Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas - 157 | Elizabeth Strychalski on Synthetic Cells and the Rules of Biology

Episode Date: July 26, 2021

Natural selection has done a pretty good job at creating a wide variety of living species, but we humans can't help but wonder whether we could do better. Using existing genomes as a starting point, biologists are getting increasingly skilled at designing organisms of our own imagination. But to do that, we need a better understanding of what different genes in our DNA actually do. Elizabeth Strychalski and collaborators recently announced the construction of a synthetic microbial organism that self-reproduces just like a normal unicellular creature. This work will help us understand the roles of genes in reproduction, one step on the road to making DNA molecules and artificial cells that will perform a variety of medical and biological tasks. Support Mindscape on Patreon. Elizabeth Strychalski received her Ph.D. in physics from Cornell University. She is the founder and current leader of the Cellular Engineering Group at the National Institute of Standards and Technology. She serves on the steering group for the Build-A-Cell collaboration. NIST web page Google Scholar publications Talk on Controlling Biology with Complexity "Genetic requirements for cell division in a genomically minimal cell," Pelletier et al.

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Every episode, I nerd out with amazing guests and dive into the best new audiobooks available on Audible. It's the book club for your ears. Listen to Earsay, the Audible and I-Heart Audio Book Club. On the I-Heart Radio app or wherever you get your podcasts. Hello, everyone. Welcome to the Mindscape Podcast. I'm your host, Sean Carroll. And it has been said that while the 20th century was the century of physics, the 21st century will belong to biology. I'm not sure if that's true or not. I think the 21st century is big enough to have more than one science in it.

There's plenty of room for both physics and biology to do good things. But there's no question that the rate of progress in biology has been pretty amazing in recent years and shows no sign of slowing down, especially in our ability to really manipulate what's going on down at the level of cells and even individual strands of DNA. Those of you who are longtime Minescape listeners will remember a conversation we had with Kate Atomala who works on synthetic life, putting together cells from scratch, right? I mean, we don't quite have that done yet, but this program of building a cell, starting with our human knowledge rather than from existing biology.

What we have done so far is taking existing organisms, removing their DNA, by an organism I mean a little tiny one-celled organism, removing its DNA and inserting DNA that we have designed and seeing whether or not it will keep going. So a few years ago, scientists at the Ventner Institute, named after Craig Ventner, were able to build what is essentially, we think, the minimal cell that actually works in some sense, that actually reproduces itself and so forth. to be honest, it didn't reproduce itself that well. You know, like we hope that an individual cell will split into so that both halves look more or less like the original cell. That's not happening with these minimal cells. So today's guest is Elizabeth Strahalski, who works as the group leader of the Cellular Engineering Group at NIST, the National Institute of Standards and Technology. And so she and her collaborators, including many of the people on the previous program, were able to fix the minimal cell to build a minimal cell that reproduces nicely in some way.

So basically they took genes and they re-inserted them back into the DNA until this little one-cell bugger was able to reproduce nicely, splitting into equal sizes, perfectly healthy, single-celled organisms. What's amazing is, oh, it's amazing, number one, that we can do that, right? I mean, this is opening up tremendous vistas for future. progress. The fact that we can do it means that someday we'll be able to do it well and easily. But also, number two, we don't know what all the individual genes do, what their responsibilities are in the cell. We know that if they're not there, it doesn't work, but we don't know what actually

happens in terms of those genes turning into proteins and making the cell go. So it is an amazing frontier where we're making progress, but there are so many things we don't know. So Elizabeth and I talk about how this stuff works, why you're looking for the minimal cell at all, how it actually happens in the lab with designing the DNA and so forth. And then, you know, we speculate a little bit about what this means about the origin of life. The fact that the minimal cell has quite a few genes in it, hundreds of genes, makes an interesting question arise, right? I mean, how did that come about out of something that wasn't a cell at all? But even more importantly, perhaps, for practical purposes. What is this kind of technology going to be good for in medicine, in drug design,

in sensing what is happening in the body? This is one of Elizabeth's specialties is imagining that we can figure out how your body is doing by inserting little sensors into it that we've constructed synthetically as cells in their own right. So I don't know what to say about this. I'm not a biologist, but it's extraordinarily exciting, and the prospects are practically limitless. And I think that, you know, in the decades to come, we're going to be seeing the payoff from this kind of research, and it's going to change lots of things in interesting ways. So let's go. Elizabeth Tjolsky, welcome to the Mindscape Podcast. Thanks so much. It's a pleasure to be here. So as a physicist, what we're trained to do, what I'm trained to do, is tear things apart, right,

to their constituent pieces, whether it's an atom or a solar system or something like that, and look for the basic underlying laws. And I know that a lot of people in biology, or at least I get the impression, that there's a conventional wisdom that there are no such underlying laws in biology. Biology is just a mess. Every organism's different. You have to go piece by piece. So is that the right impression to have, or is the search for fundamental underlying laws still a good thing that we should try to do? Personally, I think that even if there are no fundamental laws that look like the fundamental laws we might be used to finding, even the search itself is worthwhile.

there's nothing wrong with trying to strip something to its essential elements to try to ask, you know, is there an essence there? Or how much of this object that I understand or want to be essential in some way is actually the result of relationships between the different parts. And do you find in your, it's going to peek ahead a little bit, but do you find in your work that that's true? Are we like discovering little ideas that might someday grow up into laws of biology? Wow, such a great question. It is my sincere hope that we find laws. And personally, I'm hoping that we find laws that look more like quantum mechanics in the wild, right?

Or apply quantum mechanics where we arrive at a recipe book that allows us to do practical things. And so maybe biology would look less like synthetic biology, more like engineering biology. And again, you know, these are words, but words matter. It matters what you call things. And so, you know, we have some real challenges in the world right now that we face. And I think we need to bring all of our collective knowledge and capability to bear on these. And one exciting tool set that we have is synthetic biology, engineering biology. Right.

