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

0:00:00.2 Sean 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 wanna think of it that way, introduced back in the 1930s by Sewall 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, 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.

0:00:57.6 SC: 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 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.

0:01:39.1 SC: 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 'cause, 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. 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 many ways in which biology is much richer and more complicated than many problems in physics.

0:02:22.8 SC: And That's what we're gonna be talking about today. Brandon Ogbunu 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, 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.

0:03:20.3 SC: 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? 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 and action. Let's go. Brandon Ogbunu, welcome to the Mindscape Podcast.

0:04:09.8 Brandon Ogbunu: Oh, it's really quite a pleasure to be here. Thank you for having me.

0:04:12.8 SC: We're gonna cover a lot of ground, I suspect, over the next hour. But let's start with some basics. We talked 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, 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, and the story has become a lot richer.

0:04:43.5 BO: Oh, that's right. That's one way of framing it. We sure did take a nice, elegant algorithm. And that's what makes Darwinian reasoning so beautiful, that is that it is elegant. And even though Darwin himself knew nothing about the molecular basis, he really, really did get down to the molecular level, right, a process. So that's exactly right. We had the simple, elegant thing, and in the last couple of decades, we just learned so much that it's kind of added some nuance to it.

0:05:15.8 SC: Is there We'll get to it, of course. So don't worry about having spoiler alerts or anything. Is there a single, most intriguing to you way in which we've complicated the picture?

0:05:27.8 BO: Oh my, I mean, how much time do we have? I mean, I think there's.

0:05:31.9 SC: Plenty of time. We'll circle back.

0:05:33.6 BO: There's a number of things. So for example, I think the whole conversation around the randomness of 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. What we're learning is that there are elements to evolution by natural selection that very well might be predictable. Okay, and based on the sort of information that we have, there might be patterns in how mutations happen. Are they really random? So this is kind of a little bit of a philosophical question about what randomness means. And I think this manifests even at the molecular level in evolution in terms of how is that random variation that selection is acting upon, how is that actually generated? Questions live in all of those domains.

0:06:33.9 SC: We, entirely approve of philosophical questions here at the Mindscape podcast, as you know. Well, you mentioned the phrase levels of selection. So maybe that's a good place to start getting a little bit more into detail. I've had people on the podcast, philosophers and biologists, arguing about this question, does selection happen at the species level, the population level, the individual level, at the gene level? Is it all of the above? What's your take?

0:07:03.1 BO: 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, 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 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 happens. And that one I have, I think it is happening, but I'm certainly not an ardent advocate that that's everything the way some others are.

0:07:51.2 SC: 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. 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.

0:08:23.0 BO: Yeah. No, I mean, just the basic flow of information from, DNA to RNA to protein to phenotype, which is the classical kind of now, that information flow, like you articulated, earlier, you asked me 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 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.

0:09:03.6 BO: There are multiple ways to generate a tall person. So you have that, right, as a profoundly complicated 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. It has nothing to do with what you're inheriting. So it creates this deep complication with regards to being able to neatly map our genetic information, which is incredibly powerful, and being able to tell meaningful stories about organisms and humans on Earth.

0:09:36.4 SC: Maybe elaborate more on what you mean by nothing to do with what you're inheriting. 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 for the ride. Is that also what you were thinking about, or is there more to it?

0:09:51.7 BO: Yeah. No, no. I mean, I think that's one thing, right? So I think, the silly example is, what color is your hair, Sean? You have.

0:10:03.3 SC: It's kind of brownish.

0:10:04.0 BO: It's red brownish.

0:10:05.4 SC: Kind of dull.

0:10:06.9 BO: So 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 hair color, right? But there's another route, which is dyeing your hair that color. And that has nothing to do with any sort of combination of genetic information. And that's an extreme example of how the environment can do the heavy lifting and all the work that genetics can do. And 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.

0:10:41.7 SC: Yeah. One of the tools that we use to talk about these ideas is the idea of a fitness landscape. I don't know whether the physicists follow the biologists on this, 'cause 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.

0:11:01.0 BO: Yes. Yes. No, the fitness landscape and its relationship, and it's absolutely been invaded by physicists, which 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 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, Sewall Wright, one of the architects of the modern synthesis, came up with this notion of an adaptive landscape, sometimes referred to as the fitness landscape.

