specific outcome. And so now the most obvious thing — I mean, there are many questions one can ask about this — but the most obvious question for all of these things is how do you know when you've reached the correct state? So how does a regenerating limb or a developing embryo or a deer antler that's been modified by experience — how do any of these things know what the correct pattern is? And I'm going to show you one way in which set points, anatomical target states, set points can be encoded in tissue. Now probably the neuroscientists among the audience are probably already thinking, well, we know how — we have an example that does this, and that's the brain. We know in the brain that animals store set points as memories and they execute behavior in accordance with those memories to reach specific states. And how is that implemented? Well, there are these ion channels that enable the network of cells in your brain to be electrically active. And so there are ion channels that set voltage gradients. There are gap junctions or these electrical synapses that allow these signals to propagate across the network. And then so this is what it looks like. This group did this amazing video of zebrafish brains while the fish is thinking about whatever it is that fish think about. And you can then try to do this neural decoding. You can say, okay, I'm going to read this electrophysiology, and we try to decode it to try to understand what are the memories, the goals, the preferences, the behavioral competencies of this animal. In other words, we know from neuroscience that the cognitive states of beings are encoded in the electrophysiology, the real-time electrophysiology of certain cells. Now, where did this amazing trick come from? I mean, this is what allows you to be a continuously persisting complex agent that at any given moment in time is connected via memories to your past and your future selves. How — you know, where did this incredible system come from? Well, it turns out that it is extremely ancient. Basically all cells in your body have ion channels. Most cells in your body are connected by these electrical synapses. And this design feature, this architecture by which you can store and integrate information across space and time electrically in electrical networks is here from about the time of bacterial biofilms. Back from the time that bacteria were first getting together into colonies, that is when evolution discovered the amazing suitability of bioelectricity as a cognitive glue, as a way to connect competent subunits into systems that know things that the subunits don't know. And so we asked — and so this has now been over 20 years ago — we asked, could we take the tools of behavioral and neuroscience and apply them outside the brain to do a kind of neural decoding except to non-neural cells and ask what do they know? What information do they store? How does the collective store pattern memories that no individual cell has? And I'm going to show you how we do that. So, and this is — so this is, as I said, was a zebrafish brain. This is an early frog embryo that we are monitoring basically in the same way. So the first thing we did is to use a fluorescent voltage dye technology to be able to read electrical states. So you can see these are individual cells, cells in vitro. Colors represent different voltages. And you can actually see the spatial and the temporal patterns of biological states in these cells. We do a lot of quantitative simulation. So we start with the gene regulatory network that tells you which channels and pumps you're going to have in your cells. Then you can do large-scale tissue-level simulations that allow you to do things like make cuts and damage the tissue and make certain changes and actually ask what's going to happen, right? What is the behavior of this network? You can't do it in your head. It's not obvious at all. They have very sort of complex and interesting properties. I'm going to show you a couple of examples in development. So this is something we call the electric face. So here's a time-lapse of an early frog embryo. Again, the grayscale represents a voltage here using this dye. And there's kind of a lot going on, but look at this one frame. So basically before the genes turn on that regionalize the ectoderm of the embryo to become a face, already you can read out the electrical prepattern that tells you here's where the right eye is going to go, here's where the mouth is going to go, the placodes are out here. It already lays out the basic features of the face. And this voltage pattern, right, the spatial pattern of steady resting potential across the cell membrane is what basically determines the genes that are going to turn on and the regionalization of the face. Now, not only is bioelectricity a way to merge individual cells into a large-scale structure, it actually does that in multiple levels because these are individual embryos. And if I poke this one, all of these guys find out about — you can see this wave, this calcium propagation wave. If I poke this one, they don't even have to be touching each other. And in minutes, these guys all find out. So, and we have some interesting data that if anybody wants to ask about, we can discuss about hyper embryos. And hyper embryos are basically groups of embryos that solve problems that individual embryos can't solve. So there's a multi-scale kind of a hierarchy going on here. You know, these improvements in technology have been really powerful. Like I always like this picture. This is what's been happening in astronomy. This is what Pluto looked like in 1996, 2002. By 2017, look at this. You can actually see some mountains and some features. And that's what's happened in our ability to track biological states. This is what the eye spot looked like in 2012, right? We just basically knew that it was there. But by now we can actually see the very complex patterns of this region. And the technology is just developing more and more. But in addition to simply being able to read out all the biological states without having to poke each cell with electrophysiology electrodes, functional experiments are really critical. So how do we — so we can read the electrical information. How do we write the electrical information? Well, we don't have any kind of — we don't use electrodes or magnets or applied fields or anything like that. There are no waves, no frequencies. What we do is we manipulate the natural interface that cells use to hack each other. In other words, we control the ion channels and we can do that with drugs that target specific types of channels and pumps. We can do it with
