296 | Brandon Ogbunu on Fitness Seascapes and the Course of Evolution

Biological evolution via natural selection is a simple idea that becomes enormously complicated in its realization. Populations of organisms are driven toward increased "fitness," a measure of how successfully we reproduce our genetic information. But fitness is a subtle concept, changing with time and environment and interactions with other organisms around us. We talk with biologist Brandon Ogbunu about the best mathematical and conceptual tools for thinking about the messy complexities of evolution, and how modern technology is changing our way of thinking about it. Support Mindscape on Patreon https://www.patreon.com/seanmcarroll. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2024/11/18/296-brandon-ogbunu-on-fitness-seascapes-and-the-course-of-evolution/ Brandon Ogbunu received his Ph. D. in Genetics and Microbiology from Yale University. He is currently Assistant Professor of Ecology and Evolutionary Biology at Yale, and External Faculty at the Santa Fe Institute. He has been awarded a Fullbright Fellowship and was the Martin Luther King Jr. Visiting Professor at MIT. He has contributed to a number of publications, including Wired https://www.wired.com/author/c-brandon-ogbunu/, Undark https://undark.org/undark-author/c-brandon-ogbunu/, and Quanta https://www.quantamagazine.org/authors/c-brandon-ogbunu/. Lab web site https://ogbunulab.yale.edu/ Yale web page https://eeb.yale.edu/people/faculty/c-brandon-ogbunu Google Scholar publications https://scholar.google.com/citations?user=rHUzZs0AAAAJ&hl=en Wikipedia https://en.wikipedia.org/wiki/Brandon_Ogbunu Public talk: What is Lyfe? Towards a Biology of Context & Complexity https://www.youtube.com/watch?v=8rRNbiYg7fs&ab_channel=SantaFeInstitute

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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• 1:30Sean Carroll?

  Hello, everyone, and welcome to the Mindscape Podcast. I'm your host, Sean Carroll. I'm sure many of you have heard of the idea of a fitness landscape in evolutionary biology. It's a very vivid, visual kind of metaphor, if you want to think of it that way, introduced back in the 1930s by Sewell Wright, who was a population biologist, a geneticist. And the idea is that you consider a bunch of genes that make up the genome of an organism or a population of organisms, and you can imagine changing all those genes, right, by mutations a little bit. And so as a function of the actual genes and how you might change them...

• 2:10Sean Carroll?

  A population of organisms will be fit, more fit or less fit, depending on the environment that it's in. And you can sort of imagine plotting that fitness as a function of what the genes are doing. There's many, many genes, there's many different mutations you can imagine, so it's a very, very high dimensional space. But the metaphor is nice. You climb up to the top of the fitness landscape. In the course of this podcast, we actually wonder whether biologists use this before or after the physicists use the idea of an energy landscape. So since then, I looked it up. The physicists were first. Yay physicists. Physicists turn it upside down, right? You plot the energy of a system as a function of various parameters. And like a ball rolling down a hill, as long as there's dissipation, the system will go

• 2:55Sean Carroll?

  down to the minimum of the energy landscape. This is still a big idea, e.g. in cosmology or string theory, where we talk about the landscape of possible laws of physics in string theory and things like that. Anyway, the metaphor is so nice, so compelling, that it's one of those things you might be tempted to overuse. The reality, as you will not be surprised to hear, is much more complicated than that. For one thing, it's not a landscape, it's more like a seascape, because of course, your environment, which is affecting what your fitness is, is constantly changing in different ways. For another matter, it's not just you, the population organisms that you are part of that matters.

• 3:38Sean Carroll?

  There are other organisms, they have their own fitness landscapes, they are competing with you, or they're eating you, or being eaten by you, and so on. So this is one of the many ways in which biology is much richer and more complicated than many problems in physics, and that's what we're going to be talking about today. Brandon Abunu is an evolutionary biologist who thinks in terms of systems, systems biology, and complex systems in particular, thinking about how we can use things like big data and so forth to analyze how different kinds of organisms, usually for his work, microorganisms, pathogens and diseases in particular.

• 4:20Sean Carroll?

  Will respond to changes in the environment, to changes in drugs, if you're trying to fight a disease, and changes in what other organisms are doing. It's a fascinating problem, both for very down-to-earth, practical reasons, trying to do biology, especially in an era where we can collect an enormous amount of data and maybe even shape what the evolutionary trajectory is, either by selecting different organisms or directly by changing their genomes. And also a fascinating problem conceptually. Like, how do you deal with these giant dimensional spaces? How do you think about them mathematically, practically, computationally? And then how does the whole thing fit into the broader scope of science?

• 5:04Sean Carroll?

  And as you'll see, near the end of the podcast, even the broader scope of culture. Science is done by people, by human beings, who live in a culture. We live in a society. And we need to take that into account when we do our science. So a great example here of complexity science in action. Let's go.

• 5:37Sean Carroll?

  Brendan Abunu, welcome to the Mindscape Podcast.

• 5:40Speaker 04

  Oh, it's really quite a pleasure to be here. Thank you for having me.

• 5:43Sean Carroll?

  We're going to cover a lot of ground, I suspect, over the next hour. But let's start with some basics. We talk about evolution, natural selection, Darwin, et cetera. The basic idea of natural selection, to the untutored mind, like I was when I first heard about it, just sounds simple and obvious, right? Of course, you know, there's mutations. You pick the best ones, you move on. How could it be different? It seems to me that over the past few decades, biologists have really been complicating things. The story has become a lot richer.

• 6:14Speaker 04

  That's that's right. That's one way of framing it. It sure did take a nice elegant algorithm. And that's what makes kind of, you know, Darwinian reasoning so beautiful that is that it is elegant. And even though Darwin himself knew nothing about the molecular basis, right, he really, really did get

• 6:32Sean Carroll?

  Yeah.

• 6:33Speaker 04

  down to the molecular level, right, a process. So that's exactly right. We had the simple elegant thing. And in the last last couple decades, we just learned so much that it's kind of added some nuance.

• 6:45Sean Carroll?

  Is there? Yeah, we'll get to it, of course. So don't worry about having spoiler alerts or anything like, is there a single most intriguing to you way in which we've complicated the picture?

• 6:57Speaker 04

  Oh, my. I mean, how much time do we have? I mean, you know, I think there's

• 7:01Sean Carroll?

  Plenty of time. We'll circle back.

• 7:05Speaker 04

  There's a number of things. So for example, right, I think the whole conversation around the randomness of right, evolution by natural selection. I think what makes it powerful is that there is no formal hand guiding how the variation is generated and like formally steering how selection is operating. That's kind of what makes it so subversive an idea. But what we're learning is that there are elements to evolution by natural selection that very well might be predictable. Okay. And I based on the sort of information that we have, there might be patterns in how mutations happen. Are they really random?

• 7:47Speaker 04

  So this is kind of a little bit of a philosophical question, right? About what randomness means. And I think this manifests even at the molecular level in evolution in terms of how was that random variation that selection is acting upon? How is that actually generated? You know, questions live in all of those domains.

• 8:03Sean Carroll?

  We, you know, we entirely approve of philosophical questions here at the Winescape Podcast, as you know.

• 8:11Sean Carroll?

  Well, you mentioned the phrase "levels of selection", so maybe that's a good place to start getting a little bit more into detail. You know, I've had people on the podcast, philosophers and biologists, arguing about this question, you know, does selection happen at the species level, at the population level, at the individual level, at the gene level? Is it all of the above? What's your take?

• 8:33Speaker 04

  Yeah, now, my selection is all of the above, I think, which is a very, very wimpy answer. But I think it is true. I think the question is, if you think about, you know, the selection operating at all these levels, and they each are responsible for some proportion of what we see. I think the question is, how much is it operating at one level versus another, right? I think it's really that level of nuance. Now, I mostly live at the molecular level. And it's kind of like undeniable that we see selection happening at the protein and gene network and gene level. I think it gets much more controversial when you talk about these social questions about how it happens. And that one, I have, I think it is happening, but I'm certainly not an ardent advocate

• 9:18Speaker 04

  that that's everything the way some others are.

• 9:21Sean Carroll?

  I guess, even at the individual level, I've long had this feeling, but I guess I haven't talked to a real biologist about it. You know, we, in some sense, in the Darwinian way of thinking, we select on traits, right? On phenotypical aspects of things, the length of the neck of the giraffe. But the actual information is carried in genes, which is at the molecular level. And I'm guessing there might not be a one-to-one map here. So maybe we're selecting on things that we don't pass down in the right way, or there's unintended consequences.

