[00:00:00] Speaker A: Is we really wanted to emphasize the positive aspects of how we make an argument for design, that we're looking in nature for the kind of information and complexity that in our experience only come from intelligence. And this means that idea is a theory that's based upon our observations of how nature works, what intelligent agents do, what they produce. And we can then go out into nature, study intelligent agents, figure out what they produce, and then go into nature and find the products of intelligent agency. And then that gives us a positive argument for design.
ID the Future, a podcast about evolution and intelligent design.
[00:00:42] Speaker B: Well, you may already know that an intelligent design framework can help us empirically detect whether an outcome in nature exhibits properties that indicate design.
But did you also know that an ID perspective can be applied to experimental science to guide research into how the natural world operates?
Welcome to Idea the Future. I'm your host, Andrew McDermott, and today I'm joined by Dr. Casey Luskin and Dr. Jonathan McClatchy, two members of our center for Science and Culture science team, to discuss a new paper co authored by the science team that unpacks how intelligent design can guide scientific research in multiple fields of science.
Casey, Jonathan, welcome to the podcast.
[00:01:25] Speaker C: Great to be here. Thanks so much for having us.
[00:01:27] Speaker A: Yeah, thanks for accommodating us, Andrew, even though I'm kind of sick with the flu today.
[00:01:32] Speaker B: Oh, absolutely. I'm glad we were able to connect. We were going to do this in studio in our beautiful Studio west studio at our offices in Seattle, but that'll be for another time.
But today maybe you can just touch on what you do at the center for Science and Culture and then tell us about this paper and why you decided to write it. Casey.
[00:01:54] Speaker A: Okay, sure. Yeah. Thanks, Andrew. So my name is Casey Luskin. As ID of the future listeners may know, I'm associate director and research director of the center for Science and Culture at Discovery Institute. And basically I'm the director of our research program. And as folks who listen to the podcast know, Discovery Institute and the ID community in general has a very vibrant and active research program. We're currently supporting over 40 science research projects with over 40 Pisces and we're publishing. You know, over the last five or six years, we published well over 100 peer reviewed Pro ID scientific papers out of the projects that we're supporting. So very exciting time to see the growth of the ID research program.
And I don't do this alone, obviously. It's a big job to manage the research program and I have a lot of help from my colleagues such as Jonathan McClatchy, Emily Reeves and Brian Miller and others as well. And so between our team, we manage all these different projects, and also we're involved directly with doing some research in these projects in some cases. And so what we've been talking about for quite a while is to write a paper that explains to people how you can use intelligent design as a scientific theory to do research, and then also reviews a lot of the research that's being done in the ID research community.
We wanted to write a paper that was almost like a how to manual, how to use id, or how can you use ID to generate predictions, expectations, et cetera, so that you can then use it as a. As a guide or heuristic to guide your own research. So we wrote this paper. The title is the Broad Utility of the Theory of Intelligent Design to Guide Scientific Research. And it's both an explanation of what IDE is, how it works as a scientific theory, and then also a review of actual research projects that are using the methods of intelligent design to guide research.
[00:03:50] Speaker B: Okay, now, Jonathan, tell us briefly what you do at the center for Science and Culture and the parts of this paper that you worked on.
[00:03:57] Speaker C: Sure. So I'm a fellow and resident biologist at the Discovery Institute center for Science and Culture here in Seattle, and I contributed a section in the paper on how a theory might be related to evidence by virtue of primary predictions or retrodictions, and also how to frame the argument for design in Bayesian terms. I also contributed a section to the paper on junk DNA and how intelligent design actually can serve as a heuristic to guide our research into junk DNA.
[00:04:31] Speaker B: Okay, now, Casey, in this paper, you guys lay out a theory of intelligent design. And I really like the way that it's worded. In your paper, can you define ID and tell us how it's different from other ways that Intelligent Design has been defined in the past?