Optogenetics and tools like that. There are also some nanomaterials and so on. And of course gap junctions too. So we can control the topology and the actual voltage of individual cells. And now it's time for me to show you what happens when you actually do that. Now one of the interesting things that we have to watch is the ability of cells to control each other electrically. And so I'm just going to show you one interesting example. So this is a video. I'm going to play this video. The voltage again is denoted by the colors. These are cells in vitro. Notice that this cell starts out as one voltage until it gets touched by this cell and then it changes. So here's the... so it's crawling along. It's minding its own business. Nice and blue. This thing touches it. Bang. That's all it took right there. A tiny little touch like this and the cell voltage has changed and now it comes down and becomes part of this group and starts working on some novel morphogenetic thing. So here's the idea. This is what we would like to understand. We would like to understand this kind of ability to control cell behavior electrically and to tell them what to build. And so here's a couple of examples. We could look at lots. I'm just going to show you a couple. So one of my favorite examples is the induction of ectopic eyes. So you might see in your developmental biology textbook that only the anterior neuroderm up here is competent to make eye. Well, that's only true if you use chemical or molecular genetic inducers like Pax6, the so-called master eye gene. But actually it turns out that most regions in the body are capable of doing it. If you know the right prompt, the right prompt is that bioelectrical state, that little spot that I showed you in the electric face. We can induce that anywhere by injecting RNA encoding specific ion channels. Let's say in this case potassium channels that were used. And when you do this, those cells get the message to build an eye. And they do. They build an eye. That eye has all the lens, retina, optic nerve, all that same stuff that it's supposed to have. Now notice a couple of interesting things. First of all, this biological signal is instructive. In other words, it actually controls... we're not just screwing up development with a poison. We're actually building new and coherent structures. It's highly modular. We didn't have to talk to the individual stem cells. I have no idea how to micromanage the production of an eye. We didn't have to tell it which genes to turn on. Much like that top-down control that I showed you from the abstract goals in behavior or the body plan of the amphibian, that we can communicate at the highest level. We can say build an eye here. That's the trigger and everything else that's needed is taken care of downstream. In fact, lots of cool features to that. For example, if we only inject a few cells. So here, this is a cross-section through a lens in the body of a tadpole. Only a few of them were injected by us. But what did they do? They apparently can tell that there's not enough of them to really build an eye. And so they recruit their neighbors. They tell their neighbors, which were never touched by us. So all these brown cells were never injected. These guys get them to participate in the process. It's like a secondary instruction event. Now, interestingly enough, they will start an ectopic eye spot. So this is an earlier frog embryo and you can see the blue is in situ hybridization marking Rx1, which is a very early marker of eye field specification. So we can start all kinds of eye spots but only some of them will become an eye. You can in fact make eight or nine of these but only some of them will become an eye. Why? Because while these guys are telling their neighbors, work with me to build an eye, the neighbors as part of a cancer suppression mechanism are saying no actually you should be skin like us or you should be gut or something else and they basically cause them to change their mind and despite their early expression of Rx1 that winks out and they go back to normal. So that back and forth conversation about what are we going to be? Are we going to be an eye or something else takes place at the electrical level that eventually gets canalized into gene expression and then eventually into anatomy. And for regenerative medicine, this is what we would like to have control over. We would like to be super convincing to these cells. We would like to be able to give a high level stimulus. We don't want to know the 20,000 different genes that we're going to have to turn on and off. We want to give a high level stimulus to say build this structure and have the material do it. As an example, I'll just show you some of our work on limb regeneration. So frogs unlike axolotls do not normally regenerate their legs. And so after an amputation 45 days later there's basically nothing. Well we came up with a cocktail that we apply right after injury or in fact even a little bit delayed. And what it does is it immediately triggers regeneration. So within 48 hours you have an MSX1 positive blastema. By 45 days you've got some toes. You've even got a toenail and eventually a very respectable looking leg that is touch sensitive and motile. You can see the animal can feel. Now, one of the most