• 9:53Speaker 04

  Yeah, no, I mean, just the basic flow of information from, you know, DNA to RNA to protein to phenotype, which is the classical kind of now. That information flow, like you articulated, you know, earlier you asked me, like, what are the big questions in evolution? And it is precisely that there's not a very, very neat mapping between what's happening at the genotypic scale and what's happening at the phenotypic scale. And frankly, I think that's the Greatest existing question, right, in, you know, modern evolution and evolutionary genetics. I think there are multiple ways to get a phenotype of interest. Number one, right, I think there are multiple ways to skin a cat. There are multiple ways to generate a long neck. There are multiple ways to generate a tall person.

• 10:36Speaker 04

  There's more, so you have that, right, as a profoundly complicated uh set of questions. And then the phenotypes themselves that we see, like you articulated, very well may have been crafted by a force that has nothing to do with the genotype. There's nothing to do with what you're inheriting. So it it creates this deep complication uh with regards to being able to neatly map our genetic information, which is, you know, incredibly powerful and being able to tell meaningful stories about organisms and humans on earth.

• 11:06Sean Carroll?

  Maybe elaborate more on what you mean by nothing to do with what you're inheriting. I I I guess I was pointing to the idea that maybe you want a longer neck. So you select for a gene that does that, but that gene also affects other things. And then that goes along with the right. Is that also what you were thinking about or is there more to it?

• 11:22Speaker 04

  Yeah, no, no, I mean, I think I think that's that's one thing, right? So I think, uh, you know, the the silly example is, um, you know, what color is your hair, Sean? You have, you know.

• 11:34Sean Carroll?

  It's kind of brownish.

• 11:35Speaker 04

  It's a red brownish.

• 11:36Sean Carroll?

  Kind of dull.

• 11:37Speaker 04

  So the idea there is, the idea there is there are multiple genetic routes to get to that hair combination color. And we actually know a lot about kind of the evolution of uh hair color, right? But there's another route, which is dying your hair that color. And that has nothing to do with any sort of combination of genetic information. So it's like, so and that's an extreme example of how the environment can do the heavy lifting and all the work that genetics can do. And, you know, yeah, it's an extreme example to make the point, but something analogous is actually happening with a lot of the traits that we care about. They could very well be crafted by nature's hair dye.

• 12:12Sean Carroll?

  Yeah, one of the tools that we use to talk about these ideas is the idea of a fitness landscape. I I don't know whether. The physicists follow the biologists on this because physicists talk about energy landscapes all the time. I don't know who came first there, but and I understand that it's well, explain to us what the idea of a fitness landscape is first.

• 12:32Speaker 04

  Yes, yes, no, the fitness landscape and its relationship and it's absolutely been invaded by physicists. We'll talk about in a moment. The fitness landscape comes from this kind of era in modern evolutionary biology called the modern synthesis with many of the best thinkers ever, largely statisticians and mathematicians, came up with analytical and mathematical descriptions of the evolution process. And so really what this did is it kind of like formalized evolutionary biology as a quantitative field. It really lived at the population level as rather than the individual level. It did really, really beautiful things in a lot of ways for our field. Now, one of those individuals, Sewell Wright, one of the architects of the modern synthesis,

• 13:14Speaker 04

  came up with the notion of an adaptive landscape, sometimes referred to as the fitness landscape. And what this does is it analogizes the process of evolution as a physical surface where you have kind of moving uphill and downhill and that uphill is like you're climbing towards better fitness, higher fitness, maybe like I said, a coat color, a ability to resist the drug, right? And so on the x-axis here, you're kind of walking, those are individual mutations. So it's like evolution is taking these walks up and down with natural selection to do is it can change the structure of that space. And certainly a lot of evolution is

• 13:55Speaker 04

  how do you climb out of a valley? So it creates this mental analogy that has since become more than an analogy. Now is a whole subfield of evolutionary theory where people like study these surfaces. And they basically say, what is the fitness landscape for evolving antibiotic resistance? What is the fitness landscape for evolving height, right? Oh, this is going to be rugged. That means this sort of problem might be more challenging to navigate and solve than another sort of problem. And I think it's really been a helpful, profound analogy that's helped us measure and consider a lot of real problems in the world.

• 14:32Sean Carroll?

  It really bugs the physicists that higher on the fitness landscape is where you want to go. Like, that's not true for landscapes. You balls go downhill.

• 14:41Speaker 04

  It's not. It's not. No. And I've actually given this talk to some like physical chemists.

• 14:48Speaker 04

  And somehow made it out alive. Because that's exactly right. So they were first on that regard, to what degree Sewell Wright invoked the original, like those Boltzmann sort of thermodynamic landscapes. I think that's an empirical question that I'm sure a historian of science has interrogated.

• 15:04Sean Carroll?

  I'm 100% sure that one of our listeners is going to leave it in a comment. So I look forward to that. Thank you ahead of time. So, but I did talk about fitness landscapes in my book, The Big Picture, and like a couple of people pushed back a little bit. They were worried about this idea. I don't know whether it was mostly because they thought it was too compelling an analogy and you might get lost in what's really going on, or apparently it can be misused by creationists also, but anything can be misused by creationists, so that doesn't bother me too much.

• 15:38Speaker 04

  Yeah, that makes sense. I think, well, we'll start with the reverse direction. We'll go with the creationist one. Like, I think what's interesting there is, creationists do have this skill at seizing on any sort of idea that adds real tractability and potential predictability to evolution, as in that being evidence that this was engineered by a maker or something like that. So I think that makes sense. And I think this is a kind of a chat. This has happened to me. I published a paper on fitness landscapes, and I think several creationists cited it. I'm just like, wait a minute, what? I think in terms of other kind of technical criticisms of the fitness landscape, it's really, really interesting because we can think about powerful analogies. Obviously,

• 16:22Speaker 04

  physics has them. Obviously, biology has them, from the selfish gene, the blind watchmaker, to the spandrels, famous spandrels. There's several kind of powerful analogies that we use. And it's like, the fitness landscape can be considered a version of one of those, yet it can be actually mined quantitatively and theoretically.

• 16:41Sean Carroll?

  You can put it in an equation.

• 16:42Speaker 04

  You can put equations to it. And I think that's what makes it beautiful. And I think perhaps the critics of it are saying...

• 16:50Speaker 04

  Y'all need to chill. This is just supposed to be an analogy. Like, you know, it's really just a toy. Now, it's a cool toy. And there are questions that it can answer, but I think just like any analogy, or frankly, any mathematical model, frankly, you have to know where its limits are. It can tell certain things about the world, and it can do so very, very beautifully, but it's not very hard to overapply it to contexts and circumstances and biologies that it originally was not intended to.

• 17:20Sean Carroll?

  I mean, as I said, I'm a big fan of the metaphor, but I do therefore want to sort of probe its weak points, so I know where not to go overboard. I mean, one thing is that you say the x-axis, but of course, there's a gajillion axes, right? So it's very hard to actually visualize this high-dimensional space of all the different genes I could change.

• 17:42Speaker 04

  No, that's exactly right. So that's like the number one criticism of it. I guess what's interesting about that criticism is that it became much more relevant. Technology has rendered it much more irrelevant, right? Because there was a point where we actually couldn't mutate very many things in a gene. And so the analogy actually mapped where we were technologically, you know? Now, I mean, you can take a protein and just generate, I mean, the techniques like deep mutational scanning, and now with kind of there's high-throughput CRISPR-Cas sorts of methods that can change, say, hemoglobin. You can change like every sick variance of every single amino acid in large combination. I mean, these are hundreds of thousands of variants.

• 18:23Speaker 04

  How do you actually depict that in anything resembling a visual space? That one isn't kind of like a neat three-dimensional landscape. That's going to be, you know, an nth-dimensional landscape. So I think that's exactly right. I think the original landscape poses some problems for actually capturing the way information is structured in the real biological world.

• 19:18Sean Carroll?

  I wonder if, and I truly don't know, whether or not anyone has taken advantage of the fact that there are some simplifications mathematically in very, very high-dimensional spaces. Like, I'm only recently discovering this stuff myself in my physics work because it's very useful. You know, in a very high-dimensional space, almost all points on a sphere are on the equator. Almost all vectors are orthogonal to each other, right? I mean, is there, do you know of any work that is saying, "Well, because the fitness landscape is defined on the space of genomes, and that's really, really big, we can say this beautiful mathematical fact about its structure?" Or am I just whistling the dark?

