Speaker 1 00:00:05 ID the future, a podcast about evolution and intelligent design.
Speaker 2 00:00:12 Hello and welcome to ID the Future. I'm Casey Luskin with Discovery Institute's Center for Science and Culture, and we have on the show with us today a second podcast with Dr. Brian Miller, who is research coordinator for the Center for Science and Culture at Discovery Institute. Dr. Miller has a bachelor's degree in physics with a minor in engineering from m i t and a PhD in physics from Duke University. He's contributed to multiple books and journals covering the debate over intelligent design. And his most recent is a chapter in the book titled Science and Faith in Dialogue published by the South African Publisher asis. It is available open access online. We'll put a link to it in the podcast description. And Dr. Miller's chapter is titled, engineering Principles Explained Biological Systems Better Than Evolutionary Theory. And he's come back on the show with us today to continue discussing this chapter in this book. So Dr. Miller, thanks for coming back on the show.
Speaker 3 00:01:03 It's a pleasure to be back.
Speaker 2 00:01:05 So in your book chapter, you talk about some examples of engineering or, or what you believe is engineering and biology, particularly the ability of organisms to track and respond to the environment. So from an engineering standpoint, what is needed, theoretically speaking, foreign organism to be able to track the environment. And then I'd like to talk about some examples from biology where you see these theoretical predictions of your engineering paradigm existing.
Speaker 3 00:01:31 Uh, certainly. And the whole idea of a tracking model is that when organisms adapt to the environment, it's not entirely random mutations in natural selection and other evolutionary processes that are random and undirected. But what you have is the organism has sensors, people often call that receptors, but sensors is a better term, monitoring what's happening in the environment like the temperature, the pressure, the existence of predators. Now what happens is there's logic-based analyzers. So when the sensors send the information to these analyzers, if certain conditions are met that are predefined, then what happens is the logic-based analyzers will send a signal or a message for some mechanism to initiate a process that alters the organism in specific ways. And that could be changing its body structure, its its height, the number of vertebrae, or it might be even directing genetic changes. And what happened is, I first heard about this from others, people like Randy Za, and I was honestly a bit skeptical at first cuz it just seemed a little bit too good to be true. So what I did is I investigated the latest research on model organisms, and I find that it is in fact the case that a revolution is taking place in biology, that people are realizing the adaptation, even small scale adaptation is not primarily random and undirected, but it's from these pre-engineered responses to environmental conditions.
Speaker 2 00:02:53 You talk in your chapter about the idea of natural genetic engineering. Is this a form of blind evolution or does it represent a form of evolution that is in some sense programmed along the lines of what you're talking about?
Speaker 3 00:03:05 Yeah, and and this is a great example of one of the primary mechanisms in these sort of tracking models of adaptation. And it was really pioneered and pushed forward by a central figure like Jim Shapiro with the help of others. And the idea of natural genetic engineering or nge for short is that mutations are not entirely random. Genetic changes are not entirely random. You've got mechanisms that direct certain changes to happen much more frequently or only when certain conditions are met. So here again, you've got organisms that monitor the environment when certain conditions are met. These systems will cause maybe certain locations in the genome to mutate much more often or it'll cause things like what are called transposable elements, which are elements in D n A that go from one location to another to initiate changes. And um, some of the pioneering research was done on maze. And you find during certain conditions like drought, these transposable elements initiate, um, more and more evidence is that they're often targeted to a certain degree, depends on the, the transposable element. And that causes changes that allow the organisms to better survive in these changing environments.
Speaker 2 00:04:18 You also discuss phenotypic plasticity, and I found this really intriguing because again, it suggests that some of these supposed examples that are cited to us of the power of Darwinian evolution are actually a form of pre-programmed change. So first of all, Dr. Miller, can you explain to us what is phenotypic plasticity? And second, can you give some examples of these kinds of changes and why they really aren't showing neo Darwinian or blind evolutionary mechanisms at work?
Speaker 3 00:04:45 Uh, certainly, uh, phenotypic plasticity simply means pheno. Phenotypic means is referring to the phenotype or basically what you see the appearance, the organization, the physiology of a, um, organism. So it's not the genetics, it's what actually the genetics produces and other things produce plasticity just means it's flexible. When organisms encounter certain conditions, either externally or internally, they direct changes that take place in their bodies. So their bodies like cly fish can become more elongated, they can change color. The expression of genes can change that allows 'em to adapt to the environment. And these changes, again, are not random. They're not from mutations that are undirected, they're not happenstance, but they're pre-programmed. It's again, this tracking model and there's many amazing examples. And let me just give you a few examples. I I briefly mentioned cylis. You'll find that when cylis go into a certain lake, you might have one set of C lids that goes to one lake and then over 10 million years, lots or several million years, several changes take place.