interesting things about this is that and here this is an example on an adult frog. The treatment that we do, so this is in this case it was a wearable bioreactor containing some drug payload. The treatment is 24 hours. That's it. After that, you get a year and a half of leg growth during which time we don't touch it at all. So again this is a top down trigger-based kind of thing where we say to the system in the first 24 hours go down the leg building path not the scarring path and then we completely take our hands off the wheel. This is we are not using scaffolds or stem cell kinds of approaches or growth factors or any of that. It's right at the beginning the physiological decisions about what journey you're going to take through that anatomical space. And so I have to do a disclosure here because David Kaplan and I started this spin-off called Morphoceuticals where we are now trying to do this in mammals. And so stay tuned for that work is going. We have these bioreactors through which we're trying to apply these kinds of signals. So I want to switch gears for a moment and show you a different model to again hammer this notion of memory. Specifically I made this claim earlier on that morphogenesis has an encoded set point towards which it is trying to reduce error that is a homeostatic system and I want to show you another example of rewriting that pattern memory. So these are planaria and these are flatworms that have a head and a tail and one of the many cool things about planaria is you cut them into pieces each piece regenerates a complete worm. So, you might ask the question, when I cut this piece, how does it know how many heads to have? Okay, how many heads should it have? Because, of course, these cells up here are going to make a head, but so are these cells here, but these cells are going to actually make a tail. And they're right there. They're right next door neighbors, right? It's not an issue of being at a different positional information or anything like that. If they're at the same location, but they make a context appropriate decision in each fragment of what they're going to make. How do they know? And so we asked that question and we observed there's a biological pattern in that fragment that says one head one tail. And what we can do is change that pattern. And so we can using ionophores and some other tools we can say actually you should have two heads. And when you do this first nothing happens. So this animal has this incorrect internal representation of what a correct planarian should look like. But the molecular biology is correct. In other words, head marker in the head, not in the tail. The anatomy is correct. Head, tail. But if you cut this guy, all the pieces will make two-headed animals. In other words, this is a counterfactual memory. It is not true right now. It is latent because until you cut it, it doesn't do anything. But when you cut this animal, the cells consult this pattern as the sort of recorded ground truth of what a correct planarian should look like. And that is what they build to. And so that's what they do. They make these two-headed animals. And so you can see here literally we are looking at the representation of what the axial makeup of a planarian should be. And a normal body can store at least one of two different representations. Now another reason I keep calling this a memory is because it is stable. Once you change it, the tissue holds. In other words, we can take this two-headed animal. We can cut them into pieces and what they will do is continue to generate two-headed animals in perpetuity as far as we can tell forever. Here's a little video of what they're like. Remember there's nothing wrong with the genome. Okay? We haven't touched the genome. The genetic information has not been changed. What the genome actually gives you is some hardware that when the juice is turned on, so to speak, it reliably takes on a default bioelectrical state which by default is one head, one tail. But that is not the only state it's capable of. It can be rewritten and once it's rewritten, the memory holds. So this is like the basic properties of any memory. It's long-term stable, but it's rewritable. It has conditional recall. It has discrete behaviors that it can do and so on. Now I raised this issue of being convincing to the cells. In other words, producing a signal whether endogenous or applied by us that the cells are actually going to take on. We're still really working on some of the very important and puzzling aspects of this. For example, it doesn't actually in order to be convincing that message doesn't actually have to have a lot of tissue behind it. So, we can take a little chunk out of a two-headed animal, in fact, irradiate the heck out of it so that there are no stem cells in it, implant it into a normal one-headed host, and in some percentage, you know, almost a fifth of the cases in this fragment, they turn into a two-headed animal, right? So you can like even a small piece and even at a distance, some of these will still do it. Like that message will overwrite the endogenous memory of all these other tissues. You know, very interesting and not only the number of heads but actually the species specific shape of the heads. So for example this triangular species if we change the bioelectrics that control head development we can get flatheads like this. We can get round heads like this. S. mediterranea, about 100 to 150 million years evolutionary distance between these guys and these guys but no problem this hardware is perfectly happy to visit these other attractors in anatomical space where these species normally hang out not just the shape of the head but the distribution of stem cells the shape of the brain just like these other species right so this is not hardwired. You can go further and you can actually make things that don't look like planaria at all. I mean, they're not flat. You can make these crazy spiky things. You can make