0:11:49.4 BO: 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 bit 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. What natural selection to do is it can change the structure of that space. And certainly a lot of evolution is how do you climb out of a valley? So it creates this mental analogy that has since become more than an analogy. Now there's a whole subfield of evolutionary theory where people like study these surfaces.

0:12:38.8 BO: 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 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.

0:13:02.0 SC: It really bugs the physicists that higher on the fitness landscape is where you wanna go. Like that's not true for landscapes. You balls roll downhill.

0:13:10.3 BO: It's not. No. And I've actually given this talk to some like physical chemists and somehow made it out alive 'cause that's exactly right. So they were first on that regard to what degree Sewall 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.

0:13:34.1 SC: I'm 100% sure that one of our listeners is gonna leave it in the comments. 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 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 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.

0:14:07.2 BO: Yeah, that makes sense. I think we'll start with the reverse direction. We'll go with the creationist one. 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. This has happened to me. I published a paper on fitness landscapes, and I think several creationists cited it. I'm just, "Wait a minute, what?" I think in terms of technical criticisms of the fitness landscape, it's really, really interesting because we can think about powerful analogies. Obviously, physics has them. Obviously, biology has them, from the selfish gene to the blind watchmaker to spandrels, the famous spandrels. There's several 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.

0:15:10.9 SC: You can put it in equation.

0:15:12.9 BO: It can be. 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, "Y'all need to chill. This is supposed to be an analogy. 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 over-apply it to context and circumstances and biologies that it originally was not intended to.

0:15:49.1 SC: Right. I mean As I said, I'm a big fan of the metaphor, but I do therefore wanna probe its weak points so I know where not to go overboard. One thing is that you say the x-axis, but of course, there's a gajillion axes, right? It's very hard to actually visualize this high-dimensional space of all the different genes I could change.

0:16:11.0 BO: No, that's exactly right. So That's 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 'cause there was a point when we actually couldn't mutate very many things in a gene, and so the analogy actually mapped where we were technologically. Now, you can take a protein and just generate techniques like deep mutational scanning, and now there's high-throughput CRISPR cast sorts of methods. I can change, say, hemoglobin. You can change variants of every single amino acid in a large combination. These are hundreds of thousands of variants. How do you actually depict that in anything resembling a visual space? That one isn't a neat three-dimensional landscape. That's gonna be 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.

0:17:16.6 SC: 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 'cause it's very useful. 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, 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?

0:17:55.5 BO: No, no, you're not. I mean, I think there's a lot of people who ask. I mean, I think the fitness landscape, 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. For example, graph theory, right? You look at the fitness landscape, it has this graph theory-like character, right? And so there are graph theorists who have walked various features and mathematical tools from graph theory for the questions in evolution. So I'll say the good things first, is that graph theory, analytical geometry, network theory, which is related to graph theory, thinks about these sorts of problems.

0:18:38.0 BO: There are people who have tried to reconcile this thermodynamic, if you 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 unification, right? Of a mass set of mathematical tools to truly understand how evolution works? 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 we need to go further here.

0:19:17.0 SC: 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. 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 at 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 'cause we're used to doing gradient descent. We're used to like walking in the direction where things are increasing, but if things are wildly fluctuating, that's hard to do.

0:20:00.6 BO: No, that's exactly right. And I think this is one of the arenas. There are features of the way fitness land, 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 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. So what's the problem here, right? The problem here is this can create that ruggedness. This creates that confusing because you have your 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.

0:20:53.9 BO: And then you get this mutation, 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 is working. And this remains a cutting edge problem in evolutionary genetics.

0:21:11.2 SC: Yeah. This is why we got to pay you the big bucks. You got to solve these hard problems.

0:21:15.4 BO: Well, you know, I mean we're getting there. AI will help.

0:21:20.0 SC: AI will help us all or at least it will confidently state it has solved the problem, that's something.

0:21:25.0 BO: That's something that's unquestionable.

0:21:28.3 SC: 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. 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?