Well, in fact, the words that you're using are reminiscent of the metaphor I was going to ask about. I mean, in some sense, what you do is kind of like, the enthusiasts for the latest smartphone who tear them apart when they appear and try to figure out what all the pieces are, right? Like, we have some working gizmo, but in your case it's a cell or an organism and you're trying to tear it apart, right? I love that analogy. And I came originally, so as a physicist, I grew up through the experimental high-energy

physics community. And so what a lot of what high-energy physicists try to do is to smash apart the basic building blocks of matter to understand what is there. And so for me, it makes perfect sense that you want to smash apart a biological system to understand what's there and how those parts interact. Now, of course, once you arrive at those laundry lists of parts and relationships, there's still some secret sauce that we don't understand. This science around emergence and complex systems and, you know, what's that spark of life that, you know, suddenly you no longer have just, a pile of stuff, you have a pile of stuff that is, air quotes here, you know, alive. Right. Well, I mean, actually, your statement makes me realize that there's something very

fundamental and simple that I don't understand. I understand when a person dies or any higher organism dies. There's a whole bunch of processes that go on between the cells that no longer happen. So we don't, it's a very similar set of atoms and molecules, but it's no longer a living being. What happens when a cell dies? Do cells die? Do they just, you know, sort of grow old? Yeah, so some cells die. Some cells grow old in certain ways. I should mention as an aside that I am emphatically not a cell biologist. I nearly play one on TV, right? It's always good to have a cell biologist on your team when you're working in this field. And I should mention, too, that what's really exciting about the way biology moving forward around questions of, you know, minimal life, synthetic cells, is you find yourself on these larger collaborations where you need deep knowledge in lots of different fields all coming together in the interstices of those fields to make it.

happen. So I bring the physics and engineering perspective. But coming back to your question about cells dying. Sorry, no, you've opened up a little Pandora's box there. I got to dig into this more. So you and your team, your collaboration that you're working with, are literally building cells. So to someone like me, that makes you a cell biologist. So, I mean, maybe, how do you classify yourself? What are the different kinds of people who have to work together to make something like this happen? One of the really exciting things about working in the life sciences right now, and specifically in synthetic biology and engineering biology. Some people call it cellular engineering.

I want to make sure that I say a lot of the different buzzwords so that folks can go Google these later if they want to learn more. It's that we're taking these well-developed, deep bodies of knowledge from what used to be siloed disciplines. And we're basically mashing them all together to see what sticks. So, for example, you mentioned control of biology earlier. So if I take control engineering as we understand it from, say, radios and computers or building a car and then overlay that with cell biology, what might be possible? Right. And this is one thing that folks are trying to understand right now. So typically, you know, you might have somebody who has biophysics knowledge.

You need people who have knowledge of the different measurement techniques that you need. For example, maybe electron microscopy or boroscence microscopy or DNA sequencing. You might need an automation specialist because, as we know, biology labs are looking less and less like someone's standing. next to a bench with a pipetter in their hand in a white coat, hopefully goggles too as well. Please everybody, to the extent that you're in your garage, biohacking, please take safety serious. Wear the goggles, yes. And it's starting to look more and more like laboratories filled with robots. So you might have, we have a mechanical engineer who's part of our team in my laboratory, for example.

We work with folks who are experts in machine learning, because as you automate these measurements and these experiments, you get a whole lot of data out, more than it's easy even to think about. It's a lot. And what that does is it lets you ask new and different kinds of questions about biology, because you can bring to bear all of this incredible computation power that you get from machine learning. Okay, so I interrupted you when you were explaining how cells can die. I mean, what is the process inside that no longer happens to that collection of atoms and molecules? Well, the easiest thing to say, I think, is that it's no longer doing the things a living cell does. And I know you've had other guests on your show who have spent time talking about what is life or even about building synthetic cells. I think for me, as somebody who comes from more of a physics and engineering standpoint,

I like to think of life and death as kind of the spectrum. Okay. In the same way that I think of a spectrum from chemistry to biology. And depending on what you want to build or do what capability you want to have, you might want to situate yourself at different places on those spectrum. So, for example, I might want to bioremediate something. which would mean that I'm going to engineer something biological and then release it. Well, do I really want it to be able to reproduce?

Right. Maybe not. Right? I've seen that movie. It doesn't go well. No, you know, it never does. It never does. And a couple years ago, I was actually trying to convince some funders that we need to try out terraforming technology.

on an asteroid or something offer because that way when we need it here, it's not our first go. Right, right, right, right. Yeah. Well, okay. So, so that is very interesting. I think that, like you said, we've had people on the show who have talked about what is life, what is a cell. Kate Ottomala, who I guess is a collaborator of yours, or at least in the same big group, talked a little bit about synthetic biology, but maybe some of the people listening right now haven't even heard that. And so what I'm guessing is everyone knows that cells have members, brains and a little nucleus inside with DNA and the DNA converts its little parts into proteins, which then go do things. Do we need more knowledge than that for what's about to come when we talk

about building a synthetic cell? I mean, what about how the cell works is the basic knowledge platform that we're going to have to build on here? There's so much fundamental knowledge we still need to gain. And just to make sure, you know, for completeness that we cover this, there are sort of two main paths forward that people are walking down to arrive at something like a synthetic cell or a minimal cell. So you can take the bottom up approach where you take non-living parts. If you're a purist, I guess you would start with chemistry, not even biochemistry. And you would try to assemble bits and pieces until life happens.

So this has not. been done yet, to the best of my knowledge. Okay. And although lots of folks are working in this area and making great progress. The other path forward is more of a top-down approach where you start with something that is already a living cell and you take parts out until it dies in some way. Now, it's really simple to say these things, but when you actually go about trying to do of them, you know, the devil's in the details. And with regard to the minimal cell work that

I've been doing recently with just wonderful collaborators at the J. Craig Mentor Institute and other places, and I want to give a shout out to my co-authors, James Palitia and Liji's son. They're just fantastic to work with. So what we've been doing there is, you start with something called an obligate parasite. Okay. And what that means is it's an organism that can't live outside of its host. It still has all the cellular machinery that it needs, but it's adapted to live in a very stable, very specialized environment,

which means that it hasn't needed to retain a lot of the genetic information or proteins or other bits of itself that it needs to respond to a changing environment. But this is one cell we're talking about, a unicellular organism. That's right. That's right. This is a kind of mycoplasma. Okay. And it's a goat tuberculosis, I guess. So I had to sign up outside my lab for a long time saying, you know, if you have a peck goat, don't think. So these scientists developed a way to scramble parts of the genome and then grow the cells that resulted. And then you can sequence the DNA that's left of the cells that grew well.

So you can guess that, you know, if any of the genes were scrambled and so couldn't be expressed, they must not be essential for life. So cut them out. So you can take that empirical approach. You can also take an approach where you look at related organisms and you can kind of guess what the function of different genes might be and guess whether or not a certain gene is essential or not. Now, you'll notice I'm not saying essential for what. Because when folks talk about minimal life or minimal cells, what's implicit in that is that you're minimizing to some criteria.