• 19:56Speaker 04

  No, no, you're not. I mean, I think there's, there's a lot of people who ask, I mean, I think there's, the fitness landscape, what's, what's cool about it is that it's simple enough that a bunch of different sorts of suites of mathematics, or physics, or statistics can be used to analyze features of it. So, for example, graph theory, right? It had, you look at a fitness landscape, it has this, it has this graph theory-like character, right? And so there are graph theorists who have, you know, walked various features and mathematical tools from graph theory for the questions in evolution. So I'll say the good things first, is that is, you know, graph theory, analytical geometry, network theory, which is related to graph theory, thinks about these sorts of problems.

• 20:40Speaker 04

  There are people had, have tried to reconcile this thermodynamic, it relate, the relationship between thermodynamics. So I think all of these things have been brought together, and I think all of them have added something. Genetic programming also is another one. They've all added something, but have they in some generated a, a, a unification, right, of a set of mathematical tools to truly understand how evolution works? I, I would say no. I mean, maybe there are people disagree. I would say no. I think right now we have a bunch of different pictures of evolution using these different kind of mathematical, physical, and statistical frameworks, and they tell us some interesting things about individual problems. But I think, I think we need to go further here.

• 21:20Sean Carroll?

  Good. And I guess the other thing which is more down to earth, and I know people have thought about, is the ruggedness of the landscape. You mentioned it already.

• 21:27Speaker 04

  Yes.

• 21:27Sean Carroll?

  So if, in our minds, for the listeners, since there's no video here, if we think about this axis going left to right as being I'm changing genes from one thing to another, I could easily imagine, the vertical axis is the fitness of it, I easily imagine a tiny change in a gene leads to a disastrous decrease in fitness. And that presumably makes it kind of hard to mathematically analyze, because we're used to doing, you know, gradient descent. We're used to like, you know, walking in the direction where things are increasing, but if things are wildly fluctuating, that's hard to do.

• 22:02Speaker 04

  No, that's exactly right. And I think this is one of the arenas. There are features of the way fitness, that topography up and down, that are deeply kind of counterintuitive. But I think one of those forces I study quite a bit in my lab, and that's this idea called epistasis. And epistasis basically refers to, it's a kind of an old concept with a really interesting history, but it basically refers to, you have one mutation in a gene that you, and you know what its effects are. You have another mutation, and you know what its individual effects are, but when you put them together, you got something you could not have predicted from them individually.

• 22:38Sean Carroll?

  Yeah.

• 22:38Speaker 04

  So what's the problem here? And the problem here is, this can create that ruggedness. This creates that confusion, because if you mutation, and you had it in the background of another, and boom, you could take you downwards, for example. You could be climbing uphill, perfectly fine. You're accumulating mutations, you're evolving resistance to a drug, or something like that. And then you get this mutation, the mutation interacts epistatically, we would say, with the other mutations, and boom. So I think, yeah, I think this throws a wrench in a lot of our simplistic models for how to understand, study, predict how evolution's working. And this remains a cutting-edge problem in evolutionary genetics.

• 23:14Sean Carroll?

  Yeah, this is why we got to pay you the big bucks. You got to solve these hard problems.

• 23:17Speaker 04

  Well, you know, I mean, you know, we're getting there. AI, AI will help.

• 23:22Sean Carroll?

  AI will help us all. Or at least it will confidently state it has solved the problem. I guess that's something.

• 23:29Speaker 04

  That's unquestionable.

• 23:31Sean Carroll?

  Is it possible, one complication obviously is that You've already alluded to the fact that the fitness landscape is not static, right? It changes over time.

• 23:40Speaker 04

  Yes.

• 23:40Sean Carroll?

  Is there any usefulness if we have different species coexisting and maybe one's a predator, one's a prey, or maybe they're just competing with each other? Do they both have fitness landscapes and they're sort of both changing in response to what the others are doing?

• 23:58Speaker 04

  Oh my, yeah, yeah, no, this is, this is a very, very compelling question, right? About, so number one, let's go back to what the, you know, the fitness landscape metaphor is saying, it's, it's thinking about a physical surface. Some really smart people have started to invoke the notion of a fitness seascape.

• 24:16Sean Carroll?

  Hmm.

• 24:17Speaker 04

  Right? And the idea there is that if you actually think about how evolution, so again, people keep adding kind of features to this analogy to try to match the ways, you know, the world works. Environments are fluctuating.

• 24:31Sean Carroll?

  Yeah.

• 24:32Speaker 04

  Right? I just, you know, I'm like, I'm wearing this sweater today because it is much colder today than it was yesterday and I have no idea what the weather's gonna be tomorrow. Simply that fluctuation in temperature changes some organism, probably a microbes, actual fitness landscape, maybe on our microbiota, meaning something that was uphill yesterday may be downhill today. Right? So the idea is the actual problem of evolution is deeply complicated because the topography of that landscape is changing, in which case the seascape, where things are always changing, is an extension of that now. And I think that hasn't caught on so, so broadly, but there are people that are using that analogy a bit more. Now, to the predator-prey thing, so that relates to the skiscape because they're influencing

• 25:17Speaker 04

  each other, an evolutionary arms race, their adaptations are gonna change as they're kind of evolving or interacting through time, through evolutionary time. Yeah, that's gonna change the fitness landscape. I have a close colleague, Martha Munoz, an evolutionary biologist who has this phrase, you know, that behavior is a brake and a motor on evolution. Right? So it's like, so if you're, right, so the idea is, and basically she's talking about how behavior itself becomes something that changes. Changes the fitness landscape for certain sorts of traits. Right? I just think that's a really, really interesting insight. So yeah, no, I think this is one. I think that probably outlined is one of the reasons why we need to think more carefully about adding bells and whistles and knobs to the original fitness landscape metaphor.

• 26:02Speaker 04

  Because the problems in the real world are just so much more complicated than can be captured in a simple, you know, three dimensional image.

• 26:09Sean Carroll?

  Well, you use the word complicated. Of course, that's right next to the word complexity and we're both. We both hang out at the Santa Fe Institute and talk about these things. I want to ask you the general question of how complexity science plays into this, but first, what you just said reminded me of a conversation I had on the podcast recently with Don Farmer, the economist, another SFI guy, who points to the focus on equilibrium in economics as sort of a limit on what they could do because the real world economies are never in equilibrium. This is not a good approximation. I'm wondering if evolutionary biologists have the same issue to confront.

• 26:48Speaker 04

  Yeah, I mean, yeah, maybe not quite. I mean, yeah, non equilibrium systems is a is a buzzword in a lot of different fields. In part because, as you say, the analytical tools, the original tools, you know, kind of are built around equilibrium and fixation, and yet the world is kind of in flux as we described. So just bridging into the last conversation, the notion that everything is in flux complicates the idea that there's a neat and clean adaptive peak. So let's go back to that fitness landscape, right? This idea that we've just optimized. What's interesting, you talk about how creation is utilized and kind of weaponized even the fitness landscape. That I can also see how that can work because the notion that there's a neat peak

• 27:31Speaker 04

  for any given problem and we're all just trying to get there, right? That is one that is kind of friendly to both creationist ideas of that there's an ideal and we were made perfect, right? And then it's a kind of, you know, change gears a little bit to eugenic ideas. That what a perfect human is and there's an optimal this and an optimal that, right? So I think even the quote unquote scientific side of this problem is, you know,

• 28:00Speaker 04

  you know, has issues when they push for equilibrium and stability and optimal. And that's just not how the biological world is constructed for myriad reasons. So I think that's, that's what I would say the connection is between the equilibrium conversations in econ.

• 28:15Sean Carroll?

  Yeah, okay. I mean, do evolutionary biologists explicitly use the language of equilibrium? Like, they try to find some happy place? Because I know economists do, and that was the...

