Speaker 1 00:00:05 ID the future, a podcast about evolution and intelligent design.
Speaker 2 00:00:12 In the debate over evolution and intelligent design. We often talk a lot about Charles Darwin or Jacque Mano, or the Huxley's or other key figures of the past. But what is going on right now? Actual research relating to evolution and intelligent design. Hello, I'm Eric Anderson, and today on our show we have Dr. Ann Gaer to talk about some of her recent and current work. Gauger is a senior fellow with Discovery Institute's Center for Science and Culture, and is published papers on the waiting time problem and the limits of protein evolution. She holds degrees in biology, zoology, and has carried out postdoc work in molecular biology at Harvard. Welcome Anne.
Speaker 3 00:00:47 Hi, Eric. It's good to be here.
Speaker 2 00:00:48 So Anne, we had you on the show not too long ago to talk about your background and experiences and why it matters to you to keep following the evidence where it leads even in the face of challenges. Again, appreciate you sharing those experiences. It was both interesting and inspiring to me, and I certainly encourage any listeners who didn't get to hear that conversation to check it out.
Speaker 3 00:01:07 Yeah, I appreciated being on the show and I'm glad that get to share what work has been done. Thanks.
Speaker 2 00:01:14 So today I'd like to piggyback on your experiences, but have you talk about your recent and current scientific work you're pursuing. First, tell us about your work with proteins.
Speaker 3 00:01:23 So, Doug Ax published a famous paper in 2004 where he demonstrated that the, um, rarity of a specific protein fold in sequence space was on the order of one in 10 to the 77th, which is one with, uh, 77 zeros after it. Now that's, um, huge rarity <laugh>. Yeah. But I should say very, very, very, very small. But that was one specific protein, and there was another qu question that needed to be addressed. His work said that proteins were very protein folds, specific protein folds were very rare in sequence space, but what we needed to ask next was how hard it was to get from one protein to another. There are lots of different kinds of protein folds, and some of them look like they're related. And the question is, or even within one family of protein folds, there can be lots of proteins that carry out different functions.
Speaker 3 00:02:18 The question is, how hard is it to go from one protein activity to another, even with the same fold? And so we set out to look for a family of proteins that would work. Then identified a pair of proteins that were very closely related in sequence space, but that had different functions. And we tried to mutate one protein, call it protein A, to perform the function of the related protein. Protein B, we found that it took too many mutations to accomplish the change. We never were successful in mutating the protein A, to perform the function of protein B, despite the fact that their backbones aligned very closely. They shared the same amino acids in the active site that carried out the chemistry of the reactions. And we converted the entire active site of A to look like b. It just too hard to make that conversion took too many mutations, even though the proteins were so closely related in structure.
Speaker 3 00:03:22 Another part of this, the difficulty of the story is when people do research in this area, they typically are growing bacteria in culture. They're making their mu mutations and testing in culture. So there are lots and lots and lots of bacteria making lots of protein. They put them in plasmids that overexpress the protein, and then it's not a natural situation because in an evolutionary scenario, say there was one gene not thousands of copies of the gene, and that one gene has to mutate. And when it's mutated, it only expresses a certain amount of protein, not an overwhelming amount, just a small amount. And that small amount of protein has to be sufficient to promote the change that they're seeking. But because it's done in vitro in the lab, they have tons of protein and tons of protein that work very poorly can generate the activity they're looking for. If they express it in a reasonable amount that would be occurring in nature, it's not enough to be detected. So it wouldn't do any good for the bacterium in the wild is what I'm saying, even if it got the mutation.