cylindrical and hybrid forms. Not just animals, plants do it, too. So, here's what an oak leaf looks like. And you might think that this is what the oak genome knows how to do, but actually along comes this bioengineer. It happens to be a wasp. Lays down some prompts and basically hacks the plant cells to build this incredible gall like this spiky yellow and red thing. We would have no idea that the plant cells are even capable of building something like this if we hadn't seen this. So the reliability of development is deceiving. It hides a lot of plasticity and reprogrammability. We are obviously not the only ones that evolution noticed all this. And much of it takes place with high level interfaces, not with micromanaging the molecular details. And so one of the things that we're trying to do now is build a full stack computational platform that starts off with gene expression data, then goes to the physiology of individual cells, then tissue level, and eventually to whole body algorithmic understanding of the decision-making of how do these things in fact encode different kinds of structures. And once we can connect all of that, we can use these approaches to actually pick electrosuticals. In other words, design stimuli that get the tissue to do what you want them to do. And I will just show you one successful example where we've done that for a complex structure like the brain. So here's a frog brain with forebrain, midbrain, and hindbrain. If you hit it with a teratogen like nicotine or other nasty things, you will get defects. And we wanted to know what's going on and how do we fix these defects. So we built a computational platform. So our collaborator Alexis Pietak and Vip Vyas built this bioelectrical model of the tissue from which the brain arises. And then what we did was we decided to really go after like one of the most unlikely examples that we would be able to fix and that is a genetic mutation of Notch. So Notch is an important neurogenesis gene. If you introduce a dominant mutation, so this is the overactive Notch ICD. Then here's what you see. The forebrain is gone. The midbrain and hindbrain are basically a bubble of water. These animals have no behavior. Profoundly defective. We asked the model, okay, we know what goes wrong with the bioelectrics once you've done this. How can we fix it? And the model said, well, it turns out that there's this specific channel called HCN2 that what it does is it will sharpen the bioelectric pattern back to normal. Okay? And if you do that, then even animals that are expressing high levels of this Notch ICD, you get normal brain structure, you get normal brain gene expression, and you get normal behavior. In other words, their IQs are indistinguishable from controls. And so, and you can do this either by opening existing HCN2 channels or introducing new HCN2 channels. It doesn't matter. So, what you're seeing here is that with the right computational model, we can address the bioelectrical layer to overcome in some cases even hardware defects. In other words, a genetic mutant being introduced. In some cases, and I'm not saying this is all going to work in all cases, but at least in some cases the hardware is fixable by physiological stimuli. And so this is what we're aiming for is a kind of system where we know what the correct state is supposed to be. And so this is where a lot of the hard work now has to take place is to characterize what the normal bioelectric states of different organs are. We then might have an incorrect state and we have a computational platform that says if you want to go from the incorrect state to the correct state, what channels do you need to open and close? Which means what ion channel drugs do you deliver on what schedule? And you can play with an early version of this at this website. Okay. So just a couple of things before I start to wrap up. I'm going to show you a couple of other stories. One has to do with cancer. So I've shown you regeneration. I've shown you organ formation. I've shown you birth defects. Let's talk about cancer for a moment. One of the interesting things that happens during evolution and multicellularity is a scaling of goals. And here's what I mean. The set points, the actual homeostatic set points towards which these systems try to reach start off very small. So individual cells have little tiny cognitive horizons. Their goals are all very small. They're basically trying to manage pH, metabolic state, all these kinds of things in a tiny little region of spacetime. Little bit of memory going backwards, a little bit of predictive capacity, but really this tiny little area is all it's trying to manage. But this, for example, a multicellular system like this has an enormous grandiose kind of set point. In other words, this is the correct pattern memory. And as long as you haven't reached it, your cells are going to be actively trying to get there. In other words, they only stop when they reach this particular state. This is massive. No individual cell knows what this looks like or how many fingers you're supposed to have, but the collective absolutely does. And this is what it reduces the error to. So what you see during development and during evolution in general is a scale up of the capacity to store these kind of set points. These are tiny set points in metabolic space and transcriptional space. These are set points in very large anatomical space. But that kind of system where cells join into networks where the network can remember targets that individual cells cannot remember has a failure mode. The failure mode is called cancer. When these cells disconnect from each other, and so what you're looking at here is a glioblastoma in culture. When cells disconnect from each other they roll back to their primitive unicellular tiny little goals. Meaning proliferate as much as you can, migrate to where life is good, metabolize because at that point the rest of the body is just external environment to