You're putting some boundary conditions on this thing. And when the JCBI and collaborators published in 2016, the minimal cell, this was JCBI's Syn3.0, what they did was they minimized to the criteria of the cell growth looks normal at the colony level. So when you looked, and you could even see these by eye, you've got this beautiful, you know, if you've ever seen a mycoplasma colony, it looks a little bit like a fried egg. And so it looked totally normal. And the other thing that they wanted was they wanted the cells to grow, fast enough, again, air quotes here, that it was useful for work in the laboratory. Nobody wants

to sit around for a week while their cells finally get around to double. Right. It's just not practical. Really slow down your research. So they wanted to keep genes that would keep the cells growing fast enough to be useful. Okay. Well, we took a closer look at what was happening. It turned out that none of these minimization criteria paid any attention to what was happening at the single cell level. And so when we looked under our microscope, it was absolutely bonkers what was going on. I mean, crazy. We took some optical micrographs that showed videos of these things growing in microfluidic chips on our microscope. And you would start with this perfect, you know, spherical round, totally normal looking cell.

And then you come back a little while later, and there are filamentous cells, branching cells, huge things that, I don't even know if it's a cell or a huge vesicle. And it's just crazy because we, if we try to understand biochemically what's inside of these structures, what's stabilizing them. What about the biophysics of the membrane, for example, leads to these, we don't actually know.

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and read more with Literati. That's L-I-T-E-R-A-T-I-com slash Mindscape. So your task was to fix the cell. The Vendor Institute had stripped away parts from a mycoplasm and made a sort of minimal self-reproducing cell, but it didn't reproduce very prettily. And you wanted to sort of give it a little bit more coherence somehow. Is that fair? I think that's fair. And it's a microplasma. And so you may or may not care for your purpose, whether the cell looks pretty at the single cell level. You know, your end goal might only want a pretty minimal cell that gives you this normal-looking colony.

But if you're talking about cellular engineering, you know, and you're talking about inserting new functions, inserting new genes, like understanding there are folks who are working on whole-cell models of these cells to try to really drill down and understand, well, what is each molecule doing? What is each gene product from each gene doing so we can draw direct lines from genotype to phenotype and phenotype to gene? That is to say from the DNA, the genotype, to the actual organism, the phenotype. That's right. And so, you know, as an engineering tool, you know, cell size and shape is one of the most basic aspects of cellular life. Like, maybe we should understand this guys. And so, you know, JCPI had declared victory. And in many ways, it was a great victory.

They developed a lot of impressive techniques that have pushed the feel forward. But I thought it was really important. And I'm glad they thought so also to take a closer look at division in these cells and trying to understand these different phenotypes, these different morphologies, the sizes, shapes. Let me actually back up because I think there's probably a lot of details here that would be fascinating to the audience. I mean, when you say that you take the DNA in one of these cells and you chop it up and you turn off or scramble some genes, number one, how do you do that? It's not,

I think that probably a lot of people have in mind that, you know, you have tweezers and you stretch out the DNA and then you like chop off the ones you go. You just reach it and you do it. You just use your fingers, right? I mean, these are very tiny molecules, these DNAs. This is a great point. And, you know, again, as a physicist, and I have a great interest in mechanical systems, you know, I love thinking of tweezers. But in this case, the tweezers are, well, this is what makes Lee G's work in particular

so heroic and the other scientists at JCPI, because they're the ones who really, develop the capability to reach into this minimal cell and genetically modify it. So what you can do is, so first of all, let's back up a little bit here. Yeah, please. So the minimal cell, I mentioned it was called JCV-I-Sin 3.0. Well, there was a 1.0, and 1.0 involved printing DNA on a machine. So this DNA had never been in a living organism. And then what you do is you take a cell, make its DNA such that it's no longer expressible.

So essentially you have a zombie cell now. You kind of take out its brain. You've got a zombie cell. And then you take your printed synthetic DNA, the synthetic genome, and you kind of, well, we don't know exactly how the genome transplantation process works. This is something that's still an open question. Okay. But it does work. The yield isn't great.

So this is something that personally I would like to work on more to understand, because I think if we could print, just as an aside, if we could print any DNA we wanted and insert it into any cell, whose genome we've, you know, inactivated, think of what would be possible because you wouldn't have to go through any living intermediates. You wouldn't have to make these kind of adebatic, you know, incremental changes to your cell to arrive at what you want. You could just print it. 3D print your life form, yeah.

Anyway. But we can't, but we almost can. There's like some sort of hacky way of doing it probabilistically or something? What did they do? So we tried to do it in microfrolyics, but the yield was too low for us to see what was happening. But they think that part of the secret sauce is that you know, you know, have your cells growing in a fluid in their basically in the broth that they grow in. And then you add a polymer peg, polyethylene glycol, of a certain molecular weight at a certain

time in the growth, at a certain temperature, and the moon is in the right phase. And, you know, you've got the person doing you guys the magic hands. And by the way, magic hands are actually a broader issue with regard to reproducibility and the biosciences. So, so it's a thing. And what happens is the peg makes cells kind of slam together. So they slam together with this DNA that you want inside of them. We think there's something like a big syncytial cell that forms. And then at their next growth cycle, they kind of divide apart. And so a sensual cell is one where it has multiple genomes inside of it.

So think of all of these cells forming one big membrane together. And then when they grow apart again, some will have the new genome that you've printed that's synthetic. Some might have the old and activated genome. But at any rate, once you grow them up, you now have your zombie cells come back to life with the new genomes. And part of the process of putting together these synthetic genomes was that Dan Gibson and folks divided that genome up into different pieces. and because it's hard to manipulate a whole genome at once. If you think of like, imagine a really, really long piece of cooked spaghetti. I mean, really long, you know, and if you wanted to pick it up and move it somewhere,

chances are it's going to come apart in your hands. You might get some breaks or you might nick it somewhere, right? And all of that would mean if it were a genome that it's not going to express in the way that you want anymore. So they were able to manipulate this genome in yeast using very interesting techniques that I don't fully understand, but I'm thankful that they did. And one of the reasons I'm thankful is that one of those segments, we call the segment six, ended up having, so first of all, these segments gave us handles for looking at the gene content in each of these segments to try to understand, well, does this segment have the genes that affect shape and size of the minimal? cell. So one of these segments, segment six, actually, you know, so it did seem to have these genes. And so what we could do then is narrow down our search to a much smaller number of genes. And then through a much more systematic approach, you know, testing groups of genes and then single

genes, we drilled down on seven genes that seem to together reconstitute control of cell size and shape in the minimal cell. So now there's this JCDI C-3A screen that I think something like 43 different labs are using around the world to study the minimal cell. So we call it a nearly minimal cell because it does have more genes than CIN 3.0. But what you get in return is that you have cells that are much better behaved at the single cell level. So you had what was judged to be the minimal cell, then you added in seven more genes, and that helped it reproduce in a aesthetically pleasing way.