[00:04:47] Speaker A: Sure. So if you go to the abstract of the paper, you can read at the very beginning, it says, intelligent Design, or id, is a scientific theory that uses the methods of historical sciences to infer that many features of nature are best explained by an intelligent cause because they exhibit forms of information and complexity that in our experience, only come from intelligence. And the reason we framed our definition of ID in that way is we really wanted to emphasize the positive aspects of how we make an argument for design, that we're looking in nature for the kind of information and complexity that in our experience, only come from intelligence. And this means that idea is a theory that's based upon our observations of how nature works, what intelligent agents do, what they produce. And we can then go out into nature, study intelligent agents, figure out what they produce, and then go into nature and find the products of intelligent agency. And then that gives us a positive argument for design. And then the next sentence, I think, is also important because we said ID is fundamentally a theory of design detection, but it has also been used as an experimental science to guide research into how the natural world operates in the present day. And this is really part of the story that I think a lot of folks don't yet fully appreciate. Yes, of course, if you followed id, you know that ID is all about, you know, trying to figure out how we can detect whether some natural feature was designed when it first arose in the past, and looking at telltale signs that an intelligent agent was at work, whether that's from information or function or irreducible complexity or whatever it may be. However, what a lot of folks don't realize is, is that we can actually use, and we'll talk about this more, we can use the predictions of ID to understand how natural systems probably will be operating in the present day. So we can understand not just how a system formed in the past, but also how it operates and functions in the present using the theory of intelligent design. And that's really exciting. It shows that idea is a very practical theory. It's helping us to understand both the origins and the operations of, of natural features. And then our next sentence, and I'll leave it with this, we say ID uses a positive argument, beginning its investigations by studying known designed objects and the actions of intelligent agents to identify properties that indicate design, such as complex and specified information, irreducible complexity, purposeful coordination of parts, common design patterns, and functionality.
So that's basically the essence of what we're doing. We're studying. We're saying you can study intelligent agents, figure out what they do, and then go out into nature and see if you can identify these telltale signs that an intelligent agent was at work. And you can do that both to understand the origin of how natural features formed and also how they operate in the present day. And that's what our paper is about, explaining how that method works.
[00:07:48] Speaker B: Yeah, and that's why it's so exciting to have this paper come now and to show people how both historically it has value, but also the cause is now in operation. To use Charles Lyell's old dictum, yeah, it can help us in both ways. And that's truly exciting. Now, Casey, what are the major types of ID research you Guys identify in this paper?
[00:08:11] Speaker A: Sure. I mean, so when you look at philosophy of science and sort of the history of science, there are generally three main types of scientific research.
There's theoretical research, historical scientific research, and experimental scientific research. And ID research spans all three types. So ID theoretical research focuses on articulating the predictions or expectations based upon intelligent design of what we should find in nature, and also developing methods for detecting design.
Then we have ID historical scientific research, and this is probably where we most folks are most familiar with ID research. But that's basically testing those expectations or predictions to detect design in the origin of a natural feature in the past.
And then there's. This is sort of, you know, one of the main things we wanted to talk about in this paper was ID experimental scientific research where you're, again, you're testing those expectations or predictions of intelligent design to understand how natural systems operate in the present. So, yeah, so ID uses all three types of research, and we review how examples of ID research that fit into each of those three categories in the paper.
[00:09:21] Speaker B: And the paper also lays out a methodology for using the scientific method to investigate intelligent design. How does that work? In a nutshell?
[00:09:30] Speaker A: Yeah. So we talk about the scientific method.
And the scientific method, of course, is, you know, you probably learned about it in your high school science class, biology class. It's a very simple method of observation, hypothesis or prediction, experiment and conclusion. And ID uses this is the standard method that, you know, virtually all scientific theories use. And, you know, we can get into the nuances. In some fields, experiment might mean doing actual laboratory experiments at a bench. In other fields, you know, experiment might mean data collection, looking at a telescope and collecting starlight or digging up fossils, or, you know, studying the signal of some, the amplitude of some wave, you know, coming through the ocean or something like that. So there's a lot of ways that you can do data collection or experimentation, but this is the basic method of how scientific theories work.