• 28:26Speaker 04

  Yeah, yeah, yeah, yeah. I mean, I think in different step for some simple questions. So, for example, evolutionary game theorists, for example, absolutely. They absolutely formally use the mathematical understanding of equilibrium, right? To think about how a, you know, in a situation where you have a bunch of evolutionary inches, what's the evolutionary stable strategy, for example, as John Maynard Smith and John Nash and others would have articulated. So, yeah, so equilibrium is certainly used there. Equilibrium, I think, like, in population genetics, we would say fixation, right? That's when it goes to fixation, is fixed in a population. So that's an iteration of equilibrium. It's like, you know, you have an antibiotic resistance

• 29:11Speaker 04

  gene that is better than everybody else at some point through time, and we have elegant math to predict when this is going to happen. It will, like, rise to fixation, meaning virtually all the, virtually all, everyone in the population has that allele fixed. So that's kind of where equilibrium is also invoked in evolutionary biology. Oh.

• 29:32Sean Carroll?

  So these are all very general concepts that apply to all the different levels that we were talking about at the very beginning. But my impression is that in your lab, you're looking a lot at diseases and how they resist drug treatments and things like that. I mean, it kind of sounds like we can just take everything we've been talking about and pour it over. Yeah, there's some pathogens, and we're fighting against them, and it's all dynamic, etc. But what are the specific questions that get raised here?

• 29:58Speaker 04

  Yeah, yeah, great, great, great. Thank you. So I think microbes and disease have all of these features that make them really, really great, have always been some of the best systems for asking the fundamental questions in evolution for various reasons. Number one, they're microbes and they're diseases, and people care about them, right? I think, which is very, very important. But more than that, part of the reason why we care about them is because they are just kind of wired with all of this biology. That makes them kind of fat relative fast evolvers. We can see them evolving fast. They can adapt quickly. The generation time is short. And now I kind of in the last half spent more than half century now, I mean, really much of the century. Um, we can grow these things in the lab in really, really elegant ways.

• 30:41Speaker 04

  We now have their genome sequences fully understood. They're very, very tractable system. So we, we use, we're using kind of microbes to ask fundamental questions about the way that molecular evolution is happening largely at the protein scale. So for example, this question about the fitness seascape, right? The fact that it changes. What if I have two closely related drugs that we nonetheless use interchangeably at the bedside? Right? Like, so you're, meaning we think they're very, very similar. I had a background where I did some clinical micro. So we think they're similar. We're looking at things like, all right, actually even subtle differences in the chemistry of a drug, very subtle, changes that fitness landscape.

• 31:25Speaker 04

  And so resistance to do these two drugs actually might move differently and that at a different pace. Right? So that's kind of highlighting the, you know, the relevance of this fitness seascape denim to the fitness landscape metaphor. Because it's basically saying, wow, even slight changes in an environment can profoundly influence the, uh, the pace and direction of evolution. So we're studying questions of that sort.

• 31:51Sean Carroll?

  Yeah, I mean, we all know, I guess, that, uh, we fight against the pathogens and the pathogens, they don't fight back, but they adapt back. They try to slip through our fingers, right? It, how, how much is the study of this sort of purely empirical, we try this and see what happens versus how useful is the theorizing here? Is the theorizing caught up to the messy empirical reality?

• 32:16Speaker 04

  Oh, oh, what a good, good question. I think I have a lot of thoughts about that. So, you know, number one, our, you know, Rich, Richard Linsky's long-term, uh, evolution experiment remains, I think, the greatest, like, one of the greatest demonstrations of how evolution by natural selection works. And it continues, it's now, I believe with Jeffrey Barrick now at the University of Texas. It's one of the great examples of the demonstration of evolution in action and all of its elegance and beauty. There's both patterns and it's completely unpredictable and it does all these amazing things.

• 32:50Sean Carroll?

  Maybe, sorry, maybe for the audience, explain the long-term evolution experiment.

• 32:54Speaker 04

  Sure, sure, sure. Thank you. Yeah, thank you. So basically, Richard Linsky and colleagues at Michigan State, gosh, now many, many decades ago, evolved strains of E. coli, this kind of, it's a gut bacteria, soil gut bacteria, that we have kind of domesticated to be able to grow in the laboratory very, very well. So it's one of the most studied organisms on earth. We know everything about it and we have a strain that grows very, very well. We have strains that grow very, very well in the laboratory. Well, Linsky and colleagues took this and basically generated multiple populations of them, right? So we had multiple kind of versions of the world playing out in parallel and evolved them, right, to various

• 33:37Speaker 04

  stressors, sugar, different sorts of sugars, metabolized different sorts of sugars, and basically just demonstrated over the course, right now, 100,000 generations, what happens when evolution plays out, right, and responding to the exact same sorts of stressors across multiple populations. And it's basically given us this amazing picture into how evolution happens, both at the genotypic, meaning we know how the genome is changing, and at the phenotypic, we see the acquisition of kind of new abilities to metabolize sugars. We see the gain and loss of genes. We see all of these processes happening right under our eyes. And it's given us the one of the most detailed pictures for how evolution

• 34:21Speaker 04

  happens. This experiment has been mined by theorists and it has generated new theories. So to the second part of your question about, you know, this theory. So I think those things have been in conversation experimentally in a really, really good way. Now, this last point you had, that question, which was more provocative, is has theory caught up?

• 34:43Speaker 04

  Oh my, I mean, you know, there's a big joke and debate within population genetics, which is, and you know, which is where a lot of the theorists live. They call it beanbag genetics, they would kind of pejoratively call. Because it just seems too optimal. It seems based on a world, you might have heard of Harvey Weinberg equilibrium, which is this, this kind of equation that describes, it's a null model for how evolution's operating under optimal conditions. When you have populations that are breeding at a certain kind of way, and it basically reflects no reality ever. You know, the way biology works. So I'm saying this to say, a lot of theory still suffers from a lot of that. It does, that problem, if it does not, it just does not apply to the biological world as it actually exists.

• 35:29Speaker 04

  I think the good news there, and I don't look at that as a bad thing. I think now in that what I call the post genomic era, as I call it, where genomics now just happens at a cheap pace. It's no longer the, it's not the rate limiting step in any scientific in Denver. I mean, rarely, there's some organisms that are hard to sequence, but generally speaking, everyone has access to it. I think we're going to enter a golden age of theory, personally. Because we're going to have more data than we know to do with. It's going to reveal new kind of things and patterns that we could not have considered. And I think now we have data, metadata about how the world actually works. So we can evolve, no pun intended, from beanbag genetics and actually begin to develop a theory that matches this dynamic world that we're in.

• 36:10Speaker 00

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• 36:41Sean Carroll?

  You know, I have a historical example I always pull up when people talk about this because there is this opinion out there in some circles that when we have huge amounts of data, we'll find patterns in them and that will be it. We won't have any deep understanding, we'll just know the pattern. And maybe that's true, but I hearken back to Tico Brahe, the astronomer who just, you know, had the best telescopes and actually it wasn't even telescopes, right? I mean, it was just, he was just plotting positions of stars, planets on the sky, et cetera. And on the basis of that, Kepler was able to say, you know what, it's not epicycles, it's ellipses. And on the basis of that, which was just pattern fitting, but on the basis of that, Isaac Newton is able to invent

• 37:25Sean Carroll?

  the law of gravity, which is actually understanding. So I'm optimistic that biology, neuroscience, all of these things will eventually get there. We're just at the first stage, so it's not surprising, we're not there yet.

• 37:38Speaker 04

  I couldn't agree more. And I think, and I think you point out something very important, and that is with new data, you know, are gonna come associations between things. That's what these computational tools do, you know. But that is not a mechanistic theory of understanding for how a system works. But I'm just, I'm just, but I think computation can help us with theory as well. I don't think, you know, and I think it can help us. So that's what I'm excited about. It's like, okay, well, the theory, you know, going back to the fitness landscape, the theory from the modern synthesis and the statistics from the modern synthesis, you know, Fisher, of course, is one of the architects there. You know, everybody who does empirical work of any kind is still using Fisherian statistics.

• 38:20Speaker 04

  And so I say this to say, we've done a lot of good, I mean, like, in terms of technical good, not moral good and not social good. But statistical good with theory, that's now a century old, right? We've done, we've done a lot of good with that. I really, really want to see what the next generation of theory looks like because it's good, could and should be different. And I think we can use computational tools to help us get there.

• 38:48Sean Carroll?

  Well, okay, back to the real world a little bit. Do you have examples of your work in diseases and their evolutionary adaptation where thinking about them in the right way has literally helped fight a disease in some way? Or are we still at the sort of trying to grope towards some understanding level?