0:21:56.5 BO: Oh my. Yeah, yeah. No, this is a very, very compelling question about. So number one, let's go back to what the the fitness landscape metaphor is saying. It's a, thinking about a physical surface. Some really smart people have started to invoke the notion of a fitness seascape, 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 the world works. Environments are fluctuating, right? I just told I'm wearing this sweater today because it is much colder today than it was yesterday. And I have no idea what the weather is gonna be tomorrow. Simply that fluctuation in temperature changes some organism, probably a microbes actual fitness landscape, maybe on our bike, microbiota, meaning something that was uphill yesterday, maybe 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.

0:23:01.2 BO: So 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 ski scape because they're influencing each other in 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 that behavior is a break and a motor on evolution, right? So it's so if you're involved, right, so the idea is, and basically just talking about how behavior itself becomes something that 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 you 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 because the problems in the real world are just so much more complicated than can be captured in a simple, three dimensional image.

0:24:06.7 SC: 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 wanna 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 equilibria 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.

0:24:46.9 BO: Yeah, I mean, yeah, maybe not quite. I mean, yeah, not equilibrium systems is a buzzword in a lot of different fields in part because as you say, the analytical tools, the original tools kind of are built around equilibria and even fixation to get the world is kind of influx as we described. So the, so just bridging it to 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 in this landscape, right? This idea that we've just optimized the what's interesting that you talk about how creationists utilize and kind of weaponize even the fitness landscape that I can also see how that can work because the notion that there's a neat peak 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?

0:25:40.1 BO: And then it's a kind of 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 "scientific side" of this problem has issues when they push for equilibria and stability and optimal and that's just not how the biological world is constructed for a myriad reasons. So I think that's what I would say the connection is between the equilibria conversations from econ.

0:26:12.6 SC: Yeah. Okay. I mean, do evolutionary biologists explicitly use the language of equilibria? Like they try to find some happy place? Cause I know economists do. And that was the.

0:26:23.4 BO: Yeah, yeah. I mean, I think in different steps for simple questions. So for example, evolutionary game theorists, for example, they absolutely formally use the mathematical understanding of equilibria, right? To think about how a in a situation where you have a bunch of evolutionary inches, what's the evolutionary stable strategy, for example, as John Maynard Smith on Nash and others would have articulated. So yeah, so equilibria is certainly used there. Equilibria, I think like in population genetics, we would say fixation, right? That's when a, it goes to fixation is fixed in a population. So that's an iteration of equilibria. It's like you have an antibiotic resistance gene that is better than everybody else at some point through time. And we have elegant math to predict when this is gonna happen. It will like rise to fixation, meaning virtually all the, virtually all, everyone in the population has that, that illegal fixed. So that's kind of where equilibria is also invoked in evolutionary biology.

0:27:28.0 SC: Oh, 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, et cetera. But what are the specific questions that get raised here?

0:27:56.3 BO: Yeah, yeah, 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. 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 relative fast of offers. We can see them evolving fast. They can adapt quickly. The generation time is short. And now I kind of in the last half but more than half century now, I mean, really more than a century, we can grow these things in the lab in really, really elegant ways. We now have their genome sequences fully understood. They're very, very tractable systems. So we're using kind of microbes to ask fundamental questions about the way that evolution is happening, largely at the protein scale.

0:28:54.9 BO: So for example, this question about the fitness seascape, the fact that it changes. What if I have two closely related drugs that we nonetheless use interchangeably at the bedside, right? Meaning we think they're very, very similar. I had a background where I did some clinical microbe. So we think they're similar. We're looking at things all right, actually even subtle differences in the chemistry of a drug, very subtle changes that fitness landscape. And so resistance to do these two drugs actually might move differently at a different pace. So that's kind of highlighting the relevance of this fitness seascape denim to the fitness landscape metaphor, 'cause it's basically saying, wow, even slight changes in an environment can profoundly influence the pace and direction of evolution. So we're studying questions of that sort.

0:29:48.5 SC: Yeah, I mean, we all know, I guess, that 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? How much is the study of this purely empirical, we try this and see what happens, versus how useful is the theorizing here? Has the theorizing caught up to the messy empirical reality?

0:30:14.5 BO: What a good, good question. I think I have a lot of thoughts about that. So number one, Richard Lenski's long-term evolution experiment remains, I think, one of the greatest demonstrations of how evolution by natural selection works. And it continues. It's now, I believe, with Jeffrey Barak, 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 predictable and it does all these amazing things.