And so we wanted to lay out how ID uses this exact scientific method to make its claims and to do its research. So basically, ID starts with observations. We observe what intelligent agents do when they work in the natural world, and we also observe objects in the world that we know were designed. And this allows us to effectively develop what we might call design detection criteria. Okay. And so these would be hypotheses, expectations, or predictions that we should expect to find in nature if an intelligent agent was at work. And then we can go out and do the experiment. And so that's like the hypothesis or prediction phase of the Scientific method, then we can do the experiment phase of the scientific method, where we can actually study and test natural objects to see if they contain those features. In many cases, it's informational patterns and forms of complexity, which, in our experience of observations, come only from an intelligent agent. And then, of course, if we do find those telltale signs that an intelligent agent doesn't work, then we can conclude that, you know, design is the best explanation. Now, a key point of the scientific method is that you always hold your conclusions tentatively, subject to future scientific discoveries. So we never want to be dogmatic. You know, in science, you never know what tomorrow's discovery might bring. So you want to always be tentative about your conclusions. But basically ideas using the same scientific methods that, you know, really all scientific theories use. And so what we wanted to show in this paper is not just theoretically how ID uses this. We went through a number of examples and sort of the introductory sections looking at how ID uses the scientific method in biochemistry, in paleontology, in systematics, in genetics, in systems biology, engineering, in a variety of different fields. So we go through this in a number. In physics and cosmology, even, we go through a number of examples of this.
But we wanted to get practical and actually review specific examples of ID research projects. And I know that you're going to talk to Jonathan about some of that today, and also in a. In a separate podcast, you're going to talk to Emily Reeves and Brian Miller about some of the research that they have been involved with or helping to manage that's using this method in other fields as well. So, yeah, that's basically what we want to do. We want to lay out how IDAE uses the scientific method to do research.
One of the benefits of this method is that it is totally empirically based. Okay. If you see the argument we're making for intelligent design, I mean, it is strictly scientific. We're not making any appeals to divine revelation or, you know, faith or religion or anything like that. We are making. We're simply making an appeal based upon the scientific data, the scientific evidence. And it's experimental and it's tentative, so it's always able to be overturned if new scientific discoveries show otherwise. So I think that this shows that ID fits very, very, very squarely. I mean, almost perfectly squarely within the way scientific theories operate.
Yeah. So, Andrew, just to give you and our listeners sort of a very basic appreciation for what this scientific method looks like, let me run through one of the examples that we actually give in the paper for how ID uses the scientific method in biochemistry. So we start with the observation that intelligent agents think with an end goal in mind, allowing them to solve complex problems by taking many parts or symbols and arranging them in intricate patterns that perform a specific function. In other words, intelligent agents generate high CSI and they also produce irreversibly complex machines. So we're then going to make this hypothesis or expectation that finely tuned high CSI structures will be found in biology, including irreducibly complex systems like machines that require multiple parts to function. And then we do our experiment. This is our data collection basically. And research shows that irreducibly complex machines do in fact exist in biology, which they have these irreducible cores that require multiple protein parts and, or subsystems to be present in order for them to function. And then you can also do research on the amino acid sequences of functional proteins and find that they have to be very highly complex and specified in order for them to function. So that is basically finding high CSI in protein sequences. So then we can make this conclusion that high CSI systems and machine like structures are common biology originating through intelligent design. And again, of course, we hold that conclusion tentatively, you know, subject to future discoveries. But that's the basic essence of, of how we can use the scientific method to, to guide our ID research. And there's many other examples that we go through in the paper.
[00:15:03] Speaker B: Yeah, yeah, I have a feeling I'm going to want to send a link to a lot of folks who don't quite understand how ID can be used. This is going to be a very helpful paper for that. Now this brings us to Jonathan McClatchy, because there's a, there's a Bayesian approach that's used in ID that I love ever since I discovered it. And Jonathan really helped me understand that aspect of it. Let's talk about that.
So Jonathan, predictions, retrodictions and expectations, how do they work in science and how does ID fit into that?
[00:15:37] Speaker C: Great question. So there are essentially two broad ways in which a theory might be related to evidence. There are predictions, which is where a theory essentially anticipates a data point or a set of data points before those data points were discovered.
A retrodiction is where a theory explains a data point that was already known when the theory was developed. And then a broader category that is essentially an umbrella term that describes both of those is what we call expectations, which is what a theory leads us to expect given its causal and explanatory power. And so scientific models really succeed or fail primarily by their ability to explain the relevant data and whether evidence was Discovered before or after a model was developed is by itself a fact about the chronology of human inquiry, rather than about the model's causal adequacy.
And so expectation, the term expectation, I think, captures relevant epistemic relationship without making chronology decisive. So this is especially important for historical sciences, where the events being explained often occur long before the theories used to explain them were developed.
So, to take an example, consider a gorge that is formed by river erosion. So in this case, the data point is that the gorge exists and it may have been known for centuries or millennia.
