[00:00:01] Speaker A: Whenever technology advances, that opens up new understandings. It helps us to understand life at a deeper level. And I really think the issue of quantum technology, nanotechnology, is exciting because what people are just starting to realize is that quantum mechanical effects are at the heart of how much of biology operates. So if you look at the bleeding edge of biology and the bleeding edge of engineering, you're seeing the two start to converge, Idaho about evolution and intelligent design.
[00:00:36] Speaker B: Intelligent design is fundamentally a theory of design detection. But in addition to helping us identify the best explanations for past events, Idaho can also help guide experiments that study the present day operations of nature. And that's what we're going to be talking about today. Welcome to IDE of the Future. I'm your host, Andrew McDermott. I am welcoming in a brand new studio, by the way. We're recording from Studio west in the Seattle office of Discovery Institute. And my guests today are Dr. Brian Miller and Dr. Emily Reeves.
And our focus of conversation today is a new paper co authored by our science team that unpacks how intelligent design can guide scientific research in multiple fields of science. It's a very exciting paper, actually.
I can imagine how much I'm going to be sharing this with people when they have questions, hey, what can I do?
We know it studies the past, but what can it do for us in the present? This is a great summation of all of that, so I'm excited to share it and also excited to unpack it with you guys today. Let's start just a little bit by sharing what you do at Discovery Institute and where you're currently focusing your research. Emily, do you want to start us off?
[00:01:51] Speaker C: Sure, yeah, I can kick us off. So I'm a research scientist here. I've been here five years now, which is hard to believe.
My background is in biochemistry, so I received my PhD from Texas a and M University where I really focused on how bacteria organize themselves subcellularly.
And now recently at Discovery, I've been focused on systems biology and even more specifically, how we can use design and engineering principles in biology to improve biological research.
[00:02:26] Speaker B: Okay, awesome. And for those who don't know what systems biology is, we're definitely going to be unpacking that and how it relates to engineering and to ID. Brian, what about you?
[00:02:36] Speaker A: Well, I have a PhD in complex systems physics and I've been working at Discovery Institute as a research scientist for the last 10 years.
And when I first got here, one of my main projects was helping Steve Meyer with his book Return of the God Hypothesis. So I was assisting with cosmology models and how that relates to design.
But then in the last several years, I focused a lot on more of the application of engineering to biology and related fields like the rarity of proteins, the origin of life.
Also, I've been the research coordinator, so I've been helping to support our various researchers by helping them connect with other researchers, giving them guidance on their experiments, and then helping them connect what they do to the intelligent design framework.
[00:03:19] Speaker B: Okay. It's amazing how minds just come together, you know, and can work in awesome ways together. And we definitely see that a lot at Discovery Institute. Now, you Both worked with Dr. Casey Luskin and Dr. Jonathan McClatchy, two other members of our resident scientist team at the center for Science and Culture, on this new critical review paper that was recently published in the journal Bio Complexity. And we'll definitely be telling people how to get a hold of this so they can read it, bookmark it later. But before we dive into specific.
Can both of you just touch on the genesis of the paper?
Why this paper now? How did it come together and what was the goal with writing it?
[00:04:01] Speaker A: Well, it's a funny story because what happened is KFC was sent a request for papers for a particular journal, and we decided that this would be a perfect time to bring together all of our research in one paper just to give a nice compendium of what intelligent journal design can do to support science in one place. Well, we decided not to go with that journal, but instead we went with Bio Complexity, who had a much more appropriate set of reviewers that knew how to appropriately address the material that we're covering, and they gave us excellent review.
Then this has turned into a beautiful paper that we can give to people to give them a very deep understanding both of the philosophy behind applying design in biology or cosmology or other fields, and also the specifics of how we've done it and our research has been published.
[00:04:49] Speaker B: Okay, and Emily, would you like to add to that? Yeah.
[00:04:53] Speaker C: We really aim for this paper to demonstrate how you can use intelligent design principles to guide scientific research. That's kind of like the big picture point of this paper.
And yeah, we have. We split it into three sections in the paper, three types of research. We talk about theoretical research, historical research, and experimental research. And ID has contributed to all those areas.
My research is in the experimental category, but it's. Yeah, this is basically like a 30 page tour of the last, like, decades of intelligent design research, at least the parts we can talk about where we wouldn't get somebody's career in trouble?
