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Dr. Michael Levin is a Distinguished Professor in the Biology department at Tufts University, where he serves as director of the Allen Discovery Center and the Center for Regenerative and Developmental Biology. He works at the intersection of biology, computer science, and cognitive science, uncovering the latent intelligence of individual cells and tissues. Among other topics, his lab explores how organisms repair and regenerate entire body parts, a capacity with tantalizing potential for human medicine.  Michael joins the podcast to discuss common misconceptions of biology, building biological robots, and the potential of regenerative medicine to revolutionize human health.

Tom:  Here we have Dr. Mike Levin. Welcome to the show, Mike.

Michael: Yeah. Thanks for having me. Glad to be here.

Tom:  I want to start by asking you some personal questions before we kind of leap into some of your research. So, can you tell me where you grew up and what some of your interests were as a child?

Michael: Yeah, I was born in the ex-USSR. I moved to the north shore of Boston in 78. I was nine years old at the time. And, my interests were engineering, what later became computer science and, nature, you know, bugs, insects, the living world.

Tom: Was there someone in your life that encouraged, inspired, and cultivated your science interests?

Michael: Yeah, both my parents, although my mom was not a scientist, but my dad was a computer engineer. And from a very early age, he used to take the back off the television set. We have this, like, ancient thing with vacuum tubes and a lot of, parts and so on. And, uh, he used to take the back off so we can stare at it and just sort of marvel at the idea that somebody knew how to put all that stuff together. I also had a friend who was a slightly older kid who was really into collecting insects and we would go outside and, play with beetles and caterpillars and insect eggs and so on. So, I think it started very early.

Tom: When did you get a sense that biology was the field where you wanted to explore deep and make your mark?

Michael: My original, plan was to do, computer science and AI. I went to undergrad for a computer science degree.

But, by the time I was in college, it became clear that we were going to need the insights of developmental biology to really understand how minds come into the world. And at that point, I realized that I really had to get serious about biology, and so I got a second bachelor’s degree in biology. Then by the time I decided to go to grad school, that was for genetics.

Tom: I read that you got your PhD in genetics within the framework of a medical school, what prompted that decision or was there something very deliberate about being in that kind of institutional setting?

Michael: To be honest, I applied to several places and out of the few that accepted me, that’s the one that looked like the best program. they had some amazing people. They’re doing foundational work in developmental biology and cell biology. My first lab was at the Harvard Medical School campus at a place called Forsyth Institute. I started their in. By, I realized that I needed to leave the medical school environment and really be at a liberal arts kind of campus where philosophy, computer science, physics, and all those things were represented. So, ultimately, that was not enough for what we needed to do. But at the time it was an amazing program that let me focus on the cell biology that I needed to learn.

Tom: if you had to pin yourself down and describe yourself within a particular academic field, would you say it’s developmental biology or something else?

Michael: I have no idea how to do that. people ask what I do. I don’t know. My lab does, some computer science. We do behavioral science. We do developmental biology we do synthetic biology. I write papers on philosophy of mind and, things like that. if I had to boil it down, it’s sort of what used to be called natural philosophy.

This kind of idea that, there are these deep issues that intertwine in an important way, and that trying to carve them up into disciplines that go to different, departments and different journals and so on, that’s artificial. But developmental biology is certainly a huge chunk of what we do.

Tom: Yeah, it’s probably a term that a lot of people aren’t familiar with. Is there a kind of a shorthand way you’d describe what it is?

Michael: Yeah. Developmental biology is the study of maybe the most remarkable process in the world that, has perhaps the most significance for all the deep questions that we faced. And that is the way that, Physics and chemistry becomes mind.

So, each of us were a single cell once we were all a little blob of chemicals as known as an unfertilized oocyte and through a process of embryonic development that becomes a complex organism or, even perhaps a human with a mind and a kind of a metacognition and the ability to reason about their own mind and so on.

And developmental biology is the science that studies that transformation. They focus on the body, how complex bodies come to be and why there are certain shapes and so on. I see it as very, parallel. The creation of bodies and the creation of minds, it’s the same process fundamentally, I think. but that’s developmental biology.  

Tom: Let’s turn to research, kind of starting big and then we’ll, we’ll get more focused. I wonder, what’s a common misunderstanding that, people kind of outside the academic world have about modern biology and perhaps a misunderstanding people have about, what you do in your laboratory?

Michael: Hmm. I think that most people, feel that the big questions of biology are in many ways nailed down already. And that, we understand how bodies work, how evolution works, where we come from. I do sometimes talk for school kids and if I ask a bunch of nine- and ten-year old’s, why does your body look the way it looks?