you. You're just an amoeba again and the outside body is just environment. That boundary between self and world shrinks. So what's happening here is that cancer is not more selfish than normal tissue. And sometimes people model it in kind of game theory kinds of models as being more selfish and less cooperative. It isn't more selfish. You just have smaller selves. In other words, the boundary between the self and the outside world, the region of spacetime that you care about in terms of trying to manage the states becomes very small and then the rest of the body just isn't part of the adaptive behavior anymore. So that interesting kind of way of looking at it had a very specific prediction. It meant that first of all we should be able to detect incipient tumors via their disconnection from the rest of the body. So bioelectrical dyes should be able to show us where the tumor genesis is going to happen. So that's the first thing and we showed that by injecting various kinds of tumor inducing oncogenes into tadpoles. So these are nasty things like dominant negative p53, gli, Ras and so on. They make tumors, but before the tumors become apparent and start to metastasize, what you see is that again the dye will tell you exactly where the tumor is going to be, right? And so we are now optimizing towards this kind of thing where either a human surgeon or a robot surgeon is going to be able to look down for example and see the tumor margins. He's going to see that, okay, here's the normal tissue, but here's some stuff you got to be careful of because these cells have already acquired an abnormal biological state because they've disconnected from their neighbors. Now, more importantly than just tracking it, could we change it? And so what we did here was instead of trying to kill these cells, we basically said, what if we force them into a normal biological state with their neighbors? And so again, what happens is we inject these oncogenes. And in fact, here you can see the p53 protein is blazingly expressed. It's all over the place here. Here's a massive bolus of it that normally would be a tumor except there is no tumor. This is the same animal. There won't be a tumor because we've also co-injected an ion channel. Doesn't kill the cells. It doesn't fix the genetic defect. But what it does do is force the cells to be part of this large scale network that's working on making nice skin, nice muscle and so on instead of going off and doing its own thing. And so that is something that we are currently working on in humans. This is some data on glioblastoma. We also have a project on colon cancer. Again using reusing existing ion channel drugs. So so powerful electrosuticals candidates for electrosuticals to be able to reconnect cells back to their neighbors. And kind of the final story just to mention really briefly is our program in aging. So one of the hypotheses is that much like these bioelectrical patterns that are critical for establishing normal anatomy during embryogenesis, during regeneration, during cancer suppression, but in your whole lifespan, right, as cells come and go, old cells die, new cells come in. Could it be that over age, the biological prepatterns get fuzzy, they get degraded. And if we sharpen them, and we already I've shown you one example of sharpening, that's how we fix the brain defects in the tadpole. Could we sharpen them as an aging therapeutics and could it be that the planaria, the pattern memory in planaria is part of why these guys are immortal is that they're really good at keeping the holding on to their biological patterns. So stay tuned for that. We have some interesting stuff coming on that. So I'm going to start to wrap up here and just point out a couple of interesting things. One is that because your body is made of this multiscale kind of system where there are competencies and agendas at every level starting with the molecular networks, the subcellular structures like your cytoskeleton, the cells, the tissues, all of it has the ability to take in input, make decisions and navigate various kinds of spaces. It means that we can now use various technologies including AI to start to try to communicate not just with the lowest level which is what universally we try to make people try to make drugs to hit specific receptors and pathways and so on but could we communicate with these higher levels of transduction right that we can actually do in patients what I've been showing you in these model systems and we have a couple of projects called Talk to GRN and Talk to Cells where we're actually trying to use language models coupled with real time closed loop electrophysiological data to actually use language to then communicate and get information out of the cells and actually give them commands and things like that. So that's the first thing. The second thing is that this thing that I've been describing today, this idea that bioelectricity and other kinds of physiological networks are providing a multiscale kind of competency to the material that lets it deploy plasticity and problem solving in the face of novel scenarios. This has implications for evolution because evolution is not working on a passive material where the genome directly maps to some in a fixed way to some kind of outcome. We've been working on models and you can see that in this paper in Trends in Genetics called What Does Evolution Actually Make, thinking about the information in the genome as a kind of prompt as a way to give suggestions to a material that actually has some great flexibility about how it's going to implement that. I'm going to show you this in a minute. But what's important and challenging about this is that it's trying to manage a material that has some degree of plasticity and intelligence is a two-way IQ test. You have to be smart enough to do it. And we're just learning. And here's an example. This is again an example of hacking plant cells. So bacteria basically just manage like this