And then geologists put forward a causal model, right? They established that flowing water has a causal capability of eroding rock and carving channels. And so then geologists are able to draw a causal inference. The river erosion model best accounts for or explains the gorge, even though the gorge was known about before the model was formulated.
And so the notice that the explanatory relationship is not weakened merely because the model is, is developed chronologically later than the evidence.
And so intelligent design then argues from empirically grounded knowledge of what intelligent agents can produce, and in ordinary experience, information rich language based codes or language conventions, which is essentially what the genetic code is, are associated with intelligent causation, with conscious rational, deliberative agents. And so that knowledge can then generate expectations about what we might find in biological systems, if indeed the hypothesis is correct that an intelligent agent was causally responsible for the origins of biological systems. And the theory can therefore be explanatory even when the relevant biological feature was discovered before the development of modern Intelligent design theory.
And so in the case of the information rich DNA code, we start with the historical fact, which is that the molecular structure and the coding role of DNA were uncovered in the 1950s and 1960s.
Modern intelligent design theory was essentially developed in its modern form, its contemporary form, decades later, especially from the 1980s.
And so the existence of a code can be treated as evidence relevant to intelligent design, because the existence of a code is less surprising supposing a mind is involved than otherwise. And so its earlier discovery makes a relationship a retrodiction rather than a temporal prediction. And this chronology issue is not unique to intelligent design, right? So when the Origin of Species was first published in 1859, Darwin was largely using his theory to explain observations of biological patterns that were already known time of his writing. And so explaining previously known evidence is a normal feature of scientific theory building. So the key question is whether the proposed causal mechanism provides a satisfactory explanation of the evidence, not simply whether the evidence was unknown beforehand. So that is the distinction between predictions, retrodictions and expectations.
[00:19:59] Speaker B: Okay, very helpful.
Now, you and I have had conversations about Bayes Theorem and how that applies to IDE and how it can be used. Can you tell us just briefly here, how does Bayes theorem inform how we think about expectations in science?
[00:20:14] Speaker C: Sure. So, as a Bayesian, I think about evidence in terms of a likelihood ratio, of the probability of that evidence existing given the hypothesis is true on the numerator, and then on the denominator, you have the probability of that same evidence existing given the hypothesis is false. And to the extent that that likelihood ratio is top heavy, that's the extent to which you have evidence in favor of your hypothesis. If you want to do a deep dive on this, by the way, or a deeper dive on this, I would recommend checking out a podcast I did a couple years ago on ID the Future with Dr. Timothy McGrew, where we talk about Bayesian inference, Bayesian epistemology, and how it relates to Intelligent design in quite a bit more detail. But that's the broad framework. And so, for example, if you take a murder scene or say that you've got a. A trial, the expert witness brings forward the, the murder weapon that was used to commit a homicide and points out that the defendant's fingerprints are on the murder weapon.
Now, that is, that doesn't necessarily prove that the defendant is guilty, but it is evidence for that conclusion because the, the. The defendant's fingerprints being in the murder weapon is rendered more probable or less surprising if he is in fact responsible, if he is in fact the guilty party than it otherwise would be. And so that is essentially how one conceives of evidence as evasion. And so importantly, and this is often overlooked or misunderstood, a datum need not be certain or inevitable or even probable under a hypothesis to provide evidential support for the hypothesis. You can even have a data point that is improbable given the hypothesis. And yet that data point still confers evidential support in favor of the hypothesis, because the data point is more probable supposing the hypothesis is true than if it were false. So if we suppose, for example, the hypothesis renders the data point only 1% probable, supposing the hypothesis is true, it might be astronomically improbable if the hypothesis were false. And so qualitatively, then if an observation is substantially more expected under intelligent causation than under a blind naturalistic scenario, that difference can contribute to a design inference and that argument can be formalized mathematically.
So we employ Bayesian reasoning primarily as a heuristic for comparing explanatory expectations to give an illustration.
Suppose that, and this is an illustration that was developed by my colleague Dr. Timothy McGrew. We discussed it in the previous ID, the future episode that I mentioned earlier.
Imagine that you are hiking and you're far away from roads and civilizations, and you encounter cabin, and you decide to inspect whether this cabin is inhabited. And so you prise open the door cautiously and you encounter a kettle of hot tea. That's a steeping on a stove. So the kettle is still steeping, it's not at room temperature.