Why does a certain animal have a certain anatomy? Everybody will say, well, the genome, it’s in the DNA. And I think a lot of people have that misperception.

There’s nothing directly in the genome that reads out what you’re going to look like, how many eyes you’re going to have, if you’re going to have eyes, or any of those things. All of that arises as the working out of the physiological software of cells. And I think people have a fundamental misperception about what genomics doesn’t does not explain and the role of intelligence and information processing and plasticity in what cellular collectives do in terms of development, regeneration and so on. I’d say that’s the biggest gap.

Tom: I kind of caught on fire for biology when I was in high school, and I think it was when my AP biology teacher told me to go buy a copy of Richard Dawkins the Selfish Gene. So, it had already been out for years or so, but I was enamored by it. The way it’s written is beautiful. But looking back on it, I wonder if I could just, I want to read one passage from that book to you. And maybe we can kind of work from there of like what still resonates and then what do we just need to leave back in from when it was written.

So here we go. Richard Dawkins in The Selfish Gene describes life as the following, “Replicators, swarming in huge colonies, safe inside gigantic, lumbering robots, sealed off from the outside world, communicating with it by torturous, indirect routes, manipulating by remote control. They are in you and in me. They created us, body and mind, and their preservation is the ultimate rationale for our existence. They go by the name of genes, and we are their survival machines.” Tell me, what does this kind of statement get right, and where does it go wrong?

Michael: Well, it is certainly useful to be able to think that way. And it’s a good skill to be able to focus on, information as. As the key and the physical tangible machine, as a kind of a dispensable, disposable, vehicle for it. Okay, so that’s a good way to think about it.

But the part that it gets wrong is that it assumes that this gene centric perspective is in some objective way, the correct one. And I think this is wrong on two counts and it’s deeply wrong.

One is that it assumes that any scale is the correct objective scale, and I think we need to bite the bullet and understand that all of this is in the eye of the beholder. it’s relative to an observer now. what are observers? Well, human scientists, of course are observers, but so are various parasites and conspecifics other animals. So are the components that make up the organism.

So is the organism itself, right? We are all observers and all of us are engaged in a battle against entropy and the way we do it is we, hack each other you are hacking your body cells, they are hacking each other, your organs are hacking your tissues in terms of providing signals to get them to do various things, okay, we’re all solving problems at different scales, we’re all trying to get each other to do certain things

So that’s one thing it gets wrong – is that assumes that there is one level. and then the thing that I would really argue against is this idea that the gene’s eye view is in some way privileged, and you know, Dennis Noble has written quite well, against that view, but it’s a perspective, which is useful in some scenarios, but boy, does it leave a lot on the table?

It just is, very, limiting in the sense that we’re made of cells which are an agential material. We are not lumbering robots in the sense that, the robots that he’s talking about are single layer devices.

I give another talk called “Why Robots Don’t Get Cancer.” Well, it’s because they’re made of dumb parts, okay? And so, the parts never defect, and they never do anything that the collective doesn’t want them to do. we are not like that. biology is a multi-scale architecture. We’re built of an agential material. These cells have agendas. They can learn. They have preferences and competencies. And when that’s the case, evolution works completely differently, and so does engineering. When you’re not dealing with Legos, with dumb machines, which we had been for millennia. When you’re not doing that, but rather you have a competent material, some much more interesting things are possible, both in evolution, biomedicine, engineering, and you leave all of that on the table when you put everything into the gene and nothing into the surrounding material. That’s just not how biology works.

Tom: So, in your laboratory, you’re in a certain sense creating new things with biological components versus dumb materials. Can you tell me a little bit about where some of the advantages or perhaps disadvantages of using biological components as opposed to just simple things that don’t change?

Michael: To use a silly analogy. Um, let’s imagine you’re making a tower, and your first tower is made of Legos and the Legos are a passive material. The only promises they keep is that they hold their shape. That’s it. And so, what that means is that you as the engineer must put every Lego in the right place. So, if you’re building something complicated, you must know where everything goes, and you must put them all where they go. If that’s how we think about biomedicine and the regeneration of limbs and eyes and things like that, it all seems hopeless.

Okay, there’s no way we’re going to be able to handle the complexity of all the genes, all the cells and something like a limb. On the other hand, now, let’s say we are building a tower out of dogs, little poodles or something. And so, when you do that, you find out very quickly that the original strategy doesn’t work.

You can’t just stack them on top of each other because they’re not passive materials. They’ll run off. But you find something interesting. What you can do is you can train them. interestingly, humans have been training, let’s say, dogs and horses. For thousands of years before we knew any neuroscience.