You know what the real kicker is here, too? So those seven genes included two genes that are known to be involved in cell division, FTSC and step-app. But the next one is a hydrolyze with some unknown substrate. And then four of the genes, we have no idea what they do. We think that they might encode membrane-associated proteins, but we have no idea what they're doing. So even in this most basic aspect of cell physiology, we run across these genes of unknown function.

And they are really an elephant in the room when you talk about these whole cell models, people are trying to build cellular engineering or synthetic biology general. There are so much of the genome that we aren't able to tie back to some function. So you, but you do, you have convinced yourselves that if you didn't include every one of these seven genes, it wouldn't do what you wanted to do. So you're not sure why, but you need all seven of these genes. That's right. And one thing we haven't mentioned is that this is an absolute tour to force.

this reverse genetics approach that we took. I think since I got involved in the project, I think it was seven years until we published, which is long, even for biology. It's short for particle physics, as you know. But okay, I mean, so I'm going to ask the even dumber question that I ask for every cellular, molecular, biotype person I have on the podcast. What do you mean when you say a gene? Like, I know what a base pair is in DNA.

I have this vague feeling that a gene is a group of base pairs that does a thing. But, like, how do you operationally pick out which part of the DNA strand is the beginning of the gene and then the end of the gene? Like, who says, how do you figure that out? Chris, this is a fantastic question. And personally, I rely on the experts in this area. This is not my area of expertise. But there are databases you can go look at where people kind of parse genomes. And it's not all settled also.

People might say that, well, you've got a group Bs together, these base pairs together in order to form a gene that makes some protein. And then somebody might come on and say, actually, you've got to shift it a little bit this way or that way. That makes me feel better because I didn't understand it. So if it's not understood, then I feel like less behind the curve a little bit. I defer to the experts in this. Right. Okay. Very good.

And I should, I probably should footnote something because I think I mentioned the nucleus of a cell at the beginning. But of course, these are probably going to be prokaryotic cells. There's no nuclei in them, right? That's right. And I think that one of, it would be a really interesting capability to take this genome minimization process that's been demonstrated for this very simple obligate parasite, this mycoplasma, and apply it to a eukary. So a cell that does have a nucleus, maybe a yeast, or maybe a mammalian cell, and, you know, see what we learn from that.

Okay. Because this minimal cell, it isn't just a thing, right? It's not just JCPI, Syn3A, and we're done. It's also a whole repertoire of tools and tricks and measurements that we can apply to any number of biological systems. And how many genes total in this minimal cells? So JCP3A has 493 protein and coating genes. And just for reference, an E. coli has about 4,400 genes, and humans have about 20,000.

Although I think that's, we're still sorting that out. But then there are other, they're like dandelions or whatever, they have way more than human beings, right? like human beings are not the maximum number of genes or anything close. No, but, you know, it's a natural point of reference for us. Sure. Oh, yes, absolutely. But I don't want to put us too much on our high horses. We're not the most numbered.

We're actually kind of more efficient in some ways, right, than some organisms. Well, I appreciate your comment, but I actually do want to put us on our high horses for a second here. Sure. Because when I was talking about the genome minimization process, I mentioned that there were these sort of arbitrary criteria that you're minimizing. with respect to. And one thing that I find so fascinating about the creation of, you know, proto-life, synthetic cells, minimal cells is how much of the scientist just unavoidably ends up in that process and in that product, right?

It's almost like it's an art form in some ways or a mirror where we're using this new medium of synthetic biology or engineered biological systems to hold a mirror up to ourselves. And I'm hoping that, because there's sort of two ways to get past the N-Equels one problem of biological systems. So just to back up here a second, one of the challenges of creating synthetic life is that when we look around us, we really only have one example of cellular life, and that's what's evolved here on Earth. So there are sort of two ways. I think that two main ways where we could get more examples. One would be to go out and find it somewhere else.

Hopefully we'll recognize it. The other way is for us to build it. I'm hoping that we're not so biased by ourselves and ourselves that we aren't able to imagine what else could be possible. And this also brings me back then to your question about the rules of life. And what are they? And I think so much is unknown here that as we go about engineering biological systems, it's almost as if we're not limited by the fundamental physics of it, right?

The laws of the universe of it. At this point, it's almost like we're limited by our imaginations. The minimal cell that you mentioned with almost 500 genes, I presume that once again we have a high degree of confidence that if we removed any one of them, it wouldn't work anymore, right? That's what it means to be minimal? That's right. And I should note that there are about 100 genes of unknown function left in that cell.

So we don't know what they do, but we know that if we didn't have them, things would go wrong. That's right. There are also, though, some subtleties in this, too. So imagine if you have an airplane. And you want to take parts off that airplane until it's not able to fly anymore. So let's say that I remove an engine, but I'm going to say that's not essential because I've got another engine. But what if I remove both engines, right? So the way, the process is sort of path dependent on your physics, right?

So it matters how you get there to the minimal cell. And you can find yourself accidentally deleting genes that were essential. Like we did when we developed 3.0 and we needed to put genes back in to get controlled. of cell size and shape. And even though the actual DNA was synthesized, if I understand it correctly, it was all based on real genes that appeared in living organisms. No one is yet going in and building base pair by base pair new functional genes. Is that right? Wow, that would be incredible if we could do that. We are not there yet. Okay. But, you know, this point out that there are a number of challenges still. And for minimal cells, I think that we need to know what these, we need to learn

what these genes of unknown function are doing. I think that we need to arrive at a more genetically tractable minimal cell. And what I mean by that is that we have tools where not just, you know, the properly indoctrinated and practiced folks at JCDI, but anyone is able to very easily genetically modify these cells. So right now, they put something called landing pads into the genome, where you can add genes easily at those places. So that does help a lot. But for right now, if you want to delete genes, you have to go through this docking genome and yeast, manipulating it there, and then go to do this genome transplantation process to arrive at the modified genome. Now, for right now, there aren't so many people working with this cell that JCPI won't generously offer to help you with this.

But at a certain point, I think our hope is that more folks will take up this system. Because there's so much to learn and there's so much to know. Clearly, yeah. Other challenges for the minimal cell is how do we generalize? that minimization workflow for other organisms. And then what is close to my heart, because I work at the National Institute of Standards and Technology, so I'm a metrologist.