0:30:48.6 SC: Maybe for the audience, explain the long-term evolution experiment.

0:30:51.2 BO: Sure, sure. Thank you. Yeah, thank you. So basically, Richard Lenski and colleagues at Michigan State, gosh, many decades ago, evolved strains of E. Coli. It's a gut bacteria, soil bacteria that we have 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, Lenski 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 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?

0:31:52.4 BO: 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 happens. This experiment has been mined by theorists, and it has generated new theory. So to the second part of your question about, has, it's theory. So I think those things have been in conversation experimentally at a really, really good way. Now, this last point you had question, which was more provocative is, has theory caught up? Oh my. I mean, there's a big joking debate within population genetics, which is where a lot of the theorists live.

0:32:50.1 SC: They call it beanbag genetics, they would kind of pejoratively call 'cause it just seems too optimal. It seems based on a world you might have heard of Hardy Weinberg equilibrium, which is this, kind of equation that describes, it's a null model for how evolutions operating under optimal conditions when you have populations that are breeding at a certain kind of way. And it basically reflects no reality ever, in the way biology works. So I am saying this to say, a lot of theories 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. I think the good news there, and I don't look at that as a bad thing, I think now in the, what I call the post genomic era, as I call it, where genomics now just happens at a cheap pace.

0:33:35.6 BO: It's no longer the, it's not the rate limiting step in any scientific in Denver. Any, I mean, rarely there's some organisms that are hard to sequence, but generally speaking, everyone has access to it. I think we're gonna enter a golden age of theory personally, 'cause we we're gonna have more data than we know to do with, it's gonna 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 theory that matches this dynamic world that we're In.

0:34:07.9 SC: Yo know I have a historical example I always pull up, when people talk about this, 'cause 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 harken back to Tycho Brahe, the astronomer who just, 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 the law of gravity, which is actually understanding. So I am 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.

0:35:05.4 BO: I couldn't agree more. And I think, you point out something very important, and that is with new data, are gonna call, are gonna come associations between things. That's what these computational tools do. We'll be able to, but that is not a mechanistic theory, of understanding for how a system works. But I'm just, but I think computation can help us with theory as well.

0:35:27.4 SC: Sure.

0:35:27.5 BO: I don't think, I think. And I think it can help us. So that's what I'm excited about. It's okay, well, the theory, going back to the fitness landscape, the theory from the modern synthesis and the statistics from the modern synthesis, Fisher, of course is one of the architects there. Everybody who does empirical work of any kind is still using fisher and statistics. And so, I say this to say, we've done a lot of good, I mean, like, I mean in terms of technical good, not moral good, and not social good, but statistical good with theory that's now essentially old, right? We've done a, lot of good with that. I really, really wanna see what the next generation of theory looks like because it could and should be different. And I think we can use computational tools to help us get there.

0:36:15.6 SC: Well, okay. Back to the real world a little bit. Do you have examples of your work in diseases and their evolution and adaptation where thinking about them in the right way has literally helped fight a disease in some way? Or are we still at this sort of, trying to grope towards some understanding level?

0:36:33.5 BO: Yeah, no, no, it's a good question. So I think it has, I think the, and I'm not personally taking credit for this. I just mean like my colleagues 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. So that's the most conservative view, that there's no question that we think about. And evolution now is part of drug design.

0:37:23.1 BO: So think about resistance profiling upfront. What would a 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 gonna evolve? That's kind of one of the gold standards. And these are the sorts of things that we ask in our lab and colleagues of mine ask. And I think the gold here is a paradigm where we can cut infectious diseases off drug diseases off, 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.

0:38:08.0 SC: Wow.

0:38:08.7 BO: You have a right. 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 it is actually computationally, maybe the tools will look like that. I'm saying this to say, the idea is there, the weather's, 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 at evolution, there's various forces that are influencing how 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 it's at, this can happen. We can have drugs ready for that. Right? And we can actually help people. Who are infected with various pathogens that way.

0:39:00.8 BO: 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, that 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 do this sort of technology? No. But I think 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.

