Episode Transcript
[00:00:04] Speaker A: Id the Future, a podcast about evolution and intelligent design.
Welcome to id the future. Im your host Andrew McDermott. My guest on this episode is Doctor Brian Miller, and our topic is how evolutionary fitness landscapes bolster the conclusion of design in living things and how this relates to the research of bioengineer doctor Stuart Burgess.
Now, Doctor Miller is a senior fellow of Discovery Institutes center for Science and Culture, where he serves as research coordinator. He holds a B's in physics with a minor in engineering from MIT and a PhD in physics from Duke University.
He helps to manage the CSCs ID 3.0 research program, and he speaks internationally on the topics of intelligent design and the impact of worldviews on society. Brian welcome back.
[00:00:56] Speaker B: It's a pleasure to be here.
[00:00:59] Speaker A: So you've written recently@evolutionnews.org about a recent paper published by Doctor Stuart Burgess that demonstrates the genius and optimality of vertebrate limbs. In your articles, you point out that evolutionists have historically predicted, based on their theory, that vertebrate limbs should often be poorly designed since they were the product of an undirected process.
But the more carefully these living systems are studied, the more evident it becomes that something quite different is going on. Now, in one of your articles, you talk about the concept of evolutionary fitness landscapes. This is an interesting method that visually plots an organisms fitness within its environment. Can you tell us more about fitness landscapes?
[00:01:41] Speaker B: Sure. It is, as you mentioned, just a simple way to visualize the variation in populations for some species. So if you imagine, let's say a large field, and this large field had hills, it had valleys, it might have just a region that's unpassable. And what happens is every point along that field could represent an individual in a population.
And what happens is the higher you are on that landscape, the idea is that you're more suited for that environment, so you're more likely to survive and have offspring. So what this fitness landscape does is it's a way to represent how populations in a species change with time. So you're naturally going to go uphill in this landscape, because again, if you go uphill, you produce more offspring, you're more likely to survive, so you'll have the genes that represent you will be reproduced more and more, while if you're in a low point, let's say you're in a valley on this landscape, you're not likely to have offspring. So the genes that correspond to that point will vanished. So the idea is that you can monitor the development of a population by seeing how individuals move around this landscape. So population is like a local region in that landscape. It shows the variation, and then that population will tend to go uphill over time. So that's essentially what a landscape represents.
[00:03:08] Speaker A: Okay. And the extent to which a population can traverse that landscape depends on whether the landscape is smooth or rugged. And whether it's smooth or rugged depends on whether mutations interact additively or epistatically. Tell us more about that part of it.
[00:03:25] Speaker B: Yeah, certainly. So what happens is, if you imagine this landscape is two gentle hills, so they're not that tall. They kind of merge into each other. And what can happen is that a population can move from, let's say, the base of one hill. And you can imagine that hill might represent finch peaks, where one hill might represent smaller finch beaks that are shorter and thinner, while the other hill represents finchbeaks, which are sharper and larger. Now, what happens is, if that's the landscape, you can easily move from the base of one hill to the peak of another. And again, that goes to the mutations, because if you imagined that all of the population was the same genetically, so you're at the base of one hill. What happens is, if you add new mutations, and each mutation is beneficial, that could help you to move up the landscape. So you have one mutation, it's beneficial. A second mutation is beneficial, and if you have both mutations, it's even more beneficial. So the benefits add together. So if that's the nature of how mutations interact, that they just improve on the organism, then you can easily move uphill in the fitness landscape. It's a very smooth landscape. But let's imagine that you have two mutations, both of which are beneficial. But what happens is, if you combine the two, then it becomes detrimental. So that's called epistasis. That means that the effect of two mutations is not the sum of the effects of each individual mutation. If you have this interaction, which is negative, then you get these very rugged landscapes where the landscape is very sharp peaks, very deep valleys. In a landscape like that, it's very hard to traverse it for long distances, because most of the time, you're going to end up at the peak, a peak in that landscape, and get stuck. And to move to another peak, you've actually got to go downhill. You've got to endure negative combination mutations before you can get better.
[00:05:35] Speaker A: Okay. And for listeners trying to visualize these visualizations, I do encourage you to go to Brian's articles. Look these up online, and you'll get some good videos and pictures kind of showing you what this looks like.
Now, Brian, on the topic of mutations. I recall from my study of biology that most mutations are either neutral or harmful. So how does that play into these landscapes?