So, it’s interesting. You don’t have to understand everything about your material, but you do have to catch on to the fact that the material exposes an interesting interface to you. And in the case of the dogs, that’s a learning, training, kind of, reward and punishment interface. So, once you understand that interface, you train them, and now you have something very interesting.

You have a tower that self assembles. In other words, you let the dogs go, they make the tower, you knock it down, they get right back up, okay? And what that means is that you didn’t micromanage where, every dog goes and where everybody’s paws go the way you would with, the Legos.

You got buy-in from the material. And that only works because you have an agential material. You were able to deform its reward landscape. To get what you want. And in the case of biomedicine, and this is what we work on in our lab is we try to find out why are the cells building a particular structure in the first place?

How does a collective intelligence of cells? And that is exactly what it is. It’s a collective intelligence that navigates the space of possible anatomies. How do they remember what they’re supposed to build? Can we rewrite? those memories to get them to build something else or whatever we want them to build. And how can we do that with the least input? In other words, I don’t want to micromanage all the details. I want to incentivize the cells to build a particular structure, and I want to take my hands off the wheel. And this is, for example, in the leg regeneration, project that we had in frogs.

Our treatment with this wearable bioreactor device that we came up with. After that, a year and a half of leg growth. We don’t touch it again during that year at all. We’re not there telling the stem cells what to do. We’re not there trying to D print different tissues.

At the very earliest moment, you communicate to the collective, go down this path that leads to limb formation. Do not go down the scarring path. And that’s it, right? So, that’s the kind of advantage that you have.

Tom: Yeah, I think, when most people think about sort of changing organisms to hack them and get them to do different things, people think about CRISPR and change the genetic code, but you’re doing something different. Can you tell me what are the other ways you can get an organism or certain cells to do different things besides going in and changing the genetic code? Changing out DNA code.

Michael: Yeah. Well, the first thing I should say why can’t we just do it with DNA? I mean, genetics and molecular biology has been around for decades. here’s the thing. Except for low hanging fruit, like single gene diseases where you know exactly what gene you need to twist. Most things that we care about, we have absolutely no idea which genes you would change to get them to happen.

And that’s because mostly genes don’t code for traits, genes code for proteins. And what you want to change in biomedicine are large system level traits. and it’s entirely unclear in the general case what genes are going to be mutated and how.

So, that’s going to be the limitation of genome editing. And you can tell that right way because here’s a simple problem.

You’ve got the frog and frog. Embryos don’t have legs and they make tadpoles. And you’ve got a salamander, let’s say an axil lot, and the baby ax lots do have little four legs. Now, in my group, we make something called a frog ot. And this frog OT is a bunch of frog cells, bunch of salamander cells.

They cooperate just fine. They make something that’s totally viable. Now, I ask a simple question. We have the frog genome. We have the axolotl genome. Can anybody tell me if a frog OT is going to have legs or not? and the answer is no one can say, because the genome doesn’t say if you’re going to have legs, and even if it did, you wouldn’t know how a mixed chimeric collective is going to make decisions.

We understand more and more about the hardware. We still do not understand much about how the software, the algorithms by which cellular collectives make decisions, legs or no legs. this is all the limitations of the genetics. What we’re trying to do is ask the following question.

Every kind of collective intelligence, and all intelligence is collective intelligence, we’re all made of parts of some kind, everything’s made of parts. So, all intelligence is collective. There’s going to be some medium by which these collective intelligences process information to guide their activity, specifically to move towards that region of anatomical space where the right legs and arms and everything else is so we can ask what is that?

What’s that? That kind of cognitive glue that binds these cells together to common purpose. And it turns out to be the same cognitive glue as in the brain. It’s electricity. It’s electrical networks. it’s the ability to form electrical networks and to process information in those networks. So, then we can have a research program where we try to read out and decode. The electrical information, the pattern memories, literally the pattern memories that these collectives are holding and, try to then rewrite them for other purposes. And the idea will be then, much like with a thermostat, when you change the setting, you don’t have to rewire the thing and you don’t have to exactly even know how all the parts work. What you need to understand is how it encodes the setting and how do I go about changing it? And then you let it do its thing and that’s what we’ve been doing.

Tom: I want to ask you one more question along these lines before we pivot. So, you’ve done a lot of work in your laboratory, on frog embryos, working on that species and have had a lot of success and some great insights. And there’s a couple different, organisms or names that have come out that I find is totally fascinating. I wonder if you can tell me what some of, these, I don’t know if you call them creatures or what they are, but Xenobots and Picasso Tadpoles. Can you tell me what those two things are and, and what they do?