Speaker 3 00:05:49 And then when another group of CDs go to a different lake over maybe 10,000 years, you see the same changes, the same variation. It's not random, it's pre-programmed. So fish can change the, the shape of their jaws, they can change the, the fin ray number. They can even change the expression of genes, like genes that control vision. If the light conditions change. And you have other examples like sticklebacks, this is another model organism, a different type of, of fish. And they also can, let's say, change their coloration in certain conditions. They can change the, their structure, their shape and so forth. And it's not even just the fact that it's phenotypic plasticity, but you also have natural genetic engineering. So in ccls what'll happen is you'll actually have mechanisms that'll cause transposable elements to move that'll alter the genetics of vision. It was either that or stickle back, I believe it was cylis.
Speaker 3 00:06:39 So again, what you're finding in countless organisms is that the changes, the adaptation is not random, but it's pre-programmed either through phenotypic plasticity or with genetic modifications through natural genetic engineering. Now, there are examples of random mutations that do benefit an organism like sickle cell anemia, but as Mike Behe wrote in his book, Darwin Devolves that almost every example where it truly is random mutations, it either degrades the genes, you're losing information or it's some trivial change like one amino acid change that changes a gene provision. So again, what you're seeing over and over is that even microevolution points to design and engineering.
Speaker 2 00:07:19 Yeah, Dr. Miller, I remember I had to write a paper on Cyclins for an evolutionary biology course when I was in college and I was struck by how you saw the same morphologies appearing over and over again. And it seems exactly like what you're talking about, the sort of pre-programmed phenotypic plasticity that arises quite easily and quite rapidly to help organisms adapt. Of course, this was before I think this sort of third way evolution concept of phenotypic plasticity was really in vogue. But, but you could see that, you know, this sort of thing was happening. So let's talk about systematics, uh, the relationships of living organisms. Can an engineering model of life also be applied to help us understand systematics?
Speaker 3 00:07:57 Oh, absolutely. Because in systematics you're looking at similarities between different organisms and asking how those similarities relate to how closely they're related. And from an evolutionary perspective on an evolutionary tree. And the logic of evolution is that if two organisms are more closely related, they have a a more recent common ancestor, then you'd expect everything about them to be more similar and they should both be much different from something that's far apart for them On the evolutionary tree, the problem is people have found over and over again that that's not the case. That you might have very closely related organisms that are very different even in the way they reproduce. And you might have organisms that are very distantly related, but they might have striking similarities like the eyes of humans of vertebrates and things like octopi are very, very similar. And that's not because we share eight legged ancestors, but evolutions believe it's because they evolved independently to look really, really similar.
Speaker 3 00:08:57 The problem is these inconsistencies are so common that the whole assumption that similarity implies common ancestry is clearly false. It's just incredibly inconsistent. But from an engineering perspective, it makes perfect sense. Engineers use what are called modules or design motifs over and over again because they're useful. So eyes are really good for seeing and you see eyes in very distantly related organisms because from an engineering perspective that just makes sense. So again, what you find is this engineering perspective explains the similarities in differences much, much better. And why is there kind of a hierarchy in life to some extent, like mammals have a lot of the same features? Well that's because engineers typically use a design motif. So if you look at cars and buses, you'll find the same basic design logic because that's an engineering motif that's used over and over again in engineering, TIFs kind of fit with and son of a hierarchy.
Speaker 3 00:09:58 So again, this is a beautiful example of how engineering explains similarities. Now, one of my favorite examples is there's something called perfect robust adaptation. And that's the idea that organisms can adapt to environmental changes over a wide range of inputs, um, like either light conditions or sound conditions over many orders of magnitude. And there's a beautiful paper that talks about the topology of perfect rob robust adaptation. And what you find is the same design logic employed everywhere in nature from neurons to chemical interactions. And there's a very specific design logic that will only two examples of design logics that actually work to do this that they found. So again, here's an example of how systematics fits within an engineering perspective.
Speaker 2 00:10:41 Yes, I think everything you said is just exactly right, Dr. Miller. The idea that engineers will reuse parts that work and different designs, this idea of common design I think is exactly what we'd expect from an engineering paradigm of life. But as you correctly said, that doesn't mean that we'll find no sort of tree-like distribution of traits because traits often correlate with each other. I give this example sometimes I once did a survey of shirts in my closet and I was trying to actually see if I could construct a phylogenetic tree of shirts in my closet that had some sort of a, a tree-like data structure to it. And what I found was that shirts that had a collar often also had buttons. Okay. So there's a correlation right there where these true traits kind of often nested together. That's okay. That doesn't mean that the shirts were not designed, that doesn't mean that the shirts were produced by an unguided process of some, you know, natural mechanism design processes can produce some kind of a tree-like structure.
Speaker 2 00:11:40 But when you have common design, you'll often get oddballs. Well, you'll get, you know, a shirt without the collar with the buttons or a shirt with the collar, but no buttons. You know, you'll get variations in mosaics on a theme, even though there is often some common, you might say, correlations between traits. So anyway, I think that everything you said is exactly right and I'm excited to see where this engineering paradigm of biology helps us to better understand the relationship between organisms with this gets into Winston UT's idea of the dependency graph, which we can talk about some other time. So at the end of your chapter in this book, you provide a couple of case studies that I'd like to ask you about. In fact, I think we discussed one of them, the bacterial phum in the previous podcast, but you also talk about the minimal requirements for a cell and you write engineering analyses elucidate in even the simplest of cells, much of the underlying architecture in the clear top down design logic. Can you unpack this argument for us and what it means for biology?