• 39:06Speaker 04

  Yeah, no, no, it's a good question. So, so I think it has. I think the, and you know, I'm not like, you know, I'm not personally taking credit for this. I just mean like, like Hollywood. The field, the field itself that has studied problems like the evolution of drug resistance at a molecular level has unquestionably, at the very least, most conservatively, it has changed the medical conversation about how and when and why to use antibiotics at the bedside. And I think resistance profiling is now a central part of how we think about therapies for infectious diseases and cancers. And I think that absolutely is a testament to the fact that evolutionary understanding became part of medical understanding for that problem.

• 39:50Speaker 04

  So that's the most conservative view, that there's no question that we think about it. And evolution now is part of drug design, right? So think about resistance profiling upfront, what would resistance mechanism look like? This is now a conversation that's happening. So there's that, which I think is a notable and significant application of thinking about how molecular evolution can apply. Now, more than that, I think this understanding, if we understand what a fitness landscape looks like in these environments, the question is, can we predict how drug resistance is going to evolve? That's kind of one of the, right, the gold stairs. And these are the sorts of things that we ask in our lab and colleagues of mine ask. And I think the goal here is a paradigm where we can cut infectious diseases off, or drug resistance off.

• 40:33Speaker 04

  We know where it's going to evolve, and we have a suite of drugs that can actually get, like, you can have it in place before it can get, you can actually prevent resistance from happening.

• 40:41Sean Carroll?

  Wow.

• 40:42Speaker 04

  You have a, yeah, yeah, so this is, so you're predicting evolution, not like, unlike you're predicting the weather. I mean, the mathematics and stuff is different. It's more of a conceptual analogy than a computational one. But maybe, maybe it is actually computationally, maybe the tools will look like that. I'm saying this to say, the idea is- The weather is, you know, there's some analogies, right? There's some predictability that because of the accumulate, you know, how you configure these various forces that dictate how the weather works. And I think in evolution, there's various forces that are influencing how, right? A a microbe is evolving in the presence of some drug. Well, if we understand what those forces are, we can come up with probabilities that this outcome will happen versus probably that this going to happen, we can have drugs ready for that. Right?

• 41:27Speaker 04

  And we can actually help people, um, you know, who are infected with various pathogens that way. So that's a contemporary one where evolutionary theory is really, and we do have drugs that are on the bedside that utilize these sorts of that are being kind of tested, but utilize these sorts. And I think you have drug companies now utilizing the fitness landscape to try to identify drug targets all across the fitness landscape. So has this happened yet? Is this active now? Can you the sort of technology? No, but this is, I think they are, this is very much at the point now where these sorts of sophisticated evolutionary understandings are now being invoked in serious conversations about how to generate new therapies.

• 42:07Sean Carroll?

  The idea of predicting the course of evolution is a fascinating one. I mean, my rough impression is that back in the day, you would have said, you can't predict anything. It's all randomness and we're going to find different things. And these days we seem to be moving more toward a view where, where, well, some things might predictable because that's where the peaks of the fitness landscapes are. And the randomness comes in in how we find them, not where we end up.

• 42:35Speaker 04

  That's right. That's, that's exactly right. And I think that's what makes this current, uh, you know, that's, that's what makes this era so fun. We now have the information again, now we have the data on what this fitness landscape looks like. We know where the peaks live. We know where the valleys live. We know where we are on that landscape. I think what we're like, the things that we continue to try to understand is, is mutation truly random, right? You know, as in, is it really, really, really? If there's three different things, if there's three, four different mutations, is there really a 25? 5% chance that you're gonna get each and any one of them. Well, the answer is no, right, that there are some kind of like leanings. And a lot of this relies leans in at the biochemistry and biophysical level.

• 43:18Speaker 04

  Like there's just certain sorts of chemical modifications that are more likely to happen. So therefore, mutation isn't like random in the sense that all things are equally likely. It's random in the sense that there's no particular force that is picking a given site to mutate. You know, or and even that, and I think in some cases is not true. So I think the evolution mutation rate is a hugely complicated field for this. It's basically saying, yeah, it's random by some definitions. And that's a debate I do not want to have with philosophers like you.

• 43:50Speaker 04

  But yeah, it's random in the sense that nobody, no force is putting it there actively. But it's not ran, it's, it's, it's not random in the sense that there are potential leanings in forces that may take evolution towards certain directions. So anyway, just, I'm just saying, yeah, there are, the fitness landscape would now have better data on it. We can understand it better, but there remain a lot of things we have to understand before we can truly get to the point where I'm really, really confident that this evolutionary outcome will happen.

• 44:17Sean Carroll?

  Yeah, I guess it's, the mutations are not teleological. They're not oriented toward trying to make the organism more fit. In fact, most evolutions, most mutations will make it less fit, right? And then we sort of weed them out. But, but yes, I have learned over the course of talking to biologists on the podcast that, you know, functional parts of the genome don't mutate nearly as much as the, as the junk parts. You know, because they're kind of protected, they're important. And, and so it's absolutely not all chances are equal.

• 44:48Speaker 04

  No, no, no, that's exactly right. I mean, there are absolutely parts of genomes that evolve much faster and much slower than others. For, for some of the reasons that you outlined. And I think this relates to a concept that I'm really interested in called evolvability. Right, you know? And it it kind of sounds like an interesting word because it's kind of like right evolvability. It's like the capacity of an organism or a replicator to evolve. Right. And right, and I think what makes it very, very interesting and provocative is that you think about the ability to evolve as a trait that could be under selection itself. So think about this for a second.

• 45:32Speaker 04

  Can evolution make one organism more likely to evolve? Oh, I like that. Can evolution actually make evolution, a gene in a genome, more likely to evolve? Or or less likely to evolve? And what does this mean? Okay. And so this is a a a concept, actually, Masibo Piliuchi and actually several other kind of, you know, philosophers have commented on. And it's a really, really provocative concept because it it really ask kind of how how at what levels is evolution happening? Does evolution actually care about that? Can it actually, can evolution actually control that? If it does, if you do see that organism A is more evolvable, evolve faster than organism B, is that just a byproduct of something else?

• 46:17Speaker 04

  Or did evolution actually engineered that way? This is a deeply complicated idea with a lot of strong opinions. I think one of the things that has thrown off this discussion is just defining what it means. How do you actually test? I mean, me and Sean, me and you, or who's more evolvable, right? Through evolutionary history. You mean at individual level? Do you mean at the to the to your original question? Do you mean at the population level? But nonetheless, it's cool to think about that sort of question within genes. Right? The rate of evolution in one is faster than the others. Why is that? And how do we? What what dictates it? Is it the topography of the fitness landscape? Maybe if fitness landscape is really, really smooth, right? Maybe that's a feature that allows. So we've asked questions like that in our laboratory as well.

• 47:03Sean Carroll?

  You're younger than I am. You're much more evolvable, at least I think, you know.

• 47:07Speaker 04

  Oh, no. I mean, look at what you've done and what you continue to do. I think it's a...

• 47:12Sean Carroll?

  I used to be evolvable. Now I'm just an old buddy dudie. So, we mentioned complexity earlier, and obviously, complexity has been all over what we've been talking about. But I guess I have a question. This is always a question, and I have my own answer to it, but I'm always interested in what other people think. Is the idea of complexity intrinsically a useful one? Is thinking of some specific complex system as an example of a complex system, rather than the specific system that it is? Has that been useful to you in your work?

• 47:45Speaker 04

  What a great question. I mean, I think that I'm gonna hit this several ways. So, you know, I think the beauty of complexity science, you know, is that it is both a large cabinet of formal ideas and formalisms and theories. But it is really kind of, thinking about this evolvability concept, I think it's meta in the sense of complexity science is the only field that recognizes that it has to change. One of the only field that has that wired in. The whole point is that the world is complex.

• 48:18Sean Carroll?

  Non-equilibrium.

• 48:20Speaker 04

  It's a non-equilibrium thing, and we're gonna fold new things in formalisms. Like, you know, math, formal mathematics does that, and formal evolutionary biology does that. And we incorporate new things, but that's not part of the identity of the field. Like, we just kind of happen to incorporate new things as they come up. Whereas complexity has a self-awareness about it. And so, for that reason, if that reason alone, even if I didn't love the ideas within that cabinet, I love the fact that it reinvents itself, like few other fields do. And for that reason, I think it is incredibly useful. Now, now think about the formalisms inside the cabinet. Are they useful for, right, in terms of thinking about them across paradigms?