Michael: Sure. Yeah. Picasso tadpoles were a way to test the intelligence of development in the frog. So, so the idea is, when you are trying to assess the competency of some kind of system, you can’t guess, and you can’t do it from purely observing what it does. You must do certain perturbations and ask how well it reaches its goals despite things going wrong.

And so, when you look at how a tadpole becomes a frog, it must rearrange its face. It must move the jaws. The eyes are the nostrils. Everything must move. So, most people would look at that and they would say, Okay, that’s probably a hardwired, process, after all, every tadpole looks the same, every frog looks the same.

So, all the genetics must do is somehow encode, how far and in what direction every organ moves and then, you get where you’re going. So, we said, well, that’s a hypothesis, but what if we had a different hypothesis? What if this thing is smarter than that? And the way you test it is you start everything off in the wrong position and you see what happens.

And so, we made so called Picasso tadpoles. We scrambled all the initial positions of the organs. And so that’s why the mouth is off to the side, the eyes on top of the head, the nose is off, like everything is, in the wrong position. And what we found is that they still make largely completely normal frogs.

Because all these things, even though they start off in the wrong positions, they will move in novel paths. To get where they’re going, and then they stop when they reach the correct position. So that’s a much more intelligent system. it has a goal, it knows where it’s going, it knows to stop when it gets there, and it doesn’t care that you’ve started it off in the wrong position, okay?

And so, that’s the Picasso frog.

For the Xenobots these are examples of anatomical homeostasis. So, you’ve got a system that’s trying to reach a particular anatomical outcome. in terms of these pattern memories, you might ask,

Why do the cells, work so hard to try to make a specific thing? where does that come from? And the typical answer there is, well, selection, of course. so, in the froggy environment, you must reach certain goals or, you’re dead. And so, everything that couldn’t reach those goals died out.

And the only thing that’s left are these, cells that, know how to do this one thing. I’m deeply suspicious of that story. And one of the things we tried to do is, is to look at the plasticity of these cells and ask, okay, the same cells without any DNA modifications, without any weird nanomaterials, if we just let them reboot their multicellularity, we liberate them from the rest of the animal, we ask what, what do we want to do?

Doug Blackiston, in my group did all the work and this is a collaborative, project with Josh Bongard’s lab at University of Vermont. What we did was we took some epithelial cells or skin cells, off the top of an early frog embryo and we set them aside in a petri dish.

What they could have done is nothing. They could have died. They could have wandered away from each other. They could have made a two dimensional flat, kind of a monolayer the way in tissue culture. Instead, what they do is they come together they form a little, blob.

And there are little hairs on their surface, and normally frogs and tadpoles use these hairs to redistribute mucus down the side of the animal. so, they use these hairs to row against the water, and they start to swim. And they swim, and they have all these, spontaneous behaviors. They can go in circles, they can go straight, they can do several different behavior types.

And they also have some wild, characteristics. For example, we’ve made it impossible for them to reproduce in the normal froggy fashion, because they don’t have any of the tissues or organs that you need for that, they’re just skin, but if we provide them with loose skin cells, what you see is that they move around in ways that coalesce these skin cells into little, piles and they compact them into these little balls, the little balls become the next generation of Xenobots.

And guess what they do? They run around and do the same thing. And that’s the next generation. So, we call this kinematic self-replication. we call them Xenobots because Xenopus laevis is the Latin name for the frog species that we’re using. Bots because we think this is a biorobotics platform.

It’s a way to understand how to exploit the native competencies of certain living materials and, get them to do interesting things, or modify things that they already want to do. Yeah, and that’s the story of the Xenobots and it’s just beginning. You know, we’ve got some wild papers coming on, on that topic, soon.

Tom: Coming up… I want to pivot now to regenerative medicine in the human context.

Tom: You wrote that there’s a fundamental question in regenerative medicine, which is namely, how do we convince a group of cells to produce a nice, healthy organ? So how do you begin to kind of answer your own fundamental question?

Michael: We’re working on that now in limb regeneration. So, moving from frog to mice.

I think the answer is going to be twofold. One is there’s going to be an environment that needs to be produced for these cells that convinces them that regeneration is going to be possible. It’s protected. it makes sense to put energy into doing it. I think one reason why mammals don’t regenerate their legs is if you think about how this would have played out for an early mammalian ancestor running around the forest and somebody bites their leg off.

You’re not going to have time to regenerate anything. You’re going to try to step on it. You’re going to grind it into the forest floor. It’s going to get damaged and infected. And if you don’t immediately scar it and seal it over, you’re done for.