Speaker 2 00:04:35 But they're typically also assuming that there has been a copy made. Right. Because otherwise you also have the added problem that the original protein is once it's mutating, is not able to properly perform. The original functions aren't, aren't they typically assuming that you
Speaker 3 00:04:49 Yeah. At least one copy, but the one copy is what has to actually be detected. Mm-hmm. <affirmative>. So there will be a wildtype copy there, but it's not gonna be generating anything new. It's the mutating copy that has to generate a protein that can be detected just
Speaker 2 00:05:04 To kind of bring this, you know, to home. For the rest of us who aren't microbiologists or molecular biologists, you can make a story that sounds kind of good, that says, well, you have a copy of a, of a gene and it's gonna be tweaked over time and it's gonna do something new and isn't that wonderful. But when you actually start looking at the real data and the real numbers is kind of what I am getting from, from your work and Doug's work, that it just doesn't hold up. The story just doesn't hold up. Well,
Speaker 3 00:05:31 The problem is that the way that it's done in the lab is not realistic mm-hmm. <affirmative>. And so they may be able to generate new function using lab experiments, but it's not a realistic test. And so it doesn't demonstrate that it would work in the wild. And that's the key thing. So, uh, what happened was Ralph Silky contacted us. He had been doing work, long-term work looking for a gene to change in bacterium that had plasmid that carried two genes from the tryptophan that makes tryptophan for the cell. Tryptophan is amino acid, andron is a stretch of dna n a with a sequence of genes all involved in similar functions and co-expressed. Now I just said a bunch of technical stuff,
Speaker 2 00:06:18 So
Speaker 3 00:06:19 If you wanna ask about it.
Speaker 2 00:06:21 Carry, carry, carry on. Go ahead, <laugh>.
Speaker 3 00:06:24 Okay. So he had these genes on a plasmid that there were a lot of plasmids in any cell. So thousands of PLAs in his cell, and he grew large, large amounts of bacteria in culture waiting for the, okay. One other piece, one of the genes was, was good and the other gene had had two mutations on it. Mm-hmm.
Speaker 2 00:06:48 <affirmative> and purposely, purposely introduced, you mean
Speaker 3 00:06:50 Purposely introduced? Yeah. And together the, either one of them was supposed to be, uh, complete bl knockout, eliminate function in that gene completely mm-hmm. <affirmative>, well, it didn't work, and he wasn't getting any genes to change. So he contacted us and we discovered that the problem was that the bacteria were expressing this bad protein at such high levels that any mutation that knocked out the gene function, in other words not restored, but knock out function to eliminate the gene, was an advantage. So he couldn't get reversion of the gene to wild type because the vast majority of the cells had mutations that eliminated the gene. Yeah. Deleted it, broke it all kinds of different ways. So the problem is, if it's easier to, okay, you're carrying this gene, it's not working, it's broken, it's useless junk, and you wanna improve your chances of growing faster than everybody else, what's the easiest thing to get rid of this broken gene?
Speaker 3 00:07:56 Which means the cells never could recover the wild type function, which is what he had been asking in the first place. So what does that say to us as a, as a general rule? Well, if the cost of expressing a gene is real mm-hmm. <affirmative>, and if you're carrying a broken gene or a gene that has no function but is still being expressed, there's a cost to expressing it, and that cost may be sufficient to further mutate the gene such that it's not expressed, and then that eliminates the chance of that punitive say, uh, neogen when it's on the way to being functional.
Speaker 2 00:08:35 Yeah. And and just to clarify, normally wouldn't be a mutation in the sequence itself that would cause that it would be either deletion of the gene or it would be changing the operation so it doesn't initiate the transcription, those kinds of things. Right. Is typically what it would be.
Speaker 3 00:08:52 Yeah. Tho those things. But also it can be mutations within the gene itself. Um,
Speaker 2 00:08:57 Okay. And that would cause it to not be expressed.
Speaker 3 00:08:59 I see your point. It would have to be, uh, mutations in the, um, the promoter for Theron or something like that.
Speaker 2 00:09:07 Right, right. Okay. Yeah. So this is, this reminds us a lot of course of Mike bi. He's statement that he calls the rule of adaptive evolution. That Right. It's, it's easier to break something than, than to produce something new. And if that's beneficial in the particular situation, then that's what's gonna happen. Yep.
Speaker 3 00:09:24 So the obstacles to evolution that we've identified are the cost of expression and the fact that it takes too many mutations most of the time to get what you need. And if you add in the fact that it, getting it by blind search makes it even harder.