[00:06:02] Speaker B: Yeah, and that really is one of the great challenges, because if you envision this landscape where you can go from hill to hill to hill over long distances, so that you could go from, let's say an amphibian to a. To a reptile or a. Some dinosaur to a bird or some sort of shrew to a human, well, then you have to assume that there are lots and lots of mutations which are either beneficial or at least neutral, that can make large scale changes. The challenge is what happens in practice is that virtually every mutation which creates some really significant change, the sort of change you would need to alter the basic architecture of an organism, is harmful, and that's a big problem. In fact, even if you look at mutations that produce small changes, more often than not they're harmful. And even those mutations which are beneficial typically will degrade a gene. They'll actually remove information from the system. Like Mike Behe talked about in his book, Darwin devolves. So what that means in practice is these fitness landscapes, you have some variance, you have some variability, you can traverse them in small distances, but it means it's essentially impossible for you to go long distances along the landscape, because most of the landscape represents hills and non functional intermediates. That's the big challenge.
[00:07:25] Speaker A: All right, so how do the fitness landscapes play into the conversation about Doctor Stuart Burgess's research? How do they fit in?
[00:07:34] Speaker B: Well, what happens is, if you read the literature, which I can talk about in a bit, it suggests that these landscapes are very rugged, that you've got lots of local peaks, lots of valleys. So what that means in practice is an evolutionary search will almost always end up at a peak of a suboptimal design. So you should end up with maybe a limb that works, but it shouldn't work optimally. It shouldn't be the best possible limb. So that's why evolutionists have historically anticipated that they'll see poor design in nature. So they haven't always looked to nature to teach us how we should design our machines. Well, Stuart Burgess, his research showed that the truth is the opposite of what people expected, that what you see is that vertebrate limbs are invariably optimally designed. They're the best design possible for the environment of the organism. And this is not what you'd expect if you're dealing with a blind, undirected evolutionary search.
[00:08:40] Speaker A: Okay, that makes sense. So then the question is, how did that organism get to that optimal design? And we're arguing that the evolutionary pathways are limited and we can get into this at the right time. But am I correct in assuming that the distance between beneficial mutations is that sequence space where the selective process has to search and find?
[00:09:07] Speaker B: Yeah, that's right. Because when you think of these landscapes, you can think of the landscapes either in terms of variables directly related to the traits. So you can imagine a landscape, as I mentioned before, which would be like finch beaks, where it could be the width of the beak, the length of the beak, the thickness of the beak, the sharpness of the beak, or what's more appropriate in evolutionary discussions is you think of the landscape in terms of the genetics, that every point along a landscape corresponds to a particular genome, a particular DNA sequence. And what happens is that if you have to want to go from, let's say, one peak of an organ, of one peak to another peak, where you want to create some significant change, the path from one peak to another peak corresponds to a series of genetic changes, a series of mutations.
So what has to happen if you want to go a long distance in this landscape is you have to have the right series of mutations, which will alter the organism in the right way to dramatically change it. And that's a huge problem, because, as we've talked about many times, even if you're looking at a simple protein, which is one of the building blocks of life, what you find is that different proteins with different structures are highly isolated in what's called sequence space, the landscape in terms of the genes. And to go from one protein to another protein, you have to traverse a large area in sequence space, a large region of sequence space of non functional sequences. So the time needed to find, let's say, even a new protein is enormously long. And if you're talking about more of a large scale change, then you have to not find just one protein, but whole series of proteins, new tissues, new structures. So that's theoretically implausible.
[00:11:02] Speaker A: Yeah, that makes sense. Well, has any research evaluated the nature of the fitness landscapes for vertebrate limbs within an evolutionary framework?
[00:11:11] Speaker B: Yeah, there's several research papers that address that very issue. In fact, in my article in Evolution News, one of my most recent articles, there's a 2022 paper published by a paleobiologist named. Named Graham Slater. And he was studying the fitness landscapes for mammal carnivores based on 16 trait variables. And these are variables related to things like teeth, skeletons and size. And what he published is that there are multiple fitness peaks, that it is a rugged fitness landscape. So you see a lot of research like that that's looking at just simply the traits. But there's also other research that gets into even more detail. And I just looked at a 2023 paper by Busselele et al.
I did not do that name properly. So if he's listening, I apologize. And it's titled the fitness landscape of a community of Darwin's finches. In this particular paper, they're looking at the finch beak length, depth and width variables. And again, what they find is sort of the same thing, is you have this rugged fitness landscape where you're not going to just smoothly go from one peak to the other, but you're going to have to go through valleys. It's much more complicated.
What happens is these papers are dealing with, let's say, a basic design logic and minor variations in traits, at least the last variable. So they imagine that if you have environmental changes, you might be able to go from one peak to the other, because the fitness isn't all that different. But the situation gets really, really problematic when you're dealing with vertebrate limbs, because what happens is you look at research in robotics, and if you look at, let's say, the evolutionary algorithms applied to robotic design, where you're actually looking at the design of things like limbs, and what they found is that you, again, are dealing with a rugged fitness landscape. In fact, there's a paper, a 2021 paper by Bonacie Bonacillo et al, and it's on the evolutionary algorithms of robotic design. And what he found is, when they were trying to design new robotic structures, you would typically end up in a suboptimal peak and get stuck there. And there was just no way, through an undirected search, you could find an optimal robotic design. So, again, if you look at all this research, either research based on fossils, or the study of trade variables or robotics, it all points to the same conclusion, that you're going to have multiple peaks. Most of those peaks in the landscape will be suboptimal. They'll be not the ideal design again. And that's the exact opposite of what Stuart Burgess found.