And so that is your observation. Now, how does that confer evidential support to the hypothesis that the cabin is in fact inhabited?
Well, if the cabin is inhabited, then it wouldn't be a particularly surprising observation. Sometimes people make tea for themselves. Right, but it's not an especially probable observation. There could be various alternative sets of observations. The cabin might be empty, or it might have coffee instead of tea, or it might have some other beverage. You know, it's apple cider or what have you. So the hypothesis does not render the evidence particularly probable, supposing that the hypothesis is in fact true.
However, if we suppose that hypothesis is false, that renders the observation highly improbable.
And so, in view of the top heaviness of that likelihood ratio, it supports the hypothesis that the cabin is in fact inhabited. And so in this case, the observation is not entailed by the intelligent cause hypothesis. In fact, it's not even rendered especially probable, but it is more readily explained by it. And so to bring this back to intelligent design, then intelligent causation does not entail that every design system must display a particular feature. An intelligent agent has many possible ways to achieve a goal. Design permits a variation in outcomes. In the case of the cabin, the cabin could have contained tea or coffee or no hot drink, or be empty, even if intelligent agent is responsible for the cabin. So the evidential question then is comparative, Right? How expected is the observed feature under one causal hypothesis relative to another?
So that's essentially a brief introduction to a Bayesian epistemology.
[00:25:02] Speaker B: Yeah, great stuff. Well, back to Casey for a few here. What are some of the specific observations and then expectations or predictions that you guys develop in this new paper?
[00:25:15] Speaker A: Yeah, so we wanted the paper to be very practical, to actually directly help people who want to know how they can use ID to guide their own research. So we went through a series of observations which would then lead to expectations or predictions, as Jonathan just explained, naturally, based upon our understanding of what intelligent agents can do. So I'll, yeah, I'll run through these really quickly. But and I hope that in the rest of this podcast and the next one, Andrew, our listeners will be able to see how these observations and expectations or predictions can then be used to guide research in a very practical way.
So first off, very simply, we observe that intelligent agents operate in a goal directed manner. Okay. So that leads us to expect or predict that we will find goal directed operation in natural systems. You might find multiple parts coming together to accomplish some kind of a purpose or a goal.
Another observation is that intelligent agents generate high levels of complex and specified information.
We go through in the paper kind of defining what CSI is and how it works to help us detect design. But for now, just note that that's an observation. Intelligent agents generate high csi. So that leads us to then predict or expect that we will find high CSI in natural systems. And we talk about in the paper how we find high csai in protein sequences, in DNA sequences, in a variety of other contexts.
Intelligent agents can also find rare solutions to complex problems. So this could cash out with a prediction that we will find very rare solutions to complex problems. Think of the laws of nature being fine tuned, having those very, very precise values, very, very rare solutions that present the exact parameters that are needed for life to exist. So I think we can make a good expectation that is very usable in the area of physics and cosmology.
Let's talk about sort of a more detailed observation. Intelligent agents can implement a top down design where multiple parts are coordinated to accomplish a purpose or a goal. We see intelligent agents doing this all the time. And what we mean by this is there's sort of a hierarchical design where sub components are coordinated to contribute to higher functions at each level. So basically you have multiple levels of operation of a system and, and the lower level components are all working together to contribute to goals and higher level functions that contribute towards an overarching goal. And you have multiple parts that are coordinated to do this. Well, we see this all the time in intelligently designed structures. You know, whether it's a car or a submarine or an airplane or you know, a horse and buggy. We see intelligent agents coordinate parts to produce a system that's oriented towards a goal. So this could lead us to expect or predict that we will find systems in nature that are structured with a top down design, with multiple parts coordinated to accomplish a purpose or a goal. With this sort of hierarchical design where subcomponents are coordinated to contribute to higher functions at each level.
Simple one, intelligent agents produce IRREDUCIBLY complex systems like machines. So we're going to predict or expect that we will find irreversibly complex machines in the natural world. Here's an interesting one. Intelligent agents reuse functional components in different systems. Okay? So this is actually a very, very useful observation we can make about what intelligent agents do. We see wheels being reused on cars and airplanes. We see the same computer code being reused in different versions of Windows. We see keyboards being reused on tablets and cell phones and laptops, et cetera, et cetera. So that kind of observation that intelligent agents reuse functional parts in different systems, that could lead us to predict or expect that we will find the reusage of functional components in different systems in the natural world as well. And this turns out to be a very, very useful prediction or expectation.