• 49:01Speaker 04

  And I think so. I think, so number one, let's just kind of start more vague in general. Like, the idea that systems, the formal idea that systems are constructed and the rules that construct them, right, rely on, rely on understanding kind of the interactions and the nonlinear interactions between them, and the emergent properties that come between them. Yeah, that one is kind of milquetoast now. Like, I think it's like, you know, and I think everybody else has kind of incorporated it. But I think that's a triumph of complexity science, not a reason why it's not important. Right? I think complexity science was the one that popularized that notion, and then everyone else just took it and were like, "Oh, yeah, that's obvious."

• 49:41Speaker 04

  Well, it wasn't obvious. The field championed it, and that it's correct, and so it became obvious. So, yes, I don't call, right, I talked about the nonlinear interaction between mutations, or they're not, right, uh, yeah, I don't call that, that's not formally, it's epistasis, that's firmly in genetics. Yeah, I don't call that a complexity science discovery. I call that a genetics discovery. But nonetheless, the, the, the, the, the language for it, the vocabulary for it, and the word for it, it's so much easier a concept to sell in various settings because I think complexity science equipped us all with this vocabulary.

• 50:19Speaker 04

  Now, that, now, let's, let's go to the other part of the question is like, the tools itself. Yeah, when I studied the complexity, people who think about econ, do I walk that directly over into thinking about fitness landscapes and go, "Oh, I have a tool from..." No, I, I, not, not so much. I think, um, and, you know, do I, do I, but conceptually I do. Right? Like I said, I mean, I think now, I think the, the field that I think where this work is best arguably, is how networks re, has re-completely changed how we think about epidemics. Right? Like, for example, like, that one, that one, I mean, that was a, that, like I said, it's origins, it has relatively recent origins, network theory, of course, related to graph theory.

• 51:01Speaker 04

  But it has walked into a field of mathematical epi that had a long history, and really smart people already doing great work. Has completely been like introduce new questions and come up with new strategies that have been practically helpful. So the second part of your question, you know, do I think that complexity cabinet of tools and ideas and mathematical tools? And taking them and walking them across to different problems has helped? I think absolutely, uh, which is why I continue to mine this cabinet. You know, directly or indirectly for new ways to study the natural world.

• 51:35Speaker 00

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• 52:35Sean Carroll?

  I really love this idea of complexity science as more self-aware of its being a complex adaptive system itself than other fields. I think that over and over again, in many fields, maybe economics, maybe evolution.

• 52:52Sean Carroll?

  People look for a stable foundational point, certainly in philosophy, right? Going back to Descartes, who this like, I'm a little scared by change. It could be good, it could be bad, so let me just find a rock to stand on, right? But over and over again, the universe is telling us, actually, no, that's not the way it is. You are a surfer, you are not standing on a peak, you are moving on the seascape. And the more self-aware we are about that, uh, the better off we'll be. If you'll forgive me, uh, rhapsodizing a little bit.

• 53:24Speaker 04

  No, this is, this is totally right. And I think, you know, there's a great new volume from Santa Fe Institute on foundational papers, um, you know, in, in complexity science, which I think is a useful venture for a bunch of reasons, but I think it's a good answer to this question of, is this a field? Right? Yeah, it has, it has, it has canon, right? There, there is real canon to it, not, you know, it's not, it doesn't have a principia or a, or a, you know, uh, or a origins of species. It doesn't have those, not yet. Um, but, right, not yet, right? But it does have a canon. Um, and I think that's what really makes it different. For me, laughably, and, and, you know, I, I mean, trying not,

• 54:08Speaker 04

  I don't want to discredit everything I've ever said by telling you that my introduction to, you know, to complexity science was Dr. Ian Malcolm from Jurassic Park.

• 54:15Sean Carroll?

  Of course, you're not the only one.

• 54:17Speaker 04

  I, I mean, and I was a young person then, and I think everything about who he was as being iconoclastic and was like, wow, I want to be like that. I want to be like that dude. Um, but I think, but I think there's that. And then for me, there's the more is different paper from 1972 by Anderson, um, about kind of, uh, you know, broken symmetries. And that's, that's a foundation on the, more is different. It's qualitatively different. It's not, you can't just take your deterministic tools and grow them. You have to actually change the rules as you add more components. So those were a couple of influences for me.

• 54:49Sean Carroll?

  The idea of the canon is great because I'm actually teaching with Jan Ismael a course, uh, in the philosophy department this year on complexity, where we're reading some of the classic papers. And it's absolutely clear that there are ideas appearing over and over again, right? That we build on what has gone before. It's exactly what you'd expect from a real field with a canon expanding its purview over time. Yeah.

• 55:12Speaker 04

  Absolutely. I think other fields should kind of, and they kind of have, I was taking, I mean, yes, I'm drinking the Kool-Aid on complexity science. I really think, you know, uh, just because I'm someone who struggled my whole life with having to explain to everyone why I'm reading multiple literatures. And I know you, I know you can appreciate this, right? More than anyone. Um, but I think when I found complexity science, it was just like a crowd, right? Didn't have to do that at all. Like nobody was, nobody was gonna, nobody, you know, when I was studying medicine when I was young and I'd be reading an econ book and back then it was like Jeffrey Sachs and conversations. People would be like, why are you reading econ? Cause it's kinda interesting, important to understand.

• 55:52Sean Carroll?

  Yeah.

• 55:53Speaker 04

  I don't have to do that in, in the, you know, in the complexity community.

• 55:57Sean Carroll?

  People, people don't get it when I say that the single best thing about Santa Fe is that at lunch, you don't have to justify yourself. They've only brought in other people who already get it.

• 56:07Speaker 04

  Well, at all. And, you know, just, and, you know, people aren't, you know, I mean, we're fancy, grumpy professors, but we, things are not credentialist there in a way. I don't think anyone cares about what, you know, where you come from, not really. I'm sure that's, it's elitist, the way all these spaces are elitist, but, but there's a different set of hierarchies also structured there differently. I feel like just intellectually, and I think that allows for the cross pollination. I mean, you gotta have to have a flat intellectual surface for complexity to work because things have to be able to meet each other and, you know, they work together. The reason why I took physics and biology so long, it's kind of like a fitness landscape problem, right? Like they're, they're here.

• 56:47Sean Carroll?

  They were fine.

• 56:48Speaker 04

  And it weren't the people. People serve as the bridge, which is why I showed interest, what is life is such a foundational idea, a book, book for this, for thinking about how, how physics walked into other fields.

• 57:00Sean Carroll?

  Okay, good. Back to the biology here, cause we, we've left some things hanging here. We, we talked about the possibility of predictive evolutionary biology, which is fascinating to me, but then we can just imagine being the intelligent designers. What about the prospect of we human beings going in there and mucking with the genome and designing the organisms we want?

• 57:23Speaker 04

  Yeah, yeah, yeah, yeah. I have this, you know, mental equation that I use. It's not a firm one, but it's a control equals prediction plus engineering, right? Is how I think about these sorts of things. Okay. So the idea here is you want to let me think about any system that we can control. It's because we know what they're going to do. We have a good idea of what the system is going to do with certain inputs. And we have a means of inputting those things. That's what the engineering part is, right? So be it cooking, right? Like the reason why what a good chef can do is a chef knows what chef knows what's gonna happen if you add oregano. And the chef can't add oregano.

• 58:04Sean Carroll?

  Yeah.

• 58:04Speaker 04

  Right? And add oregano like as a means to which you can do it. And if you can do that, you can control. Now, I'll start with the bad news. The bad news is some of the kind of most unscientific and worst uses of science have come from a desire to control how evolution works based on an erroneous and broken understanding of prediction in biology. So as soon as we understood that evolution was a thing and that genetics was a thing, people were thinking about steering it. This is of course what eugenics was about. It was the notion that you can optimize populations. And we don't have, I mean, there's a lot of people that have told that history. So we don't have to spend extended time there. But that's the moral part, of course, is the worst part. But like right next to that adjacent is the fact that it was technically completely wrong, right?

• 58:49Speaker 04

  Like it does it had a very poor understanding of the way human traits were constructed. So some points, if you get prediction really, really wrong, you've built it on bad tools. Now, let's flash forward today to the good news. The good news is for certain molecular systems, we actually, we have a picture of what the fitness seascape looks like. We know what mutations do. We know the distribution of fitness effects. How kind of with the ratio of mutations that a gene is going to get are going to be beneficial versus deleterious. We now, because of people like Joe Thornton and colleagues, like at the biophysical scale, what mutations will do. As in you take a protein and you add two mutations, how it'll change its folding, how it'll change its catalysis.