I study the science of measurement. I would like it, and we could make it easier to measure the current minimal cell. So this thing is really small. It's about the same size as the wavelength of light. So it's really hard to see. And so I was talking with my class, collaborators. And I said, well, can we just make this thing bigger? Yeah. Like, if we really can

just engineer the size and shape, can we just make it bigger? Well, maybe. Are there techniques to swell the cell? Or, yeah. So if this set of genes is needed, we think, minimally, at least it is, let's say, a minimal set, right? Like, if we removed anything from that set, like you say, it wouldn't work anymore. But maybe there's another set that is containing different genes that This also has the feature that if you removed anything, it wouldn't work either, right? I mean, maybe there's more than one way to be minimal. That's so important, and I'm so glad you brought that up, because, and this is why I want to focus on that workflow of minimization, because I think at the end of the day, can I say this?

I don't want to get in too much trouble, but mycoplasma is not that useful for people. It's good as a research platform, but it's not that useful. And so if we can empower people to make minimal versions of their own cells of interest, minimized according to their own criteria, imagine what we could learn. I think it would be really empowering. Hey, everyone, it's Cal Penn. I'm the host of Earsay, the Audible and I Heart audiobook Club. This week on the podcast, I am sitting down with Ray Porter,

the narrator of Andy Weir's audiobook Project Hail Mears. Mary, massive sci-fi adventure about survival and science. And what happens when you wake up alone very far from Earth? I really had to make a decision because I caught myself getting that frog in my throat and starting to get teary as I'm narrating some of these sections. And it's like, okay, yo, yeah, yo, is this indulgent? And I really thought about it. I was like, no, at this point, it would kind of be betraying the trust the author and

and the listener have in telling this story if I don't go through it. But there's places in this book that deeply emotionally affected me, and I left it on the mic. That's great. Because it served the story. People will say like, oh, my God, I cried at the end. It's like, yeah, dude, me too. Listen to Earsay, the Audible and IHeart Audio Book Club on the IHeart Radio app or wherever you get your podcasts. When people turn to telehealth or weight loss, they're looking for real support.

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That's orderly meds.com slash podcast. Individual results may vary. Not medical advice. Eligibility required, C-Sight for details. And even if this particular species is, like you say, not that useful, can we nevertheless relate this set of genes, the 400 and some genes, the 400 and some genes in the cell, are these same genes elsewhere?

Do they also exist in other kinds of cells? So we feel like this is an instruction set that is common to all kinds of life, or is it not? That's true for a lot of the genes that are in the cell. And this is a common technique that cell biologists use, where they look at related organisms or even not-so-related organisms and try to guess what the function of different genes or proteins is.

And this is one of the reasons why it's a little vexing that we still don't understand the mechanism behind cell division in these cells. Because we can't point to a mechanism so far from another cell and say, oh, our mycoplasma is dividing like that other cell. But one reason why mycoplasma was chosen is that it comes pre-minimized by nature as an obligate parasite. So it's been kind of singled out for a while as a candidate hydrogen atom of biology. And I want to mention, too, that this idea of a minimal cell is really not new. I mean, way back in ancient times, you know, philosophers love to think about the building blocks of matter, including life. But more recently, in the 20th century, I believe in the 1930s, a bunch of scientists got together. physicists were involved, I think even in the leadership role, and they decided that a minimal cell could exist and you know what, maybe it ought to exist.

So because it can do three things for us and it still can do three things for us. It can tell us maybe how cellular life came to be. Maybe we can learn something about the history of life. It might offer a platform for understanding life today. So for understanding the present, cellular life. of our life, including ourselves. And more recently, especially with the work that I've become involved in, I think that it can offer a platform for the future for what life could be.

And more specifically, what could we engineer cellular life? We're at this really exciting time where we have the ability to hopefully soon make lineage agnostic life. And this is a term that I'm stealing from Andrew Endy at Stanford. Okay. I heard him use this term, and it was just such an eye-opener for me, because up until now,

every cell has come from another cell, but now that doesn't need to be the case anymore. Right. So what does that mean? What do we do with that? How do we understand that? I mean, you actually raise a challenge that I hadn't quite appreciated before about the origin of life, which you mentioned, something that we can try to learn something about. If the minimal cell has almost 500 genes and genes have lots of base pairs in them, that sounds like there's

quite a gap to be bridged from a bunch of macromolecules to a living, functioning cellular organism, yeah? It's going to be a while yet, I think. And so stay tuned, you know, hopefully in our lifetimes. We'll get there. And this gap is one that we're trying to. We're trying to be a while. We're trying to close from both ends, right? We're using this bottom up approach and this top-down approach. And the challenge that the bottom-up folks have, I know I shouldn't pick favorites. Maybe I am here a little bit. But the bottom-up folks, there hasn't been success yet. And so it's very difficult to focus their efforts. Whereas if we start from something that's already alive, we're good at killing things.

Yeah. No. Or even injuring them, yes. One way forward, I think, in the top-down approach, is to use those capabilities of automation in the laboratory machine learning to make lots of different versions of the genomically minimal cell and then put them in lots of different environments because they're minimized in a specific environment, right? It can't separate those two, right?

in the same way that, you know, we're nice and alive in our offices right now, breathing the air around us. You take that away and, you know, you might say, well, maybe they're missing the gene that they need. Well, but really you just needed something in your environment. Right. But if we can, if we can, you know, massively parallelize and automate this process of minimization, maybe that's a path forward for learning new and interesting. things about the minimal cell. Maybe that addresses what I was going to ask next, which is if we have this minimal cell

with 400-some genes, and as you've very provocatively emphasized, we don't know what some of them do. How are we going to learn that? Is it just a matter of like taking one by one and seeing what fails, or because they do interact with each other in such crucial ways, do we need to be more subtle than that? You know, up until now, learning the function of each gene, it's really been hard one. And I've experienced that personally with these seven genes that we've identified in the minimal cell over. I even made a T-shirt that said seven genes, seven years.

All right. We need to speed things up a little bit, I guess. You know what? We really do. And now that we've gone through this process once, there are all kinds of things that we would do to speed it up. Use automation. Use proper design of experiments. So for you biologists out there or other folks who don't know that this is a thing,

please know this is a thing, where you can more efficiently design your experiments to get better answers out of a fewer number of trials. We can do that. We can work with our machine learning colleagues to interpret the data because numbers are great, but if you can't get any meaning from them, you know, what are they worth? Yeah. And I mean, this might be jumping outside of what we've been talking about, but there's the issue of how the genes come together to make a reproducing cell. But then there's also, in bigger organisms, the issue of how cells come together to make an organism.