featureless lump and fungi don't do much better. Nematodes can make something that has a little bit of a shape and mites maybe. But by the time you get to insects, they can get the plant cells, the leaf cells to make this beautiful thing, right? So the sophistication of the hacker matches the sophistication of the product of what you're able to do. And so we have to get a lot more clever about how it is that we communicate goals to these various subsystems. And so I'll just very briefly mention some new technology that's currently sitting in our lab. It's been up and running for oh probably about a month now. That is a closed loop AI powered kind of a robot scientist that basically makes hypotheses about how to traverse anatomical space. That is what signals given to cells will get the collective to do one or another thing. It has little wells inside where it actually can give different stimuli to those cells. So the vibration, optical stimuli, chemical stimuli, electrical and so on and then it observes what happened and it learns from that experience, revises its hypothesis and goes back and does it again. So this is basically a new colleague that is working with us to operate in anatomical morph space using living cells as the front-end interface to explore that space of possibilities. I'll just show you one example of the kinds of thing we build. And so Josh talked about Xenobots. I didn't bring any Xenobot slides today, but here's an anthrobot. So this is basically the question of if you can't reach the goal states that you normally reach despite perturbations, what biologicals will often do is find a new set of set points. So this little creature is not something I got off the bottom of a pond somewhere. If you were to sequence it, you would find 100% Homo sapiens genome. Okay, not edited. These are adult, not embryonic, adult human tracheal epithelial cells that self-assemble when you take them out of the body. They self-assemble into this little motile creature. This is what they look like. They swim around because these little cilia are waving. They have all kinds of interesting properties, right? So these guys taken out of the body can no longer make a human. They can't be a human body, but they do something very coherent and they have some interesting features. First of all, over 9,000 differentially expressed genes. Again, no genomic editing, no synthetic biology circuits, no nanomaterial scaffolds, no drugs, just a different lifestyle that they've adopted and they spontaneously change half their genome is now expressed differently. Okay, that's the first thing. The second thing is they have four different behaviors. Okay, not 12, not one, but actually four different motility behaviors that you can quantify. This is the probability transition diagram between them like you would do with any animal. One of the first things we realized they could do is if you take a lawn of iPS derived human neurons, you put a big scratch through it, the anthrobots will come, they'll settle down into a cluster. This is I don't know probably 10 to 12 bots here. They're shown in green. And they'll settle down and what they start to do is to knit together the gap. Okay? So when you take them off you'll see that under where they were sitting they were trying to repair this. So they have some sort of ability to induce the neurons to join up. And who would have known that your tracheal cells which sit there quietly in your airway for decades if you take them out they become a self-motile little creature that can fix neural defects. And this of course we're working towards these kinds of things as patient-specific in the body robotics that they're made of your own cells so you won't need immunosuppressive drugs. We're trying to figure out what are all the things that they know how to fix and how to deploy them for biomedicine. Something else you really can't see the labels here at all. But one of the interesting things about them is they're younger than the cells they come from. So this process of becoming an anthrobot actually rolls back the clock as measured by the epigenetic clock on these guys. They're actually younger than the cells they come from. So again, there's an aging story. So this is kind of my last slide and what I'm just going to say is that almost everything that people are excited about today in terms of biomedicine comes from these kinds of approaches, these sort of bottom-up approaches focused on the hardware. And we would like to complement that with tools taken from other disciplines. So this is cybernetics, behavioral science, cognitive and computer science. That tells us that the material we're dealing with is actually amenable to all sorts of kinds of top-down approaches that lets us do very complex things that are really difficult with this. And so we as bioengineers and as workers in regenerative medicine, but also if we're seeking to understand evolution and our own origins of both of our bodies and of our cognitive systems, we really have to drop this idea that the material is only to be described by simple open-loop models in which nothing knows anything up until you get to a big mammalian brain. But that actually the sciences of information processing and of behavior are helpful all the way down. And bioelectricity is the interface layer that really enables that control of growth and form. It's not the only one, but I think it's the one that we have the best amount of control over now and I think we'll be able to hack this for some incredible applications and some of that is described here. So I'm going to stop here and just thank the people who did all the work. So my postdocs and grad students and the team at Josh Bongard's lab who worked with us on that discovery engine that I showed you and we have lots of amazing collaborators and thank you to our funders. Here are my disclosures. There are three companies that have licensed our various technologies that I've shown you today.