Another thing when we study what engineers do is that intelligent agents will optimize systems around competing constraints. So I love to use this example because my laptop has a terrible battery, okay? However, it is a very, very powerful laptop, and I love my laptop. It's got a lot of horsepower. But on a plane, if I'm not plugged in, it's not going to last for more than a couple hours or so, max. So my laptop has been optimized around power and computing power, basically, and energy. It's not optimized around energy usage. It's actually optimized around using up energy, so that way it can give you more computing power. So intelligent agents will often optimize competing constraints, so we can actually predict or expect that we will find performances optimized around competing constraints in the natural world.
Another, this may seem obvious, but consider this. When intelligent agents make things, they produce components or they include components with a function or a purpose, okay? When I make something, it's going to have a reason for being there. Now, it could be an aesthetic purpose, but there's going to be a purpose for it being there. You know, even my, my masonry jar here that I drink water out of, you know, it's got a purpose of holding water so I can drink it. All, right? So we can predict that in the natural world, if a system was designed, components will have a function and a purpose. Or at least originally they have function and a purpose. Of course, we can see that sometimes functions and purposes can be degraded over time.
And then lastly, I like this one because from my geology background, I've studied some paleontology. And this observation says intelligent agents can rapidly generate large amounts of new information, including generating new systems which are fully formed and different from previous systems. So if this is true, then we could predict that we could see the rapid appearance of new information. And in the natural world, where systems are, are basically appearing fully formed and designed to be functional from the outset, showing some degree of discontinuity from other preexisting systems. So this is what we observe intelligent agents can do. They can rapidly generate new systems that have, you know, a new design, there's sort of a new blueprint that's been conceived, and then we introduce them fully formed in the natural world, ready to operate. Well, could this be cashed out, say, in the fossil record, when we see new types of life forms appearing abruptly without any clear or direct evolutionary precursors, basically seeing new information explosions in the history of life. So there's a lot of useful expectations and predictions that we go through in our paper and there might be more that we missed. I'm sure there are. What we want to do is we don't want to just give people fish, we want to teach them to fish. This is how intelligent design works. You observe what intelligent agents can do. You use those observations to generate expectations or predictions of what you should find in the natural world, and then go out and use that method to guide your own research. And we think it's been a pretty fruitful method in the research program that we're supporting in the ID community and we want more people to use it. So that's why we wrote the paper. Now I think Jonathan will talk about a specific example, which is a really cool one.
[00:32:22] Speaker B: Yeah, yeah. I was going to turn to Jonathan and ask about junk DNA. You know, perhaps one of the clearest expectations or predictions developed in this paper is regarding junk DNA. So, Jonathan, explain how ID uses the scientific method to do research and discuss how this relates to the subject of junk DNA.
[00:32:42] Speaker C: Sure, absolutely. So there are two competing paradigms that purport to account for life's origins and development on Earth. Those are the evolutionary view and the Intelligent Design hypothesis.
And both of those computing explanations or computing hypotheses put forward different sets of expectations of what we might find in life.
So on the evolutionary hypothesis, junk DNA, our chromosomes are replete with nonsensical sequences is essentially unsurprising trial and error. And neutral accumulation can leave genomic debris and historically large portions of a million DNA were predicted to be non functional.
Whereas on the Intelligent Design hypothesis, conversely, we would expect that function should be the norm rather than junk being the norm.
And that derives from our observation of what intelligent agents typically do. Right. Intelligent agents produce systems with function or purpose. And so if genomes are designed, then widespread genomic function is a natural expectation that falls out of that.
And that prediction then motivates us to look for and to continue to inquire into functions for supposed junk DNA, rather than presuming the absence of such functions, simply because we don't yet know what a certain genomic element is doing.
And so that is essentially how intelligent design, I think, has heuristic power when it comes to encouraging further scientific research into functions for genetic elements that have been hitherto considered to be junk.
[00:34:45] Speaker B: Yeah, and you know, critics of ID sometimes allege that junk DNA was never predicted by evolutionary theorists. Is this true?