[00:13:55] Speaker A: Okay. And the more complex the trade, the more rugged the landscape is. Well, you spoke about the general pattern of limbs in terms of bones, ligaments and tendons. Are other factors important in the function of limbs, and how do those factors relate to the discussion?
[00:14:13] Speaker B: Oh, yeah, that's a really great question. What happens is an optimal design for something like the flipper of a whale or the hand of a human requires exquisite engineering of the bones. The tendons and the ligaments, they have to be interconnected in exactly the right way. But that's not sufficient. What also has to happen is you have to have countless sensors throughout your limb. And what these sensors do is they detect things like motion, pressure, tensions. And these sensors are integrated with a very complex control system. And that control system uses the information from those sensors, like if you grab something and it sends signals back to the muscles to alter the muscles and the motion in exactly the right way to manipulate the limbs properly. What happens is these control systems are very complicated, and they have to be very carefully designed, or they won't work properly. In fact, I just recently read a 2023 paper by Rawat et al. And it's entitled Intelligent Control of robotic a comprehensive review. And what this paper talked about is the extreme difficulty of designing these control systems, how you have to fine tune multiple parameters, and you have to have very complex algorithms for them to work. So if you imagine that a series of mutations altered the limb of one organism, one vertebrate, and turned it into another limb, like the flipper of a whale or the wing of a bat, that would be useless to the organism unless you massively re engineered these sensors and these control systems. And that's not something that can happen through a blind, undirected search. An engineer has to plan the design in advance and carefully set the parameters properly, or it will not work. That's the challenge. And what's interesting, Stuart Burgess talks about this in the paper I wrote about more recently, that if you look at the flipper of the whale or the wing of a bird or the limb of some other organism, that's, let's say a terrestrial animal, they have radically different designs and radically different control systems and sets of sensors, because what you need in the ocean versus flying in the air versus walking on the ground and on all fours is extremely different. So, again, that's a massive feat of engineering that's taking place for every organism.
[00:16:50] Speaker A: Right? And we're getting this out of biology and paleontology, but we're also, it's interesting getting these same implications out of the fields of robotics and biomechanics.
They're studying this as they're trying to build these artificially, and they're coming across these same design constraints. Well, the standard evolutionary model focuses on changes to genes. Has any research evaluated the nature of fitness landscapes based on genetic sequences?
[00:17:22] Speaker B: Yeah, for the last over a decade, people have been asking that very question, and they've been doing computational modeling. They've been doing actual studies of mutations to answer that very question. And here's where the situation becomes really problematic. Because when you think of an engineer trying to change a robotic design to have a new limb, or if you think of an engineer thinking, well, how would I redesign the animal of something like a wolf to turn it into the flipper of a whale? That's a really hard problem. As I talked about, it's extremely difficult, even for engineers to design these properly. But when you're dealing with mutations, mutations are not tools that gradually change an organism at the architectural level, that mutations will create distortions, disruptions.
You're going to get multiple things that can change at once. So it's a very blunt instrument. It'd be like trying to fix a watch with a hammer and chisel. It's just not the right tools. But even worse than that, if you look at the studies of these fitness landscapes based on genetics, what you find is they're extremely rugged. Like, there's a paper I reference in my paper in fitness landscape. It's a 2014 paper by VC and Crom. And what they talk about is how even if you take a small portion of the genome and you look at mutations to that DNA, you find a lot of epistasis, which I talked about before, which means that the fitness landscape is extremely rugged, is what they conclude. So if you looked at, let's say, the landscape for all of the DNA associated with limbs or some other complex trait, you're dealing with an exceptionally rugged landscape. And what these authors concluded, based on this rugged landscape, is one you're going to end up on these local fitness peaks most of the time. So, again, you'll never discover an optimal design. They don't say that explicitly, but it's implied in what they write. But what they explicitly say is you also have to deal with the fact that any path through this landscape inevitably is going to require specific, neutral, and even deleterious mutations. And that's a huge problem, because if you look at the waiting time research of people like Anne Gager and Gunther Bechley and Ola Hofsesser in their papers, what they've shown mathematically, as well as other researchers, is that if you need specific, coordinated, neutral mutations, it takes an enormous amount of time for an organism to achieve that. And what happens is the response to that by many people is, well, maybe you don't really need specific mutations. Maybe any old combination mutations will do. But this research into the genetics or the fitness landscapes based on genetics shows that, no, in fact, because of the ruggedness, any path will require specific, neutral and deleterious mutations. So the waiting times problem becomes enormously problematic in that context.