0:39:34.0 SC: 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 gonna find different things. And these days, we seem to be moving more toward a view 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.

0:40:01.4 BO: That's right. That's exactly right. And I think that's what makes this current, you know, 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 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 four different mutations, is there really a 25% 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 biochemistry and biophysical level, like there's just certain sorts of chemical modifications that are more likely to happen.

0:40:49.3 BO: 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 or, and even that, and I think, in some cases is not true. So I think the evolution of mutation rate is a hugely complicated field for this read. It's basically saying, yeah, it's random by some definitions. And that's a debate I do not want to have with philosophers. But yeah, it's random in the sense that nobody, no force is putting it there actively, but it's non-random in the sense that there are potential leanings in forces that may take evolution towards certain directions. So anyway, I'm just saying, yeah, there are the fitness landscape, we 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.

0:41:45.7 SC: 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 yes, I have learned over the course of talking to biologists on the podcast that functional parts of the genome don't mutate nearly as much as the junk parts, because they're kind of protected, they're important. And so it's absolutely not all chances are equal.

0:42:15.3 BO: 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 some of the reasons that you outlined. And I think this relates to a concept that I'm really interested in called evolvability, right? And it sounds like an interesting word, because it's kind of like evolvability. It's like the capacity of an organism or a replicator to evolve, 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. Can evolution make one organism more likely to evolve?

0:43:00.9 SC: I like that.

0:43:06.9 BO: Can evolution actually make evolution, a gene in a genome more likely to evolve, or less likely to evolve? And what does this mean? Okay. And so this is a concept, actually, Massimo Pigliucci, and in fact actually several other kind of philosophers have commented on. And it's a really, really provocative concept, because it really asks, kind of, at what levels is evolution happening? Does evolution actually care about that? Can evolution actually control that? If it does, if you do see that organism A is more evolvably evolved faster than organism B, is that just a byproduct of something else? Or did evolution actually engineer it 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.

0:43:57.2 BO: How do you actually test? I mean, me and Sean, me and you, or who's more evolvable, right, through evolution and history? I mean, at the individual level, do you mean at 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 what dictates it? Is it the topography of the fitness landscape? Maybe if the fitness landscape is really, really smooth, right? Maybe that's a feature that allows, and so we've asked questions like that in our laboratory as well.

0:44:30.1 SC: You're younger than I am. You're much more evolvable, at least I think, you know.

0:44:35.7 BO: I don't know. I mean, look at what you've done and what you continue to do. I think it's...

0:44:38.9 SC: I used to be evolvable. Now I'm just an old fuddy-duddy. So we mentioned complexity earlier, and obviously.

0:44:45.2 BO: Yes.

0:44:46.1 SC: 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?

0:45:11.7 BO: What a great question. I mean, I think that I'm gonna hit this in several ways. So I, you know, I think the, 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 hard for the 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.

0:45:40.9 SC: I like that.

0:45:41.0 BO: We're The only field that has that wired in the whole point is that the world is complex. And so that mean...

0:45:45.1 SC: We're non-equilibrium.

0:45:46.7 BO: It's a non-equilibrium thing. And we're gonna fold new things in formalisms and like math, formal mathematics does that, and formal evolutionary biology does that in formal, like we incorporate new things, but that's not part of the identity of the field. We just, 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, think about the formalisms inside the cabinet. Are they useful for Right. In terms of thinking about them across paradigms? 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 non-linear interactions between them and the emergent properties that come between them.

0:46:50.4 BO: Yeah. That one is kind of milk toast 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 we're like, "Oh, yeah, that's obvious." 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 non-linear interaction between mutations or not, right? Yeah. I don't call that com, that's not formally 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 language for it, the vocabulary for it, and the word for it.

0:47:37.0 BO: It's so much easier concept to sell in various settings, because I think complexity science equipped us all with this vocabulary. Now that, now let's go to the other part of the question is like the tools itself. Yeah. When I study the complexity, people who think about econ, do I walk that directly over into thinking about fitness and landscapes and go, o"Oh, I have a tool from the... " No, not, so much. I think, and do I, but conceptually I do. Right? Like I said, I mean, I think now I think the field that I think where this work, the is best arguably, is how networks re has completely changed how we think about epidemics, right? Like, for example. Like that one, yeah. I mean, that was a set that, like I said, it's origins and has, it has relatively recent origins network theory of course related to graph theory.