• 59:34Speaker 04

  And we have this down to the, you know, to the physical scale now. So we know so much. So prediction's getting stronger.

• 59:42Speaker 04

  We absolutely better than ever because of Jennifer Doudna and colleagues can engineer genomes.

• 59:48Sean Carroll?

  Sure.

• 59:48Speaker 04

  At an increasingly better resolution. So we are getting the tools now we can actually, right? We can actually steer, as some of my colleagues call it, steer evolution towards certain things. Now, of course, Francis Arnold and colleagues awarded the Nobel Prize in 2018 for directed evolution. As in, I want a protein that's more stable or that can metabolize sugar faster. And that's a sort of control because, you know, in the sense of they know what they want and they're just marching evolution towards that path. But that's not like full control because you're still kind of like, you're kind of just hoping that you get the certain evolution moving up. You're not actually controlling all the steps.

• 1:00:32Speaker 04

  Now we have the technology to actually steer and engineer a better hemoglobin, a better drugs, better proteins that do certain things. We're actually getting the information now where that control equals prediction plus engineering. The engineering is getting better and as, and this is where theory and experiment come in. That helps with the prediction part. And so this paradigm is like here already and will continue to be a thing.

• 1:00:57Sean Carroll?

  So yeah, Francis Arnold, my former Caltech colleague and friend. If I understand correctly, her work, it was almost like breeding, right? I mean, she was really just changing external parameters and like we do with dog species or dog breeds or something like that. And like you say, now we can go in and monkey with the actual DNA. So have there been, I'm not up on it, have there been big breakthroughs in doing that? So not just monkeying with the DNA, but for a particular purpose, designing an organism?

• 1:01:29Speaker 04

  Well, yeah, I mean, I think, yeah, I think, I mean, the work that, that is closest to mine is colleague Jacob Scott and colleagues. Emily Dozen and colleagues have, I've done stuff like actually steered evolution down certain, and it's like, and drug resistance. So similar problems like however, how a microbe or a cancer cell is evolving resistance to a drug. Yeah, people have tweaked parameters. There's a bunch of labs, Michael Baim and colleagues have also thought about these sorts of problems. We've actually steered evolution towards certain evolutionary outcomes when it's come to antibiotics. Now, I think what you're asking is something much more grander than that, as in, do we have like a

• 1:02:09Speaker 04

  a grand molecule that we've engineered from these sorts of things? And I think some of this just has to have happened in an industry for certain sorts of So, yeah, we are using evolution to build molecules in a generic sense. But have we yet? I think, what would be the goal there? What would be the thing that would actually announce this as, whoa, right? As another kind of improvement? I think what that would be, would be, we get a type of molecule or protein that we thought was like impossible that has profoundly important implications for life on Earth. And let me give you an example. There's a protein called RABSCO, right? Okay.

• 1:02:53Speaker 04

  RABSCO is one of the most abundant proteins on Earth. It lives, it's basically a critical component of kind of how plants, basically, you know, in certain critical reactions in terms of how plants are able to metabolize things. It's a critical, critical thing. And one of the things about it is, according to some biophysicists, it's kind of a lousy protein. It's actually not very good, right? But something about the fitness landscape of this protein has made it very difficult to evolve. It's actually not that easy to engineer. So, there's a lot of people in the world, my colleague Matthew Shelders at MIT, for example, good friend, are trying to come up with ways to engineer this thing. If someone can invoke a way to steer the evolution of RABSCO

• 1:03:38Speaker 04

  towards a much better, kind of more efficient version, you feed a lot of people. You feed a lot of the world, you save a lot of plants. So, this is an example of where this sort of kind of control, and there's this kind of features to the biophysics of it that make it tricky to work with. I think that's the problem right now, but there's a lot of interest and investment in a lot of companies. Because this is going to be a major, I mean, whoever does this is rich. So, that's an example of where this sort of approach, we want to go next. And I think labs are working on these sorts of things now.

• 1:04:13Sean Carroll?

  But you and I are rich in intellectual reward. Doesn't that count for something? If not actually rich in money?

• 1:04:20Speaker 04

  Of course. No, no, no. I love being able to do this. I think there's a lot of stress in doing that.

• 1:04:26Sean Carroll?

  Yeah, fair enough. So I guess there's a basic thing that for a long time I've realized I don't understand, but you're putting a finger on it. Because you talk about evolving proteins. And in my brain, the genetic information is in DNA, and that gets transcribed in the RNA, and that prints out some protein. So the thing we should be engineering is the DNA. But what good is it? Or how do we think about engineering proteins directly?

• 1:04:52Speaker 04

  Yeah, no, I mean, we know what the individual mutations are at the DNA level. We know the amino acids they encode. So it's kind of like, when we're engineering at the protein level, or when things are evolving at the protein level, they're actually happening molecularly at the DNA level.

• 1:05:09Sean Carroll?

  Got it.

• 1:05:10Speaker 04

  Right? So it's happening there. So the actual engineering is going on there. And then you can make, you can make a lot of, you make proteins from that information.

• 1:05:18Sean Carroll?

  And this is another great example where everything matters. This is why biology is so much harder than physics, right? Because it's not just the DNA instructions. There are enzymes, and there are conditions, and there are constraints. And I presume that you care about engineering these in a very down-to-earth way.

• 1:05:35Speaker 04

  Yeah, I mean, you know, I think this is really, really important. I think this is where I like to position myself as a bit of a gadfly in that field. Because, you know, I'm all of these things at once. And by all of the things, I mean this. I believe in like, many of the deterministic laws that underline population genetic systems. I think those have been elegant. I think they've helped us understand how evolution works. I think they've added rhyme and reason to it. They've given us mathematical and statistical tools to be able to study evolution in populations. So I'm there. But I'm also on the other pole of, if we want to solve any real problem in contemporary biology, rabisco, drug resistance, cancer evolution, genes for depression, genes for

• 1:06:20Speaker 04

  high, type 2 diabetes. If you want to look at any problem in biology, we are not going to solve it with simple deterministic perspectives or tools. We must, must rigorously incorporate these other dimensions that are influencing the way that this landscape is structured. Right, that influenced the topography of it, that influenced the way variation is generated, right? And that is, right, you know, forces like epistasis, which we talked about, gene by environment interactions, the fact that the environment is sending this seascape to completely different topographies all the time. And, and, you know, now there's epigenetics, which I won't touch right now, but I think it's a, it's a really a kind of other thing that modifies

• 1:07:04Speaker 04

  how phenotype happens. Pleiotropy, like which we refer to either the idea that a mutation that does one thing in a cell is actually probably doing multiple things in a cell. And so that complicates the, right, the topography of this landscape. I think this is where we need to live theoretically is learning how these different concepts, which, you know, complicate evolution are actually a beautiful thing to study. And I think we're doing that. I just think we're headed to an era, we can integrate these things more formally into a grammar. And I think then we'll be able to control evolution in a much more meaningful way. We'll be able to predict evolution in a much meaningful way. But do I think that there's certain things within biological systems that are just gonna be not predictable

• 1:07:47Speaker 04

  in a useful way? Like I do. And I think this is where stuff like embryo selection is happening, right? People are trying to actually pick their children and certainly for certain traits, right? Hair color and eye color, we have a very good picture of how to predict. But I think, you know, you want, you want a Sean Carroll in physics. I mean, good luck with that. I mean, how, how, how possibly could you separate what you were born with from the vagaries and the influences and the dimensions? And that's not a hippie concept. That's a technical concept of invoking the notion that this is dictating who you are

• 1:08:24Speaker 04

  in, in a very meaningful way.

• 1:08:25Sean Carroll?

  Well, look, it's late in the podcast. This is exactly where we're allowed to broaden our scope a little bit and think about the big picture. I mean, obviously there are ethical, social, political considerations to come in when we start talking about designing organisms. Have, you must have thoughts about this. Do we have any guidelines about how to do this responsibly? Is it, I worry that

• 1:08:52Sean Carroll?

  Philosophy is supposed to be a source of wisdom about ethics and morals and things, but I kind of think it's a trailing indicator. The philosophers wait for things to happen and then go, "Oh, that was bad." And we're kind of in a position where we need some leading ways of thinking about things that haven't happened yet because the things that could happen are maybe a little scary.