And, you know, cells differentiate in interesting ways. Are there, is there a parallel track where we get to learn about how cells interact to make, like, morphology and things like that based on what genes they have? So even just last week, I was at a conference with those folks. So there's this whole ecosystem of different groups of researchers. And, you know, I have spent some time having interesting conversations and interactions with the build-a-cell folks and the minimal cell folks and the synthetic-cell folks. But then there's also the tissue engineering folks who interface with the, you know, stem cell differentiation folks and the oral. organoid folks and the soft robotics folks. And you can kind of step your way through all of these adjacent related, very interesting

fields. And it's just the most fun you can have. And I'm hoping we can all learn from each other because you're absolutely right. At the end of the day, wouldn't it be fantastic if we could build capabilities not just at the cellular level, but also at the multicellular level so that we can engineer life at all scales. and, you know, even extend it a little bit further to become much better at interfacing biotic and abiotic systems. So I glean from your answer that even though this would be very, very exciting and we should try to do it, we don't know that much yet. We did have Michael Levin on the podcast from Tufts, and he talked a little bit about how organisms seem to have some kind of memory of what their shape was supposed to be.

like if you move the eye of something, it'll move back and so forth. But roughly speaking, I get the impression this is all kind of mysterious. We don't know, even if we don't know how a specific cell regenerates happily, then knowing how a macroscopic organism is going to do that, it's going to be a much harder challenge. How wonderful that there are these interesting questions for us to investigate. You know, there's fantastic progress in all of these areas. Again, I'm not the best person to...

speak to them. I guess I'll just put an advertisement for, you know, the life of a scientist here and just invite anybody just warmly, please come investigate these with us. Good. It's a wonderful community. And everybody's ways of thinking about different ideas and approaching the problems, they all bring a unique perspective. And, you know, clearly the way we've been thinking about is so far with the information

we have. we're not there yet. And so maybe you can come and bring your ideas and, you know, lead us all to the aha moment. Good. So let's get back to the cell or the individual cell. So you've hinted at the idea that one of the motivations for building this minimal cell is that we can then de-minimalize it. We can sort of do things with it. You know, we can start adding things that we want it to do. So what are your favorite potential? potential applications. And we can be a little speculative here. Like this is not a grant proposal review. We're thinking about what the future might hold. This is a great time for me to put in the obligatory nist disclaimer. So nothing I say here represents an official position of the U.S. government or implies endorsement of any company, commercial product or service. We promise. Good. Very good.

So in some ways, what we're talking about here is, and I'm going to use this term on purpose, even though it is provocative, this idea of gain of function. Okay. So in some ways, you could, so typically people use that term around infectious disease research. But if you take it literally, gain of function is engineering biology. And if you talk about building a minimal cell or life from scratch, that's almost the ultimate gain of function research. So I'm imagining that we learn enough. And maybe the minimal cell gives us a path forward with us.

where we can build up best practices, maybe some rules, design rules, rules of thumb, protocols, where for different classes of function, we understand how to put those back into the cell, which functions play nicely together and which don't, right? How do we manage resource utilization in the cell? And all of these bring us back to that idea of control in biology. And, you know, you've been using the term engineering

biology and cellular engineering, but unless and until we have a rigorous control engineering or biological systems, it's kind of an aspirational title to say that it's engineering. And so one thing that my lab works on is building sensors. So if you imagine a control, let's say, one that you might be familiar with from the cruise control in your car or the thermostat in your house, what you have is you have some some desired output of a system. And there's a controller, then that controller might do something

to actuate some change in the system that leads to some output. Well, if you can close that loop with a sensor, with something that measures that output, you can then feed that error back in to the controller to make sure that, you know, you stay at your desired output. Right.

So what my lab does is it uses biological parts to make living measurement systems or sensors inside of cells. Because biology is measuring itself all the time. Seems like we ought to be able to make measurement tools out of biology also. Free is great, but only if it's useful. Free credit scores from some apps can differ by as much as 100 points from your actual FICO score that 90% of top lenders use when you apply for a credit card, personal loan, car loan or mortgage, that can mean a higher interest rate, a bigger monthly payment, or worse. Denied.

My FICO gives you your actual FICO score. The score lenders use straight from the company that created it. For the moments that matter, get the score that matters, your FICO score. Visit myfico.com and get started for free today. Okay, so you, when you started talking about that with the thermometer, sorry, the thermostat analogy. I was going to ask, you know, well, so why have biological sensors if we have perfectly good physical ones? But I guess you've already answered it because you said you're putting them inside the cell. So what are you trying to sense inside the cell? If it gets sick or

if it's stressed or if it's happy? Absolutely. You're spot on. So I want to give a shout out too to the communities who are, they are trying to put miniaturized physical sensors inside of cells. So they look like nanomachines or all kinds of great and interesting things. And that maybe falls more under the biotic-abiotic interfacing research at a cellular level. Now for us, we're engineering proteins, for example, to measure small molecules in a cell's environment, or you can imagine measuring the stress state of a cell. And one place where this matters is imagine that you have a company and you're growing vats of bacteria. that you've engineered to make a small molecule product or a medicine.

And so you might want to know, because you've got a lot of money tied up in that bat. And, you know, as we know, biology can do strange things sometimes for, you know, inexplicable reasons. Because we don't understand it well enough yet. So you might want to have sensors built in so that, you know, if you have a physical sensor outside of the cell, you might just know that maybe the pH is off. or the yield of your product has changed in a way that you're unhappy with. But imagine having a sensor in each cell. In fact, imagine having that control loop in each cell.

So when they yield output from that cell does something you don't like, that actuator in that control loop then kicks that cell into becoming a high producer again so that you can maximize your profits and then fund more research with your profits. So that is bringing it closer to this true goal of engineering rather than just sort of building an individual cell, putting it to work in more specific ways. You know, you can think of biology is one of the most advanced manufacturing platforms that we can imagine. And there's a place, of course, for, you know, industry, the way that we built it up up until now. But imagine if we could grow our products the same way we grow corn.

Or imagine instead of a 3D printer that you smell to plastic in your house, you have something that looks more like a breadmaker and you actually grow the products that you want. There are lots of visions here. And I think that we don't know yet what it's going to look like. Presumably, well, so for one thing, I want to get on the table that, as far as you know, no one is putting 5G sense networks into vaccines or anything like that, right? because it sounds very close.