0:48:28.3 BO: But it has walked into a field of mathematical epi that had a long history and really smart people already doing great work and has completely been like, introduced new questions and come up with new strategies that have been practically helpful. So the second part of the question, do I think that can complexity cabinet of tools and ideas and mathematical tools and taking them and walking them across to the different problems has helped, I think absolutely. Which is why I continue to mine this cabinet directly or indirectly for new ways to study the natural world.

0:49:02.8 SC: 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, people look for a stable foundational point, certainly in philosophy, right? Going back to Descartes, where it's like, 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, the better off we'll be. If, if you'll forgive me, rhapsodizing a little bit.

0:49:51.7 BO: No, this is totally right. And I think, you know, there's a great new volume from Santa Fe Institute on foundational papers. You know, 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. Oh yeah, it has canon right there, there is real canon to it. Not, it's not, it doesn't have a principia or a origins of species.

0:50:20.8 SC: Not yet.

0:50:20.8 BO: It doesn't have those. Not yet. But right. Not yet. Right. But it does have a canon. And I think that's what really makes a difference for me, laughably. And, and you know, I mean, try not, I don't wanna discredit everything I've ever said by telling you that my introduction to complexity science was Dr. Ian Malcolm from Jurassic Park.

0:50:42.6 SC: Of Course, you're not the only one.

0:50:43.9 BO: I mean, and I was a young person then, and I think everything about who he was as being iconoclastic and I was like, "Wow, I want to be like that. Yeah, I wanna be like that dude." But I think, there's that, and then for me, there's the More is different paper from 1972 by Anderson, about kind of, you know, broken symmetries. And that's a foundation idea. 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 are a couple of influences for me.

0:51:16.9 SC: The idea of the canon is great because I'm actually teaching with Jan Ismail a course, 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.

0:51:39.9 BO: Absolutely. I think other fields should kind of, and they kind of have, taken, I mean, yes, I'm drinking the Kool-Aid on complexity science. I really think, you know, 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 can appreciate this, right, more than anyone. But I think when I found complexity science, it was just like a crowd right where I didn't have to do that at all. Like 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 the Jeffrey Sachs and conversations. People would be like, why are you reading econ? Because it's kind of interesting, and important to understand. But I don't have to do that in the, you know, in the complexity community.

0:52:23.2 SC: 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.

0:52:34.7 BO: Well, at all, and just. And people aren't, I mean, we're fancy grumpy professors, but things are not credentialist there in a way. I don't think anyone cares about what, where you come from. Not really. I'm sure that it's elitist the way all these spaces are elitist, but there's a different set of the hierarchies also structured there differently, I feel like, just intellectually. And I think that allows for the cross-pollination, you kind of have to have a flat intellectual surface for complexity to work because things have to be able to meet each other and 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 here.

0:53:14.1 SC: They were fine.

0:53:15.9 BO: And then they weren't the people, people serve as the bridge, and which is why Schrodinger's What is Life is such a foundational idea, a book for this, for thinking about how physics walked into other fields.

0:53:27.7 SC: Okay, good. Back to the biology here, 'cause we've left some things hanging here. 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?

0:53:52.2 BO: Yeah, yeah. I have this mental equation that I use. It's not a firm one, but it's a control equals prediction plus engineering, right? It's how I think about these sorts of things. Okay, so the idea here is if you want to 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 the engineering part is right.

0:54:20.6 BO: So be it cooking, right? The reason why what a good chef can do is that a chef knows what's gonna happen if you add oregano and the chef can't add oregano. Yeah, right. Can't add oregano. That is a means through 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.

0:55:01.0 BO: It was the notion that you can optimize populations. And we don't have to. 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 right next to that, adjacent, is the fact that it was technically completely wrong. It had a very poor understanding of the way human traits were constructed. So at some point, if you get prediction really, really wrong, or and you've built it on bad tools. Now, let's flash forward today to the good news.

0:55:29.3 BO: The good news is for certain molecular systems, we actually 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. I mean, we have this down to the physical scale now though. So we know so much. So prediction's getting stronger.