• 1:09:17Speaker 04

  What a good way of putting it, that you're a trailing indicator. And you're right, I mean, we think, you know, I can't think through the problems with anywhere near the clarity that a philosopher can, but what I can do is do them right away, to your point, and not be a trailing indicator. That's actually a good way to think about what my job is. And I think thankfully, there's a generation of geneticists who are of this like mind and are reading the philosophy literature and ethical literature. Now, I think here's what's critical, is that there are ethical issues involved, right, about, you know, engineering, you know, embryos with certain traits and what that means and how you're influencing future generations.

• 1:09:58Speaker 04

  And I think those are linked to the technical problems with that sort of, in many ways, they're sometimes linked to the technical problems. And that is, right now, we operate as if we know how the biology works and the debates are only technical. That's not true. If you were talking about the genetics that underlie type 2 diabetes, for example, I mean, we have a lot of beautiful science on that, but the predictive genetics are a disaster.

• 1:10:26Speaker 04

  A disaster. And it's not like the work itself is bad, but that the predictive, the predictions that you're going to make are going to be disastrously wrong. Because that's just not how the biology works. We want this neat and clean picture. In fact, the term, as you know, for this is physics envy.

• 1:10:44Sean Carroll?

  I do, yeah.

• 1:10:45Speaker 04

  Right, which goes back, I learned it from Gould, but I think many people have used it in various ways. We, you know, I think there's three kind of main problems that play contemporary biology when it comes to prediction. One is physics envy. Two is an idea that we're incorrigible categorizers and we're not critical about the types of categories that we use, right? And three is this elegant concept developed by my dear colleague, Doc Edge, a population geneticist, who calls it giddiness. And giddiness is this notion that... We're going to just solve all the problems if we just keep sequencing, or just keep collecting data, collecting data, and keep collecting data. So when it comes to like the problems of predictive genomics or engineering,

• 1:11:28Speaker 04

  you know, we just have this overconfidence that we even know what we're doing. These traits are deeply complicated. The environments change them profoundly. Uh, and, and the notion that we understand and predict at this moment is just wrong. It's just technically wrong. And so that's kind of where I step in and try to be a voice and say, hey, you know, uh, this isn't doing what you think it's doing. And because of that, maybe we shouldn't do it right now.

• 1:11:53Sean Carroll?

  Well, let me, let me be a little disruptive here and mention that because we've been, we've been so good over the whole course of the hour. But as we're having this conversation, we learned over the past couple of days that the nominee for the head of the Department of Health and Human Services is Robert F. Kennedy Jr., who doesn't believe that vaccines work at all, doesn't believe that we should fluoridize our water, uh, et cetera. There's a, this is obviously sort of a commonplace, uh, observation, but you can't separate the science from the culture, right? There, there are reasons why we've been brought to this place. Is there, is there something scientists could do?

• 1:12:38Sean Carroll?

  I take it for granted, Brandon, that you are on my side in thinking that vaccines do work?

• 1:12:44Speaker 04

  I do. I happen to share that view.

• 1:12:46Sean Carroll?

  All right. Uh, how should we? I, I always struggled this myself as someone who is a scientist with a sort of medium-sized public profile. What are the actions to take to make people get it better?

• 1:13:02Speaker 04

  Oh my, so first thing, and I, I'm not just saying this for no reason. I mean, I think it's what you've decided to do with this, as this, and an example of it was what you decided to do with this podcast. You know, I, I think about, I was reading the other night that Michael Faraday, one of my scientific heroes, um, you know, amongst many things, you know, started the Christmas Day Lectures series. For any started in 1827, I believe, which were specifically for the public. They were for the public and I don't know, I haven't read, I actually haven't read a good Faraday biography, which is actually on my list of things to do. But I don't know what the motivation came from, but it had to have come from the fact that Faraday himself was an outsider and was one of the people who could benefit from

• 1:13:43Speaker 04

  that exercise and they, and they continue today, but you know, those, those lectures. I'm saying this to say, I, I say a little bit hyperbolically, and I hope nobody is offended, that the act of getting people to understand science in the world is the Manhattan Project of our time. It's the thing we need, we need to kind of like, I'm talking about generate a whole generation of people who are technically trained and understand that the hardest thing to do is fit a complicated idea in the head of somebody who's not as educated. It's the most challenging thing. It is a technical exercise. It's not like just a charisma exercise, it's a technical exercise. It should be prioritized as such.

• 1:14:26Speaker 04

  And I think, not to be again dramatic, but I think the survival of science requires a movement like this. So that's what I try to do in my career. And I think as I move up, if you will, I hope to kind of give that movement a more of a voice, more oxygen.

• 1:14:43Sean Carroll?

  Well, you know, we can always be better at it. I remember I heard you give a public talk and I was struck by, I mean, it was great on the science, but also you were much more open about your biography than I would ever be. And it was, it's not what we scientists are trained to do, but I knew it was a conscious decision and I, I think that it works, right? People want to hear those personal details. I mean, they love hearing about Forniger's cat and, and E. coli, but also the, the story, and my wife Jennifer tells me the same thing all the time, the story of how the human being got there, that's ultimately what is going to affect them at a deeper level.

• 1:15:26Speaker 04

  And that's exactly right. And it can really be a part of the scientific story, right? And you know, the example that I used in that talk, and I think in my life is, you know, I share half my genes with my mother, who's from Baltimore, of course.

• 1:15:41Sean Carroll?

  Of course.

• 1:15:42Speaker 04

  And the difference of, and the difference in our life trajectory.

• 1:15:46Sean Carroll?

  Yeah.

• 1:15:46Speaker 04

  I mean, do you think that's because of what was in my genome?

• 1:15:51Speaker 04

  Like how could that possibly be true? How could you, I mean, but that's what the conclusion is, right? The fact that she unfortunately left us largely without anything. And I'm able to have this hyper successful career for somewhere where everybody predict that from what's in a genome. So my point is, we have these technical stories in our lives. You have demonstrated, you know, typical plasticity, physics in one sense, philosophy in another. That is taking a trait, the ability to think and compute and apply it to multiple different, like that's the concept. Right? So I think the more we can animate these concepts technically, and that's not any less rigorous in application than the fitness landscape was molecular evolution. So I think that's my thing. We need to animate these personal stories.

• 1:16:35Speaker 04

  They make us more engaging. And I think real discovery in explanatory power lives within them.

• 1:16:43Sean Carroll?

  You were kind enough to come on the podcast before you have a book out, but I know that you're thinking about a book. So at least give you the chance to pre advertise a little bit. What are you, what are you thinking about writing here?

• 1:16:55Speaker 04

  Oh, you're, you're incredibly kind. Well, so yes, I'm writing a book and I'm not going to give a title yet because the title is still in, in development. But what the, what the project is, is actually relates directly to what you just said. I'm coming up with a series of contemporary thought experiments for complicated ideas in biology. Think about, you know, think about what, what the, what the selfish gene did for how we thought about the evolutionary process. Say what you will about it for it now, at the time it was incredibly important, right? For the regeneration of people. Think about the way that kind of reframed how we thought about the process of molecular evolution. I think there's a new wave of problems in biology that require a new wave of innovative thought experiments and analogies.

• 1:17:40Speaker 04

  Not unlike the fitness landscape, right? Not unlike the selfish gene, not unlike the blind watchmaker, but ones that fit the contemporary world that we're in. And that's going to be what the project is about.

• 1:17:50Sean Carroll?

  And is it? I mean, how much are these cultural questions going to come in?

• 1:17:54Speaker 04

  Absolutely, 100% because I think these are the contemporary problems that we're talking about. The problem of racism is composed of 15 different forces. But one of them is a poor understanding of the way the species is structured, right? And our propensity to put things in boxes for silly reasons. That boxes themselves that have leaky barriers, the boxes that are artificially constructed, historically constructed, or technologically constructed. Yeah, no, we're going to address problems like that. We're going to, we're going to, we're going to address a lot of contemporary debates about the way evolution works and what, what, what we can hope. It's a hopeful text because it's about what, what we hope a biology can be in the future.

• 1:18:37Sean Carroll?

  I like ending on a point of hope. That's always very good. So Brandon Abunoz, thanks so much for being on the Mindscape Podcast.

• 1:18:44Speaker 04

  Thank you so much for having me. I had a blast. Continued success to you.

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