Speaker 3 00:12:38 Yeah, and this is one of my favorite projects, and I'll be writing a paper on this over the next several months. And one of the biggest challenges for biologists to explain is self replication. All attempts to explain self replicating molecules like the r n world completely collapse under close inspection for reasons I've gone into in my articles and other podcasts. So how does self replication work? Well, there was a, a famous, uh, theoretician mathematician named Van Noman that asked the question, what are the minimal components required for self replication? How will this work at the most basic level? And then other, uh, people like NASA have looked at what are the minimal requirements of things like self replicating space probes? Others have looked at self replicating robots, robots that could reproduce themselves. And what they found is that there's a minimal set of components that appear over and over and over again.
Speaker 3 00:13:27 There's a certain design logic that has to be met. Now what I did and others have done is compared that design logic to studies of minimally complex cells, like what's, how simple can a cell get before it can no longer function before it'll just break apart and turn back into simple chemicals. Uh, people like ventor, that, that was a classic paper on that and is designed of a minimally complex cell. And what do you find is anything that even remotely resembles a cell? Must have several key components, has to have the mach machinery for energy production, delivery information repositories and processors, selective gateways with active transport sensors coupled to signal transduction pathways and signal processing mechanisms to implement in instructions, manufacturing, auto assembly processes, automated repair machinery, air correction systems, waste disposal and recycling methods and control systems that are capable of global coordination. You can't get simpler than than that. If any of these mechanisms aren't there, a cell would break apart and turn to simple chemicals. So again, what you're seeing is such clear evidence of design that again, to deny it, you have to actively suppress the truth. But this is a great template because in any study of, let's imagine we study, we find some completely new organism deep in the cave, we can use this as a template to predict what the minimal components will be and how they will interact. So it's a very powerful paradigm.
Speaker 2 00:14:50 So Dr. Miller, where do you see this engineering approach to biology going in the future? And do you see this paradigm growing and bearing fruit? Do you see it attracting scientists, biologists, engineers to use this kind of an approach? Where do you see it going?
Speaker 3 00:15:04 Well, it's, it's really exciting because what has happened in the past historically is there's been some conflict between the in intelligent design research community and mainstream biologists. Because we're starting from such different premises, they assume life has no teleology, it's clumsy, it's suboptimal very often, and it doesn't really look like human engineering. And we assume the opposite. But now as mainstream biologists are coming into a design-based perspective, even if they deny design philosophically, and they may simply put a Darwin stamp on it at the end of the day, and they're welcome to do that, but we are now able to collaborate with mainstream biologists because we are really on the same page. And some of we have, as our engineering research group, we have biologists who collaborate very nicely with other researchers who are not necessarily on the same page philosophically. So that's one thing you'll see in the future is this continued cooperation e even if it's more behind the scenes.
Speaker 3 00:16:00 Also what you're finding is that more and more biologists are using this engineering framework, either consciously or unconsciously. So I see this as the future biology. It's really, we're in the earliest stages of the next great scientific revolution. So what is it gonna look like in the future? Well, we at the engineering research group are thinking very deeply about that. How is biology gonna be reformulated over the next century? We'll talk about that at the Next Cells conference. And basically more and more we're gonna be finding the same design motifs used in in nature over and over again. We're gonna be able to use those design motifs and that underlying logic to predict what we'll find in organisms. So in the same way Dean Schultz did that with the bacterial fige, I think we're gonna see that over and over again. People look at a biological system, understand the top-down overarching design logic, predict how the different components and sub-components will work together. Look at common engineering motifs we use and see how that maps onto biology. What's even more exciting is that because biology is produced by a mind far greater than ourself, it uses a logic and methods that are beyond our comprehension, but many are within our comprehension. So the more we study biology, the more we can borrow from biology and improve human engineering. And again, Stuart Burgess has been doing that already very, very well. So that's the future of biology.
Speaker 2 00:17:23 Well, Dr. Miller, you have really been on the forefront of trying to help biologists and engineers work together so they can better understand what is going on in biology, both with Discovery Institute's Engineering Research group, which has multiple projects that you're helping to oversee. And also with this fantastic paper in the new book, science and Faith and Dialogue. And again, your chapter titled, engineering Principles Explain Biological Systems better than Evolutionary Theory. It's open access, it's free for anyone to download. I'll encourage them to check out the description of this podcast where they can download the book for free and read your chapter to understand this exciting new way of understanding biology.
Speaker 3 00:18:01 Yes, absolutely. It's been such a pleasure to be here. And again, I just covered a lot of material, but in that chapter, our references to the technical literature, so I encourage all of you to look at the technical papers to see that what I'm saying is actually true and accurate.
Speaker 2 00:18:15 Well, thanks so much Dr. Miller, for your work and for sharing with us. I'm Casey Luskin with ID The Future. Thanks for listening.
Speaker 1 00:18:23 Visit
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