Speaker 2 00:09:40 Mm-hmm. <affirmative>. Okay. So Ralph comes to you and says, Hey, I'm having trouble getting this, uh, reversion, I've, I've made two mutations in this gene that I know is a good gene. Otherwise, it's just got two things it needs to find to become a good gene, but it's not working because it's easier to just get rid of it. And then what, what did you guys start looking at?
Speaker 3 00:09:59 We sequenced different isolates from his library of genes. So he, he froze down the populations of cells that he, um, was working with and then sent us individual cloned samples from that library of gene.
Speaker 2 00:10:13 So you kind of get a snapshot of the history of what had happened, right?
Speaker 3 00:10:16 Mm-hmm. <affirmative> out of 15 samples, I think we found that all of them were breaking the gene in some way. Mm-hmm. <affirmative>.
Speaker 2 00:10:24 Yeah. That's incredibly interesting. So this also is related, isn't it, somewhat to another big project you've been working on, which is the waiting times problem. Right.
Speaker 3 00:10:33 So the waiting times problem means how long will it take to get a gene that is adaptive that will work? And if you have to have say, five separate mutations, how long will that take in a population? I, um, met up with a scientist named Ola Huster. So Ola Huster is a Swedish professor of mathematics and well-respected. I contacted him about other problems, which we'll probably talk about in a bit, but he also got interested in the waiting times problem together with me and Gunther Beckley, who's a German scientist, paleontologist. We got together and asked, is there a system where we can look to identify mutations that might produce a particular structure in an organism in the course of evolution, estimate how many mutations it would take, and then create a model for the likelihood of each mutation happening and the waiting time to get all of them together based on what we know about protein evolution. It's, uh, a difficult question that a lot of people have tried to answer, and they've used different models. The answer has been uniformly that it takes too much time unless there is a, um, trick involved in the math in which you are sneaking in some sort of way to rescue the situation. For example, there was a model where they had created a scenario for the evolution of a gene, but neglected the fact that mutations do happen that move you in a forward direction, but then there are backward mutations too. True. So, um,
Speaker 2 00:12:17 Yeah, you don't get to just have, uh, you know, rich Richard Duncan's weasel situation where any beneficial thing gets set aside and carefully preserved Yep. While you look for the next one.
Speaker 3 00:12:28 You, you don't get to put it in the bank.
Speaker 2 00:12:30 Yeah. Yeah. So when you say that a lot of people have looked at this, is that something that's happened over a long period of time or has it been more recently? Uh,
Speaker 3 00:12:38 It's been more recently. The, the flurry of work has been more recent.
Speaker 2 00:12:42 And why is that? I
Speaker 3 00:12:44 Would suspect it's because of the challenge of Id <laugh>.
Speaker 2 00:12:47 That's exactly, that's exactly my impression, because frankly, Anne, I feel like, and maybe I'm, you know, I don't wanna give credit where it's not due, but I feel like the work that you and Doug and others have done, and, and Mike has raised these issues to the point where people have actually started to pay attention because the question, can a protein turn into a different protein? Can a mutation, uh, a series of mutations turn a land mammal into a will? That is not a question that was widely asked in the evolutionary community because it's simply assumed that it could. Yeah.
Speaker 3 00:13:22 Well, they had,
Speaker 2 00:13:23 It's a matter of faith. Yeah.
Speaker 3 00:13:24 There were models of how long you would have to wait to get a particular mutation, but it didn't ask the combinatorial question. Mm-hmm. <affirmative> was how long it would take to get a series of mutations that needed to co-occur. Now by co-occur, I don't mean the mutations happen at the same time. Yeah. What I mean is they eventually have to end up in the same organism
Speaker 2 00:13:45 Right.
Speaker 3 00:13:47 Together in order for the combination of mutations to promote the new function.
Speaker 2 00:13:51 Yeah. And just for those who are new to the area, uh, we're talking about a population of organisms, a particular mutation that arises in one of the organisms, which then eventually has to spread throughout the population. And then if you're talking about coordinated mutations, there's gonna be another one that has to also arise. And so, like you said, it doesn't have to be at the same moment, but eventually it's gotta show up in the same organism.