No, no one is. And, you know, the vaccines, the way they are now are just such a marvel of biotechnology. It's almost, you know, I'm almost like personally offended that you're almost minimizing this achievement by saying that, not you, but one, you know, people. The conspiracy theorists, yeah. But at the same time, there's absolutely nothing wrong with questioning technology. You know, if you don't question, if you don't feel like you can ask, then how will you learn? But I presume that fighting disease would be an obvious target for these engineered cells. Absolutely. So there are cellular therapies out there already. You have RT therapies. And this idea, so I want to be clear here that the idea of control is important because that in one important way, because

that leads to safety. So if you know you have a well-controlled cell, now you can be more confident about selling a cellular product. And you can be more confident that every cell that you sell to someone is going to behave in exactly the way you've programmed so that you're actually curing someone's cancer instead of giving them cancer. Right. Kind of important, yes. You know, and we're,

We're used to, again, I'm going to go back to an airplane, we're used to stepping on airplanes and being confident that we're going to arrive safely. And I would love it if we could get to the point where we don't even think about cellular engineering or engineering as separate from just engineering because it's so predictable, it's so reliable, it's so robust, it's so safe, and it's such an integral part of making people's lives better. Because that's what we're after at the end of the day. Well, as a good example of that, quite a while back, I heard a famous scientist speculate about the possibility that we would be able to engineer cells, monocelular organisms that we could release into the atmosphere and they would eat up CO2 to help solve global warming or something like that. Now, obviously, there is a problem with the runaway.

You don't want to get away with all the CO2 in the atmosphere. But beyond diseases in our bodies, is that kind of large-scale engineering? something that we could imagine as part of the future of cellular engineering? I think people are imagining that. And, you know, there are people out there thinking about how to best bring to bear biotechnologies to any of the big challenges we face collectively. And climate change is, of course, one of them. Food security is another.

Reshoring our supply chains is another. And you don't necessarily need to release cells. to get them to gobble up CO2 for you. Why not change our biomanufacturing processes so that they are carbon neutral or carbon negative? Up until now, we've very much been limited by energy. We've been worried about how much energy does it take to manufacture something.

But what if we switched our thinking and thought instead about, well, how much carbon? What's the carbon budget of my manufacturing process? People are engineering plants or trying to figure out better ways of growing plants so that they lock more CO2, for example, in bigger root structures. And so over time, you're sequestered more. There are a lot of ways to think about doing this that don't necessarily go straight to

just blanket the, you know, blanket the globe in it. Okay. And the other potential application that I've heard a little bit about, which I wanted you to comment on, is building either little robots or computers out of either DNA or cells. And, you know, my own questions about this, but maybe I'll let you say is, is that one of the things that you can imagine we're going to be pursuing? I think that the idea is one that is worthwhile. I don't know that it's going to look like computers that we have already. I mean, silicon is very good at what it does.

But biology is not silicon. And one of the things my lab is working on is, you know, we're building these sensors, but then how do we process that information from the sensor? So up until now, a lot of folks have been working on Boolean logic inside of cells. So that's very similar to the kinds of logic that your computer is doing. But there are other ways that we know to make computers think airspace. What if we could put a different kind of information processing inside of cells? Maybe we could, so for example, there are folks who use DNA strand displacement circuits to

make a neural network like computations. Could we put that inside of living cells? Maybe we wouldn't use DNA. Maybe we would use a different kind of biomolegium. You know, so there are different ways of thinking about. how to put information processing inside of cells. And, you know, the cells are doing it all the time. So, you know, maybe we should start instead of, instead of by trying to, you know,

jam computers into cells, maybe we should just, you know, invite this cell to T and say, cell, you know, tell me about yourself. Like, how are you processing information, you know? And this is, this goes back historically to this rip between physics and biology. You know, when I was a young scientist, the physicists were great at going up to biologists and saying, we're here to solve it for you. Well, that's no way to make friends. No, no.

So I think what we're seeing now is people coming together and taking a moment to really learn what the other knows, see the value in it, and try to put together the quantitative approaches from physics and engineering with the incredible descriptive knowledge that we find in biology. Well, and something that one of the reasons why I was questioning this particular goal is because, as a physicist, I'm impressed by the fact that the environment inside a cell is so very, very different than the environment that we have in our daily lives or in a computer, right? The thermal motions of molecules are huge compared to other things that are going on. It's a very noisy, hard-to-be- predictable environment. Does that get in the way? of trying to build computer-like things on those scales? You know, I prefer to think of it as a feature instead of a bug. So I think you have to be willing to see it not as a liability, but as an asset.

So in a previous life, I built small nanofluidic staircases and crammed long DNA molecules in very tight confinement at the top of the staircase, and they would walk, basically. they would jiggle around in this environment, and they would entropy for roots. So there's an entropy gradient for the DNA molecule in this staircase, and they would come out, and we were thinking about,

well, who cares about this? It's kind of a cool widget, I suppose. So it's like a slinky going down the staircase? Nano slinky, absolutely. Nano slinky, okay, good. And when I came to engineering biology, I never really lost, lost that vision of molecules moving in ways that seem controlled but are driven by random processes.

Right. Right. And so how is biology, how are biological systems arranging themselves such that they're taking full advantage of the biophysics or just the physics, you know, the thermodynamics of what's happening in their system? Because, you know, if I were a cell, I would probably be lazy. You know, I wouldn't want to use more energy than I would need. And so I would want to harness everything that, you know,

it's just going to happen naturally with minimal efforts. So when I think about minimal life, and I know that Jeremy England has also talked a lot about this. So how would I harness the kind of gradients, the various gradients that I'm experiencing anyway? And then only modify those that are worth it to me because they give me some fitness advantage. And in some sense, this is a place where the relationship between energy and information comes to light, right?

Even I've written papers about this. This is a frontier right now of statistical physics, non-equilibrium dynamics. There is some sense in which biological organisms use information to do work, right, to make things happen. And I've always been curious and I have no strong opinions, about when that happens. Like at what point do you say, oh, this is using information rather than this is just rolling down a hill in a sort of a mindless way?

Do you know the answer to that, or do you have opinions about it? I love talking about this stuff. Again, I defer to the experts. I would love to sit down and think about different experiments to view. As we think about our capabilities with putting together engineered systems, whether we might use an element of nanotechnology in our experimental design,

you know, a little biology, you know, whatever pieces you want to put together to control the system in a way that you're able to ask and answer very cleanly some of these questions that you're posing. I think, you know, we just need to find the right people who have the right friends with all of these expertise together. I don't know yet what those experiments would look like, but I think they're coming.