[00:34:53] Speaker C: No, this is simply revisionist history. In fact, we discussed this in the paper in some detail. We give a lot of quotes from various authors who did predict that much of our genome was junk. So, for example, in 1969, King and Jules contended that 99% of mammalian DNA is not true genetic material. In 1972, Ono predicted at least 90% of mammalian DNA could be nonsense. In 1980, Doolittle and Sapienza said that natural selection operating within genomes will inevitably result in the appearance of DNA with no phenotypic expression whose only function is survival within genomes.
In 1980, Orgel and Crick assumed that much DNA in higher organisms is little better than junk and noted that the idea of selfish DNA is firmly based based on the general theory of natural selection. And in 2009, Dawkins asserted that the greater part, 95% in the case of humans of the genome might as well not be there for all the difference it makes.
[00:35:51] Speaker B: What has research over the past few decades indicated about whether junk DNA is functional?
[00:35:56] Speaker C: So research over the past few decades has revealed that far from being genetic junk, the genome has more and more of these non functional or supposed non functional sequences have turned out, upon further investigation, to be. To be functional. ENCODE has revealed that more than 80% of our genome is chemically active or expressed. But that doesn't necessarily entail function, but it is certainly suggestive evidence or preliminary evidence of function. We've also identified specific functions for many instances of pseudogenes or endogenous retroviral like sequences, or repetitive sequences like lines and sines and so on. And so it's. It's premature, I would contend, to insist that a particular genomic element has to be junk simply because we don't yet know what it's doing. And in fact, as science has advanced, that junk DNA argument has had to retreat. And so it's Essentially, we've heard of the God of the gaps argument. Well, this is in fact a Darwin of the gaps argument where Darwinism hinders further scientific investigation. Whereas Intelligent Design encourages us to continue investigating because Darwin leads to the expectation that much of our genome will be the accumulation of genomic debris that is non functional or doesn't contribute to the fitness of the organism. Whereas Intelligent Design leads us to expect that in fact, junk, genuine junk, will be the exception rather than the norm.
[00:37:32] Speaker B: Okay, and this really speaks to the, what's known as the heuristic value of Intelligent Design, doesn't it? Can you tell us how the case of junk DNA sort of captures how valuable an ID framework can be?
[00:37:46] Speaker C: Yeah, absolutely. So as we said earlier, Intelligent Design really implies purpose. Right. Intelligent agents characteristically produce things for reasons for purposes. Right. They produce functional systems.
And so we therefore this leads us to then expect that we will find function. If genomes are designed, then a natural expectation that falls out of that is, that is widespread functionality.
And this then allows us to ask productive questions. We can search for functions rather than presuming non function.
And we can thereby conduct research which reveals regulatory, developmental, immune and structural functions for genetic elements that had hitherto being considered non functional.
[00:38:40] Speaker B: Yeah. Now this really sets us up for another discussion I'm going to have with Dr. Emily Reeves and Dr. Brian Miller. They're going to explain how an ID framework can be used to, you know, basically analyze nature and things that are in operation in nature biological systems, namely how we can study that by using principles within an ID framework. So that's what's next. Now, Casey, where can our audience find this paper? This is one that we're going to want to bookmark, read and share widely.
[00:39:15] Speaker A: Well, the paper was published in the journal Biocomplexity, so you can go to biodash complexity.org and download it. We've also been writing about it on our website, Science and Culture today.
It's an open access paper, so it's free to download.
[00:39:30] Speaker B: Okay. Completely free. So no, no cost. Sometimes those papers, you know, you charge 20 or 30 bucks just to get them.
[00:39:37] Speaker A: But Bio Complexity is open access, so we were very glad to have it published there.
[00:39:41] Speaker C: Yeah.
[00:39:42] Speaker B: Okay, excellent. Well, Casey, Jonathan, thanks for your time today.
[00:39:45] Speaker C: Thank you. Great to be here.
[00:39:46] Speaker B: And audience, if you enjoyed today's episode, please consider leaving a positive review and rating at Apple Podcasts that helps others find the show and learn about the evidence for intelligent design in nature. Until next time, I'm Andrew McDermott. Thanks for joining us.
[00:40:03] Speaker A: Visit
[email protected] and intelligent design.org this program is copyright Discovery Institute and recorded by
[00:40:11] Speaker C: its center for Science and Culture.