Speaker 3 00:14:14 So there are, in mammals, for example, there are, there's sexual reproduction. And with sexual reproduction, there's always a recombination of the genes mm-hmm. <affirmative> present in the mother and the father. So that's how you get mutations spliced together. So they're in the same organism or, or they come in it, it has to eventually end up in the same gene, in the same protein, or in the same, uh, regulatory region, so that, uh, you end up getting the product that works. Yeah. And that's, that's the question that matters is how long does it take to get these mutations assembled?
Speaker 2 00:14:52 Right. Right. And sex. And with sexual reproduction, you also have the possibility of the mutation being weeded out.
Speaker 3 00:14:57 That's also true. On average, uh, the estimate is a mutation has to arise 12 times before it succeeds in spreading through the population. Wow.
Speaker 3 00:15:08 The numbers are rather difficult to overcome. And most of the population genetics models that I've seen ha are clear in that respect. Mike Behe, in his book Edge of Evolution, said that it would take too long for two mutations, well beyond two mutations to arise for, uh, it to ever work in humans. And he got a response from a rather well-known team of mathematicians saying that he was wrong, and they did their own calculations, <laugh>, and they came up with a number that was larger than the time that was supposed to have, you know, we're supposed to have evolved from chimps Yeah. In 6 million years. And the time they came up with was like, uh, what was it? I don't remember precisely this, this,
Speaker 2 00:15:56 The Du Dirt and Schmidt paper,
Speaker 3 00:15:57 The Dirt and Schmidt paper. Yeah. And Mike pointed out that they had made an error in their calculations, which haled the number mm-hmm. <affirmative>, but still it was <laugh>, like two orders of magnitude too large to have ever worked. And yeah. So they didn't even, you know, they're arguing with Mike over his numbers and, and coming up with a number that is ridiculously large, uh, for the way
Speaker 2 00:16:21 These, these, these are mathematicians who came in to, to check this and, uh, make sure that the evolutionary story was holding up. And then they, their own calculations showed that it didn't hold up. And then when that was pointed out to them, they said, well, that's a problem for the biologist. And they <laugh>, they, they, they kinda, as far as I know, I don't know if they've done any more, um, publications. No,
Speaker 3 00:16:42 I don't think they have. I don't think they have <laugh>. There've been other people who've done work. Um, but the best and the most thorough model that I know of is the one that Ola put together, Ola Hoster. Mm-hmm. <affirmative>. And that was published in a proceedings of a symposium volume, and then looking at the waiting times problem with Ola and Gunther Beckley, we published a paper in the Journal of Theoretical Biology called on the Waiting Time until Coordinated Mutations get fixed in Regulatory Sequences. And it's a very thorough model, but the, the thing is, it doesn't address the specific waiting time for a specific biological problem. It just is a general mathematical model.
Speaker 2 00:17:28 Right. Well, and part of the issue is that if you're saying, for example, what is required to take a, make a big change, you know, the, the land mammal to the whale or the, the fly that can't fly to the fly that can fly, we simply don't have all the information to know exactly how many and which mutations are required.
Speaker 3 00:17:49 Uh, yep. You can estimate, but you don't know specifically what mutations took place. Yeah.
Speaker 2 00:17:55 Is it fair to say that you're kind of looking at a general, and I would say almost even in some ways an optimistic view of the number of mutations that might be required for some of these large scale changes?
Speaker 3 00:18:06 We try to be as thorough as we can. So we look at mutations occurring at any time, and we incorporate something called stochastic tunneling, which means mutation one can occur and mutation two doesn't have to wait for mutation one to get fixed. Mm-hmm. <affirmative> mutation three doesn't have to wait for the other two to get fixed. They can arise at separate times in the population and, uh, independently grow to fixation. And then when they're all in place in the same gene is when you, you succeeded. And the interesting finding that we reported is that the waiting time increases exponentially for each additional mutation. So two mutations versus three mutations is an exponential increase in the waiting time. And that's when you allow for, uh, mutations to having to take place without any benefit to the organism, you have to wait till they're all there before there's a benefit mm-hmm. <affirmative>. And it's allowing for back mutations, like I said before. So it's a pretty careful model.