0:56:10.0 BO: We absolutely, better than ever, because of Jennifer Doudna and colleagues, can engineer genomes at an increasingly better resolution. So, we are getting the tools now we can actually steer, as some of my colleagues call it, steer evolution towards certain things. Now, of course, Frances 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 the sugar faster. That's a sort of control, because 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, 'cause 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. Now we have the technology to actually steer and engineer a better hemoglobin, 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 this is where theory and experiment come in. That helps with the prediction part. And so, this paradigm is here already and will continue to be a thing.

0:57:24.9 SC: So, yeah. Frances Arnold, my former Caltech colleague and friend, if I understand correctly, her work was almost like breeding, right? She was really just changing external parameters like we do with dog species or dog breeds or something like that. Like you say, now we can go in and monkey with the actual DNA. Have there been, I'm not up on it. Have there been big breakthroughs in doing that? Not just monkeying with the DNA, but for a particular purpose, designing an organism?

0:57:57.8 BO: Yeah. I mean, the work that is closest to mind is colleague Jacob Scott and colleagues, Emily Dolson and colleagues, have done stuff like actually steered evolution down certain, and it's like drug resistance, so similar problems. Like how a microbe or a cancer cell is evolving resistance to a drug. Yeah, people have tweaked parameters. There's just a bunch of labs. Michael Boehm 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.

0:58:32.9 BO: Now, I think what you're asking is something much more grander than that, as in do we have like 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 the generic sense. But have we yet, I think what would be the gold standard? What would be the thing that would actually announce this as whoa, 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 for, let me give you an example. There's a protein called RuBisCO. That we have.

0:59:21.1 SC: Okay.

0:59:21.3 BO: RuBisCO is one of the most abundant proteins on earth. It's basically a critical component of kind of how plants basically, 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 Shoulders at MIT for example, good friend, we're trying to come up with ways to engineer this thing. If someone can invoke a way to steer the evolution of RuBisCO 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.

1:00:14.6 BO: So, this is an example of where this sort of kind of control, and there's just 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 in investment in a lot of companies, 'cause this is gonna 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.

1:00:40.3 SC: But you and I are rich in intellectual reward. Doesn't that count for something if not actually rich in money?

[laughter]

1:00:48.8 BO: I mean, of course. No, no, no. I love being able to do this. I think there's a lot of stress in doing that.

[laughter]

1:00:55.6 SC: Yeah. Fair enough.

1:00:55.6 BO: More stress.

1:00:55.7 SC: 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. 'cause you talk about evolving proteins and in my brain, the genetic information is in DNA and that gets transcribed in the RNA and then that prints out some proteins. So the thing we should be engineering is the DNA, what good is it or how do we think about engineering proteins directly?

1:01:21.3 BO: 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 what, so 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.

1:01:36.8 SC: Got it. Okay. That's very helpful.

1:01:37.7 BO: Yeah. So, it's happening there. So the actual engineering is going on there, and then you can make a lot of, you make proteins from that information.

1:01:46.1 SC: 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.

1:02:04.0 BO: Yeah. I mean, I think this is really, really important. I think this is where I like to position myself as a bit of a gad fly in Medfield. Because I'm all of these things at once and by all of the things, I mean this, I believe in like the many of the deterministic laws that underline population in genetic systems. I think these 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 that's, I'm there, but I'm also on the other pole of, if we wanna solve any real problem in contemporary biology, RuBisCO, drug resistance, cancer evolution, genes for depression, genes for high type 2 diabetes. If you wanna look at any problem in biology, we are not going to solve it with simple deterministic perspectives or tools.

1:02:56.8 BO: We must, must, rigorously incorporate these other dimensions that are influencing the way that fitness landscape is structured, that influence the topography of it, that influence the way variation is generated. And that is 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 now there's epigenetics, which I won't touch right now, but I think it's a really a kind of other thing that modifies how phenotype happens. Ply atrophy, 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 topography of the fitness landscape. I think this is where we need to live theoretically, is learning how these different concepts, which complicate evolution, are actually a beautiful thing to study.

1:03:56.5 BO: 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 in 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, a Sean Carroll in physics. I mean, good luck with that, I mean, 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 I'm invoking the notion that this is dictating who you are in, a very meaningful way.