So, my gut feeling is that a lot of it is environment, and having a protected almost amniotic like environment where the cells perceive that it makes sense for them to regenerate.

That’s one component. And so, for that, David’s lab makes these wearable bioreactors. We call them these biodomes.

And the idea is going to be to come up with a correct cocktail of ion channel and other drugs that are, in the biodome to convince the wound to immediately start growing towards a regenerative path instead of a scarring path.

Tom: What do you think are some of the most pressing questions? And areas of focus that should be guiding biology, guiding medicine in the years ahead?

Michael: I think the biggest question of all revolves around really understanding the competencies, and that includes the prior memories, beliefs, expectations and information processing, goal directed activities of the material of cells and tissues. I think that right now, most of Molecular medicine is where computer science was in the forties and fifties back then to reprogram your computer.

You must interact with your hardware. You must physically rewire it. And that’s what everybody’s doing now with pathway engineering and genomic editing. You know, the single molecule approaches. All the excitement is around these hardware solutions ever smaller.

A lot of medicine today, except for, antibiotics and surgery and maybe a couple of other things, the medicine we have today doesn’t fix anything. there are very few things we have actual cures for. What we have are ways to address symptoms and you address the symptoms.

You try to hold it steady and, well, first, as soon as you stop the drug, usually it comes back or gets worse. And, cells will fight it. there’s drug, resistance and, habituation and things like that. Because you are trying to micromanage agents. These are not passive materials.

And so how do we get buy in from the collective that, yes this is not some parasite, trying to hack you from outside. this is a good idea that, you should be following. This is, in fact, the best way to get, others to do things when they think it’s their idea.

So, this is the future understanding the proto cognitive capacities of our material.

Tom: you’ve studied a lot of different organisms in your lab. I’m wondering, to the degree to which you’ve looked at these different forms of life, have they given you certain insights into what it means to be human, or why we behave the way that we do?

Michael: Yeah, the Planaria, I think are my favorite, in the following, sense you can take a Planaria and, this was discovered by McConnell in the sixties. You can take a Planaria they have a true centralized brain and so on. You train ’em on some tasks you cut off their head, including the brain.

The tail will sit there doing nothing for, let’s say nine to ten days. In the meantime, they, they grow a brand-new head. With a brain and then you can test them and show that they’ve retained the original information So what that suggests is that the information is somewhere else presumably somewhere else in the body, and it is imprinted onto a fresh brain.

So that seems weird, but in an important way, we are all like that planaria because the you of today do not have access to the past. All you can do is piece together your previous existence from the engrams and the other, traces that the previous versions of you have left. so, memory is basically a communication to your future self.

And at any point. Our view of what we are, who we are, our memories of ourselves, the stories we tell ourselves about what we are as a self, all of that is pieced together pretty much in real time from, the material that is in your, body and brain. And from that perspective, I think these Planaria are telling us something very profound about the nature of.

What it means to be a living being in this world where materials come and go, your cells come and go, you know, molecules enter and leave your body, but the ship of Theseus of your body is remains the same, but you must reconstruct it. It is a constant work in progress as are your memories.

Nothing is static. And I think that’s, profound.

Tom: Yep. Yep. Boy, I’ve got a lot to reflect on, philosophically on. On those matters, uh, yeah. Okay

Tom: the time we have remaining, I want to kind of circle back to where we started. I wonder if you got into a time machine and, had a chance to interact with your childhood self or maybe high school self. What might that conversation look like from the, experience you’ve had as an adult, the research you’ve done, and your childhood curiosity? what does that conversation look like?

Michael: Wow.

You know, it’s funny because you never know, what you might say that could derail the whole thing, right?

So, you gotta be careful with that stuff because I, I have no idea how, some of that might change.

But I would say a couple of things. as a kid, I had some scientific heroes, and I read a lot of books by some very smart people. And I was just amazed how some people can both do the research but put it together into understanding of the world, especially across sciences, you know, people who write about physics and philosophy and computer science and math and things like that, folks like Paul Davies and Dan Dennett and many, many, many others.

And I think what I would do is I would just show my younger self. That these are people I work with now, keep at it because, you’re gonna be lucky enough to be in the mix with, some of these amazing, questions that these people are asking.

I’ve been unbelievably fortunate to be working with the people in my lab and my colleagues and collaborators. It’s just I can’t ask for anything more.

Tom: Mike, thanks for talking to me today. It’s been great.

Michael: Great. Thank you so much. Yeah. Thanks for the conversation.