Speaker 2 00:19:15 Yeah. No, that's, that's, that's perfectly helpful. Thank you. I was, I meant optimistic in the sense that sometimes people will say, well, gee, you only need a couple of mutations to, to move from point A to B, but that, that's probably not true in a lot of biological changes. <laugh>. Right. We're gonna need more than that. So, yeah. So this, this is, would you say this is one of the most comprehensive models that's been laid out?
Speaker 3 00:19:36 Yes, I would, and I think there are still further tests we can make in other papers that incorporate selection as a factor. But actually the thing we're testing is if there's no benefit to any of these mutations until they're all there, that would be the most rigorous test for an evolutionary scenario. Otherwise, you have to have a situation where each mutation provides a benefit. And then it's a sort of a hill climbing effect where it's, it happens a lot faster when you can select for the, where there's a benefit for each mutation and it increases in the population faster.
Speaker 2 00:20:15 Sure. Sure. Yeah. And that, that takes us back to what you were talking about earlier with the isolation within sequence space and how hard it is to move from, from point A to B. Yeah. Mm-hmm. <affirmative>. Okay. So I know you've done a fair amount of work in the area of human origins over the last, um, few years. And have you even kickstarted, I think some new research and conversations within the scientific community in this area? Tell us about that.
Speaker 3 00:20:39 Okay. This is a, a project that I didn't plan on having a philosopher contacted me and said, uh, how strong is the scientific argument against Adam and Eve? And I, I said, I don't know <laugh>, I haven't thought about it, but I'll go look. And uh, he was tremendously excited. He said, I was the first geneticist that was willing to even look at the problem. Oh,
Speaker 2 00:21:04 Interesting.
Speaker 3 00:21:04 And so I did, I went off and checked the literature and of course the first thing you find when you type in evolution and Adam and Eve is the paper by Francisco Ayala, where he said he, the title was The Myth of Mitochondrial Eve. Mm-hmm. <affirmative>. And people may remember the big uproar that happened with the paper, claiming that we all descended from the same woman in Africa some 150,000 years ago. And this was based on sequences of mitochondria in different women in from Africa and, uh, around the world. And the reason it caused such a furor is because Christians took that and ran with it and said, oh look, we've established the reality of eve population, geneticists were angry about this use of the paper because Yeah. The problem is that there may have been a single eve that we're all descended from, but there were other women present and alive at the time, and it's just that their genes didn't make it through to our current population.
Speaker 2 00:22:14 Right. Yeah. So it's a misuse of the paper cuz the paper wasn't claiming that Eve was either the first or the only.
Speaker 3 00:22:21 Right. Yeah. And so, um, Francisco ay is a famous population geneticist, and he set out to disprove this issue of Eve, and he did it by looking at a particular gene in the immune system called H l A D R B one. And that's complicated that name, but it basically means it comes from the human leukocyte population, h l a, that gene. And it, it's a variant of that of the Dr gene that is widespread. There are many, many versions of it in the population. So he sequenced a number of genes in various individuals, and then he made a inheritance tree, a genetic inheritance and tree to say how those genes may have been related over time and traced it back to a single origin, sort of like what happened with mitochondrial eve mm-hmm. <affirmative>. And then he said, okay, what's the rate of mutation in this gene?
Speaker 3 00:23:21 And he did some calculations to establish the rate of mutation. And he drew a line at 6 million years and there were something like 32 different versions at the time, 6 million years now, 32 different mutations. There had to be at least 32 different women contributing to the, the genetics at the time of 6 million years, which is more too much for eve if Yeah. Eve if, if Eve was at 6 million years, she would've been only one out of 32. Problem though is a problem with his calculations. I, I continued looking in the literature and within a couple of years, some other genetic researchers from Sweden, Bergstrom at all said there was a problem with his research because the piece of DNA n he chose to look at had a high rate of mutation, so it would skew any calculations he did. It also was under strong selection, so that would skew everything in the calculations.