1:04:53.1 SC: 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 that come in, when we start talking about designing organisms. You must have thoughts about this. Do we have any guidelines about how to do this responsibly? I worry that 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, 'cause the things that could happen are maybe a little scary.

1:05:44.7 BO: No, what a good way of putting it, that you're a trailing indicator. And you're right. 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 black 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, about engineering embryos with certain traits and what that means and how you're influencing future generations. 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.

1:06:41.4 BO: 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. And it's not like the work itself is bad, but that the predictions that you're going to make are gonna 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.

1:07:12.0 SC: I do, yes.

1:07:13.6 BO: Which goes, I learned it from Gould, but I think many people have used it in various ways. 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, 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, keep collecting data, and keep collecting data. So, when it comes to the problems of predictive genomics or engineering, we just have this overconfidence that we even know what we're doing.

1:08:00.4 BO: These traits are deeply complicated. The environments change them profoundly. And the notion that we understand that 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, this isn't doing what you think it's doing." And because of that, maybe we shouldn't do it right now.

1:08:21.0 SC: Well, let me be a little disruptive here and mention that because 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, et cetera. This is obviously sort of a commonplace observation, but you can't separate the science from the culture, right? There are reasons why we've been brought to this place. Is there something scientists could do? I take it for granted, Brandon, that you are on my side in thinking that vaccines do work.

[laughter]

1:09:11.5 BO: I do. I happen to share that view.

1:09:15.0 SC: All right. 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?

1:09:27.6 BO: Oh my. So first thing, and I'm not just saying this for no reason. I mean, I think it's what you've decided to do with this, an example, that was what you decided to do with this podcast. I was reading the other night that Michael Faraday, one of my scientific heroes, amongst many things, started the Christmas Day Lectures series. He 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, 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.

1:10:10.6 SC: Exactly.

1:10:11.0 BO: And was one of the people who could benefit from that exercise. And they continue today, those lectures. I'm saying this to say, 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 just a charisma exercise. It's a technical exercise. It should be prioritized as such. 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 more of a voice, more oxygen.

1:11:11.4 SC: Well, 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's not what we scientists are trained to do. But I knew it was a conscious decision. And I think that it works, right? People wanna hear those personal details. I mean, they love hearing about Schrodinger's cat. And [laughter] And E. Coli, but also 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 gonna affect them at a deeper level.

1:11:54.7 BO: Oh, that's exactly right. And it can really be a part of the scientific story. And the example that I used in that talk and I think, and in my life is, I share half my genes with my mother, who's from Baltimore of course, she's from Baltimore of course.

1:12:08.8 SC: Of course.

1:12:10.0 BO: And the difference in our life trajectory.

1:12:13.7 SC: Yeah.

1:12:14.5 BO: Do you think that's because of what was in my genome? Like how could that possibly be true? How could you, wait, but that's what the conclusion is. 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 phenotypic 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. So I think the more we can animate these concepts technically, and that's not any less rigorous in application, in the fitness landscape, was molecular evolution. So, I think that's my thing. We need to animate these personal stories that make us more engaging and I think real discovery and explanatory power lives within them.

1:13:10.8 SC: 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 thinking about writing here?

1:13:22.7 BO: Oh, you're, incredibly kind. Well, so yes, I'm writing a book and I'm not gonna give a title yet, because the title is still in development, but what the project is actually, it 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 what the selfish gene did for how we thought about the evolutionary process, say what you will about for it now, at the time, it was incredibly important.

1:13:53.6 SC: Yeah.

1:13:54.0 BO: For a generation 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, not unlike the fitness landscape. 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.

1:14:17.8 SC: And is it, how much are these cultural questions gonna come in?

1:14:22.3 BO: 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. 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 gonna address problems like that. We're gonna address a lot of contemporary debates about the way evolution works and what we can hope. It's a hopeful text 'cause it's about what we hope a biology can be in the future.

1:15:04.8 SC: I like ending on a point of hope. That's always very good. So, Brandon Ogbunu, thanks so much for being on the Mindscape Podcast.

1:15:11.4 BO: Thank you so much for having me. I had a blast. Continued success to you.