[00:20:30] Speaker A: Okay, that makes sense. Now, does this discussion relate to arguments made by doctor Steven Meyer related to developmental gene regulatory networks?
[00:20:40] Speaker B: Yeah, it certainly does. Because when you start looking at these networks that control the overall body type, the problem becomes even worse. Because when I talk about these fitness landscapes being rugged, I'm really talking about a region in a fitness landscape around a existing limb. Like, if you have something that's like a mammal forearm, and you make small changes to that forearm, how is it possible to improve it? To what extent can you improve it? And it's very difficult because of the ruggedness of the landscape. But when you talk about organisms in completely different phyla or different classes, like the difference between a fish, an amphibian, and a reptile, now suddenly what happens is it's not a matter of just taking an existing limb and making minor changes. You have to re engineer the entire organism. And that involves these developmental gene regulatory networks, these genes that control the early development of an organism to create its basic architecture, its body plan, its design logic. And what Meyer has argued, based on the best research of leaders in the field, people like Charles Marshall and Eric Davidson, is that mutations that alter these networks are invariably harmful. If they're expressed, if they do something observable, if they alter the architecture, they're always harmful. What that means is that if you look at the overall fitness landscape for all organisms, what you find is there's little regions, which would be those rugged fitness landscapes, but those little regions are separated by massive oceans of non functional intermediates, organisms that could never survive. So what that means is all the problems I talked about are amplified by the difficulty of going from one type of organism to something significantly different, where once you've done that, then you have to traverse these rugged landscapes. So when you combine those together, it represents an absolute death blow to the standard evolutionary model.
[00:22:48] Speaker A: Wow. And I just want to make sure listeners get this as we review some of this and wrap it up. So, two dire challenges to undirected evolutionary models.
So, first, youve got the vast number of suboptimal local peaks in the fitness landscape that precludes any possibility of an evolutionary search ever discovering the perfection of design consistently seen in vertebrates and other taxa. And then second, the constraints on viable paths require a portion of any trajectory along the landscape to include multiple, specific, neutral and harmful mutations. Yet the timescales, they don't add up right. The time scales required for obtaining coordinated, neutral and deleterious mutations are prohibitively long. So there, and I'm reading from your work, obviously, Brian, but there, in a nutshell, listeners are the two dire challenges that this study of evolutionary fitness landscapes poses to the evolutionary framework. Well, how does this bolster the case for design, then, in living things?
[00:23:53] Speaker B: Oh, that's a really terrific question, because design, when you evidence to design, essentially represents evidence that a mind chose an outcome for a purpose. And the idea is that that outcome could never have come about through an undirected process, through chance in natural law. It could only come about because a mind chose an extremely rare outcome for purpose. And what this research shows of Stuart Burgess is that you have what is clear evidence of design, because what you have is you've got things like vertebrate limbs, you've got the hand of a human, you've got the flip of a whale, all of which show perfection of design. It showed that a mind chose an engineering pattern which was perfectly designed for that organism. And as we talked about, an evolutionary search could never have found that perfect design, because it's always going to get stuck on some imperfect design, which is that the, which is at a local fitness peak. So again, the evidence of purpose, the genius of engineering, and the fact that a lot of the engineering seen in life is the same engineering we use, because we know it's the best way to design something, all that points to a mind behind life, okay?
[00:25:20] Speaker A: And that's why studying fitness landscapes can really give you a visual sense of the difficulties of the evolutionary process and the insurmountable challenge of getting to that mount improbable, as Richard Dawkins calls it, that perfection of design. It just does not add up when you look at it closely. Well, Brian, where can listeners go to learn more about this intriguing topic?
[00:25:47] Speaker B: Well, again, I'd strongly encourage readers to get a basic background of the problems we talked about to read Darwin's doubt by Stephen Meyer. But also, I have three recent articles dealing with Stuart Burgess article on how the pattern of vertebrate limbs is optimally designed in general, and each specific limb is perfectly designed. So that's at Evolution News. It's my last three articles, and I will soon have another article published about Stuart's more recent paper, which I interviewed him on in a previous podcast. So that's a great place to gain more information.
[00:26:21] Speaker A: Okay. And we'll include links in the show notes for this episode so that you can tap into the things Brian has just mentioned. Well, Brian, thank you for dropping by, spending some time with us, unpacking fitness landscapes.
[00:26:33] Speaker B: Thank you. It's been a pleasure.
[00:26:35] Speaker A: For id the future, I am Andrew McDermott. Thanks for listening.
Visit us@idthefuture.com and intelligentdesign.org dot this program.
[00:26:46] Speaker B: Is copyright Discovery Institute and recorded by its center for Science and Culture.