Speaker 3 00:24:17 So they said, we need to find a neutral piece of DNA n a to sequence that doesn't have those problems. And so they looked at a sequence of d n a, just another, another different sequence, just a little bit removed from the one Ayala had chosen and they redid it. And the number of genes that were necessary to explain the pattern of inheritance with that new sequence was reduced substantially. It was reduced down to seven. Now seven is pretty close to the nu number that Adam and Eve could have carried. Adam could have had two, and Eve could have had two. Mm-hmm. That would be four four total that would be allowed at the, at the origin <laugh>. So I said seven. Huh. That's interesting. And I wrote about it in a book called Science and Human Origins along with Casey Luskin and Doug Ax.
Speaker 3 00:25:10 So that was the whole start of my interest in this question of Adam and Eve <laugh>. It kept growing. I decided that what needed to happen was we needed to model a mathematical model where we could start with two and let the population grow and fo follow the mutations in the, in the different population individuals, and get to a point where the diversity in the population, the number of mutations in their spread, matched what we have in the current population. Mm-hmm. <affirmative>. And, and then you could find out how far back you could go to get to two sort of by
Speaker 2 00:25:48 Yeah. Two individuals,
Speaker 3 00:25:48 By, uh, two individuals by a process of illumination.
Speaker 2 00:25:52 So, so Anne, talk to me about this primordial diversity just a second, because I think some of the early research had kind of assumed a single version of the gene or a single version of the genetic sequence. Were, were you the first one to suggest that you start with primordial diversity or had others already talked about that?
Speaker 3 00:26:10 I don't know. We did the model two ways. One without any diversity, so single version mm-hmm. <affirmative> and the other where we assumed that Adam and Eve could have been diverse in all copies of the gene, meaning they carry different mutations.
Speaker 2 00:26:24 Yeah. And that's the four.
Speaker 3 00:26:26 Right. So, so Ola made a model and I, I think it was a brilliant model. He solved a problem of computational expansion because as you grow a population forward, it goes exponentially, it doesn't mm-hmm. <affirmative> doesn't go just a little bit at a time. So his model allowed us to make the calculations going for a long time. We wanted to be able to go back as far as 6 million years, which is a long time to follow the population. So we started with two and we let it run forward. And then it turned out that if you had no diversity at all, you just had one version of the gene, it would take a million years to get to what we have. Okay. As a current, that was already a falsification of what the population geneticists had claimed, which was that we couldn't get the diversity even at any time before 6 million years.
Speaker 3 00:27:25 Yeah. Then we continued the work and we said, what about if we do it with diversity, as you said, original diversity in the population in, not in the population, but in the individuals of Adam and Eve, if they carry different versions of the gene going forward. And it wasn't just one gene, we were looking at the whole genome mm-hmm. <affirmative>. And so we used a database called a thousand Genomes database as our, uh, source of information, and we worked our way back to the start with a, to generate a tree. And then we let the populations run forward following that tree, and we found that we could get diversity matching the current diversity in our, in the human population in 500,000 years. Okay. Now, 500,000 years isn't that long. It's about the time when Neanderthal and, and Denise events separated from the human lineage. 500,000 years is a drop in the bucket compared to 6 million.
Speaker 3 00:28:25 So what does this prove? It doesn't prove that there was Adam and Eve, it just means that it's possible there was an Adam and an Eve. It also allows for the possibility that there was a sudden bottleneck, uh, where something killed off a, a whole population of, of homages mm-hmm. <affirmative>, um, except for two, that would be a possible answer for, for why it happened. Now, the response from scientists has been interesting about the time we were finishing up the work and getting ready to write it, someone called Richard Bugs, who is a professor in England of plant genetics challenged Venema about a statement he'd made in his book that claimed it was not possible for us to evolve from two, because it was too unlikely. And so bugs challenged Venema and they started a debate online, back and forth writing to each other and arguing.
Speaker 3 00:29:26 And then Joshua SWAs joined in and, um, everybody was arguing the case that I was making about what was possible in human genetics. And they came to a final resting point where Bugs and SWAs said by their own calculations, using a different method that we could have come from a population between 500 and 700,000 years ago. Okay. And that, that venema was wrong, uh, <laugh>. So then I proposed the model in December of that year, it was published, Ola and I published it. And what was the response? Well, we, first of all, SWAs said, well, I already showed that <laugh>, but SWAs had known about the research we were doing already. So, you know, anyway, and then a group of, uh, scientists who work with Swo said, well, you know, it, it didn't have to be a new creation. It could have been this bottleneck thing, but who believes that a bottleneck could happen that way? <laugh>.