Sounds like a good topic for a workshop at the Santa Fe Institute. I might have to propose something like this. Sign me up. All right, very, very good. And so the last sort of topic I wanted to talk about, unless you want to put others on the table, is we've been talking about cells the whole time, but in fact, I get the impression that a fraction of your research

is on the idea of cell-free synthetic biology, like break out of the boundaries of the cellular membrane, but still do biology. So what is that even all about? Great questions. So I mentioned earlier that you can consider life on a spectrum. So if you have a spectrum with chemistry on one end and, you know, a full-blown naturally evolved, let's say, mammalian cell on the other end, a human cell,

like there's a lot of space in the middle. And so if we kind of walk along that spectrum, we might find, you know, if you go from chemistry to something called a pure system where people have reconstituted certain molecules and you can get protein expression. If you walk a little further, there are folks who grind up cells or mush them up and get rid of all the spatial organization and just keep the components in a tube and can express proteins or do other things with that. So it can still do biology even though you've smushed its organization to smithereens. For example, then you can keep walking down that spectrum and you can come to a minimal cell and, you know, et cetera, et cetera. but to stop at the cell-free system,

it's such a bad name. But there it is. Sometimes they're called self-free expression systems or transcription translation systems. These are all, you know, words to go Google, TXTL systems. What's really nice about them is they allow you to use the machinery of biology without that set system needing to be alive. And so, for example, if you're making a product,

you don't need to figure out how to transport that product outside of the cell across a membrane to then go, you know, separate it out and sell it to something. You can start thinking about doing chemistries that would be lethal to a cell. And the cell-free system might not care. The whole system is open. And so you can literally just stick your pipetarin and put more of whatever you want into it. more energy, more, you know, more molecules to more precursor, whatever you want. You can just stick it in.

And so people are using cell-free systems as a bio-manufacturing platform for these reasons. Another way people are using cell-free systems is as a stop along the design-built test learn cycle. It's a workflow in biology. and if you're engineering biology, that's one way to think about your workflow. And so I might start by saying, well, I want a cell that does X, you know, function X. So I'm going to design the DNA. I'm going to order it from the DNA store.

It's going to come to me. I'm going to assemble it. And I'm going to put it into a cell. For example, I might put it on a plasmid and subject my cell to a high voltage such that the membrane breaks down partially and the DNA goes in. and I electro-forated. And then, and then, you know, let's see if we get function X. Well, very often, you don't. And so what went wrong?

And in the meantime, you've been going through this workflow where you've been waiting for cells to grow, that takes time. It's much faster if you can take an intermediate stop and use a cell-free system to just quickly test your DNA because it doesn't have to grow. like you've got your cell booth in your you know your minus 80 freezer just pull some of that out that you'd make freebies right put your DNA in and say well you know does it glow or you know

do do I can I do a test that will tell me that I can be reasonably certain this is going to work in vivo if I test it out first in V3 and so the idea is that it would make your your workflow faster because you're not needing to do everything in cells. Now, we, just like for cells, we don't have a great understanding of everything that is in that cell-free system often, especially if it's lysate-based, you know, if you're making it from the guts of real cells. People are working on it. We do have some good models, but there's room for improvement. And this makes me think about, once again, this origin of life question. You know, typically when I wrote in my book The Big Picture about the Origin of Life,

I repeated what I was told, which is that you need compartmentalization, reproduction, and metabolism to qualify for life. And compartmentalization always seemed like the odd one out there, right? I mean, I can see what the advantages are, but the necessity was a little bit more obscure. Is it if following what you said at the beginning of the podcast that we should be open-minded about other kinds of life, could we imagine that if we go to other planets, life does not happen in cells, but it's just more of a continuum that sort of smushes together in interesting ways? I think that's a wonderful thing to think about.

You know, some folks think that life might have arisen in rocks where there are very small chambers. I think there's also a lot of room in engineering biology for figuring. out how to put back in engineered compartmentalization or, you know, ways where you can gather up molecules of interest in closer proximity so that they react in ways that are more advantageous for you, you know, and that might look like some of the really interesting liquid liquid phase transition work that folks have been doing inside of cells. You know, other folks are using microfluidic and nanofluidic chips, so where you have these very small chance, in say glass or silicon, where you're confining biomolecules,

maybe a cell-free system, and looking at how those behave under this engineering confinement. Also spreading it out on chip gives you an easy way to image, so to measure what's happening. So I think there are different ways people are going about influencing or engineering back in something like cellular confinement or nanosephemymen confinement or nanoscale confinement.

So, I mean, there's just too many good things that you dropped in there in terms of questions we don't yet know the answer to. I remember once, I told this story before, so maybe it appeared on the podcast, but I invited a biologist, Bonnie Bassler from Princeton to come to give the physics colloquium at Caltech, and she was talking about bacteria and quorum sensing. and my graduate students afterwards were just shaking their heads with like, it's so easy to find questions we don't know the answer to in biology, but we can do experiments to try to answer.

In particle physics, as you know, it's just harder, right? It's a more mature field. So I'm very excited about the frontiers that you're working on. It's a great time to be in this field. I do want to mention that, you know, as a physicist, I very much grew up in the shadow of some of the physical. six grades who were involved in bringing about the nuclear age, right? This is the cautionary tale that we're all, you know, we're all taught.

And I'm hoping, I'm very optimistic that synthetic biologists, engineering biologists, will learn from history and think long and hard not just about what we could do, but what we should do. And the stakes are so high for us. We have to build trust if we're going to. arrive at a future where we can all thrive. I remember there's another story that I've often told. I do science consulting for Hollywood.

And my wife, Jennifer, was the director of a consulting organization. And she set up for a movie, a bunch of scientists that were tasked with, you know, if we wanted to build a terrible plague, you know, a viral plague that would kill a lot of people, what would we do? And the scientists came together. They came out going like, oh, we could totally do this. And it is, you know, I think physics has sort of passed that threshold where the research that is going on in fundamental physics now, Higgs bosons or dark matter or string theory or whatever, is not in any danger of being weaponized in any moment. And that baton has been passed to fundamental biology, where the number of things that could happen is pretty scary.

But I always like to end the podcast on an optimistic note. So give us your most optimistic reading of how the future is going to become. better because of synthetic biology and engineering biology. My hope is that our ability to apply biotechnologies to make people's lives better than, you know, it might look like new kinds of therapeutics. We've already seen it in new kinds of vaccines and cellular therapies. I'm hoping that it gives more people, more access to ways of making things. So you might have heard of the biofab movement.

No. So if you have like Maker Labs where people have a lot of tools for working with plastics and metal electronics, well, extend that to biological systems as well. Yeah. And at the end of the day, we're biological. And it's so important for us to make sure that everybody, has access to what we need to keep ourselves healthy and happy, you know, clean air, clean water, healthy, nutritious food.

There's so much upside to biotechnologies here. Again, I think that we're limited by our imaginations. And then after that, how do we want to prioritize what we could do? Well, it's clear that it works for everyone. Yeah, I mean, thanks for having. very, very rapidly. It's fun to look at the progress in real time. And it is important, as you emphasized, to at the same time, take a step back and think carefully about whether we're doing the right thing as we're doing all of these fun things. So Elizabeth Tjahalski, thanks so much

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