Speaker 2 00:30:33 Okay.
Speaker 3 00:30:34 So where does this, the question rest, it just means that I've proved they can't claim it can't happen. Yeah. Because it might have.
Speaker 2 00:30:43 Yeah. Okay. Very interesting. Yeah. Well, it's, it's an interesting issue that you've worked on, and I think it's important just from a standpoint of when you hear claims, whether it's the original paper about Mitochondrial Eve, whether it's Ayala's work, trying to disprove that, whether it's claims about 6 million years or a 10,000 person population minimum, you know, dig in a little bit. Right. I mean, it's a lesson for all of us don't, don't accept those claims at face value, uh, until they've actually been dug into a little bit. And, and in this case, it turns out there were multiple things that were wrong with the, the evolutionary story and the claims that it's simply impossible that we could have come up from a population of two, or it's impossible that it could have happened more recently than 6 million years ago. So I think your work's been, you, you know, it's probably not gonna give any comfort to folks who hold to a young Earth, but I think it, it tells us that there's still open questions and that some of the previous thinking wasn't quite accurate.
Speaker 3 00:31:47 Yeah, I think so as an aside, there's some quotes from Lu Pastura that are relevant. It's, this is not specifically the topic, but because he's a great scientist, it's good to hear what he might have been thinking on the question. He said, great problems are now being handled. Now, this is in the middle of the 18 hundreds, keeping everyth thinking man in suspense the unity or multiplicity of human races. That means whether we all come from one mm-hmm. <affirmative>, one couple, or whether, whether there were separate origins of the races, the creation of man a thousand years or a thousand centuries ago. Of course those numbers are way too small, but the problem is when it happened, oh, the fixity of species. Does that sound familiar? Yeah.
Speaker 2 00:32:36 <laugh>
Speaker 3 00:32:37 Or the slow and progressive transformation of one species into another. There, he's, he's addressing Darwin directly the eternity of matter, <laugh>, the big bang, the idea of a God unnecessary. Well, that's what we're talking about. Hmm. Um, and such are some of the questions that humanity discusses nowadays.
Speaker 2 00:33:00 So they're wrestling with this in the mid 18 hundreds, right?
Speaker 3 00:33:04 Yep.
Speaker 2 00:33:04 Wow. Well, that's great. That's great. So Anne, just um, if you've got a minute, tell us about a couple of the books that you've been involved with in recent years and some of the contributions you've made there. Well,
Speaker 3 00:33:14 The first was the Science and Human Origins book with Doug Axon. Casey Luskin. Then there are the scientific papers. And then I helped to edit the Theistic Evolution book, theistic Evolution, not to not about being in favor of theistic evolution, but as a critique of theistic evolution, scientific, philosophical, and theological critique. There's a big thick book. You could use it as a door stop. <laugh>.
Speaker 2 00:33:40 Yeah. Massive, massive tone for anybody who's interested. But great, great contributions from lots of individuals in, in the community there. So that's a great contribu.
Speaker 3 00:33:50 And we are in the process of publishing another book, which is from the Catholic point of view. The, the Theistic Evolution book I worked on before was Evangelical Through and through. Hmm. Um, but this next book is asking the question, how intelligent design theory might actually best explain Catholic teaching, uh, compared to evolution.
Speaker 2 00:34:15 All right. Excellent. Well, Anne, thank you so much for being with us today and for all the important work you're doing to help move our understanding forward in these areas.
Speaker 3 00:34:23 My pleasure, Eric.
Speaker 2 00:34:24 Thank you for joining us for this episode of ID The Future. To hear more about the important work carried out by scientists that shows evidence for design and nature, and helps us better understand our own origins. Join us again here at ID the Future or Honor sister YouTube channel Discovery Science for ID the Future. I'm Mary Anderson. Thanks for listening.
Speaker 1 00:34:44 Visit
[email protected] and intelligent design.org. This program is Copyright Discovery Institute and recorded by its Center for Science and Culture.