[00:05:37] Speaker B: Yeah, sure, yeah, yeah.
It's a hefty paper, but as you said, it is a great summary of several fields and how ID framework can help that field. Now today we're going to focus on how ID principles can inform engineering based methodologies that can advance the study of biological systems. And Emily, you've done a lot of work in this area lately, so I'm going to ask, I'm going to direct some questions to you specifically before I get Brian's take on some other research.
Although you're both welcome to weigh in on each other's responses. So Emily, what is or was the systems biology revolution and how does it intersect with intelligent design?
[00:06:20] Speaker C: Yeah, that's a great question.
[00:06:22] Speaker A: Really.
[00:06:23] Speaker C: The systems biology revolution was driven by technological innovation.
In the early 2000s there started to be development of DNA sequencers and these more advanced mass spectrometry techniques. This gave rise to genomic and proteomic and transcriptomic data.
Technology changed and we started being able to actually capture these really big data sets.
And then when we looked at the data sets, what we saw is totally different from what you'd expect out of like a cobbled, haphazard system. Instead, these big data sets showed that there, that the systems that were producing these were incredibly complex.
And so this revolution was really necessary because we couldn't explain these data sets just using the reductionistic framework and then how that intersects with intelligent design theory. So intelligent design theory says, right, that you expect in nature there to be design like you expect, or intelligent agents use certain design principles, right, like they do top down design. They use like hierarchical organization where you have like one subsystem doing one thing over here and the another subsystem doing something different.
So agents build things in this way.
And so the way that systems biology intersects is because these big data sets started to be most readily made sense of by using concepts like hierarchical organization, optimization, robustness, like those kinds of things that come straight out of intelligent design theory.
[00:08:19] Speaker B: Okay, and so how does systems biology use ideologic to do the research?
[00:08:26] Speaker C: Yeah, so if we think about the scientific method as like observe. You have your first step, you know, of making observations and then you make hypotheses and then you test those through experiments or whatever. And then, you know, you figure out was your hypothesis true or not. Right. Those are the four main steps of the scientific method.
So like in the, for systems biology, the observation was that you have all this big data that cannot, that shows that organisms cannot be explained from like a reductionistic standpoint. Right?
And then the hypotheses really was, okay, can we use these systems principles to actually make sense of what these big data sets. So that was the hypothesis, and then they tested it, right? They brought systems biology as a field, started using these key principles to try to make sense of the data, and the result is it worked. And I like to say, you know, the proof is in the pudding. There's like five university departments now that just are called, like, systems biology departments. There's multiple academic journals. So this has really been a successful venue of research.
Now, what's interesting is that systems biology, I think, largely has tried to sanitize as much design language as possible.
But that doesn't mean, like researchers in this field, they're not necessarily ID proponents, but they are still using design principles to push biology forward.
And one of my personal favorite examples of this is a researcher at Wiseman Institute of Science called Yuri Alon.
So he's just built his career around studying different design principles and engineering and looking at the engineering and biology. And he's like one of the top systems biology researchers.
So you don't have to be an ID proponent to use design principles to do better research. And systems biology is certainly an example of that. But if you really like, for instance, these things like hierarchical organization, optimization of a system, those things were not predicted by Darwinian theory. In fact, the opposite was really predicted. So systems biology is really using ID theory, especially in terms of the steps of, like, observe, hypothesize, to help us do better biology.
[00:11:09] Speaker B: And I find it interesting that technology has driven the rise of systems biology. And that very terminology that we use in technology and in engineering is the terminology that best explains the biological systems that we're now studying with it. So it's quite interesting. Brian, what would you add to the advent of systems biology and what you've noticed?
[00:11:32] Speaker A: Well, I think Emily's exactly right that with the advance of technology, we can now understand life at a much deeper level. We can understand the details at a much smaller level.
And also what's happened is engineers have started to work with biologists at a much greater extent. So they brought that framework, their insights, their models into biology and have done it in a way that's created enormous advancement. So they're able to see and understand patterns in biology that many biologists didn't because they weren't trained as engineers.
So we really are in what others have called not just us, but what others have called a biological revolution. And then also even on the philosophy side, you have philosophers of biology that are operating from a very traditional materialist perspective who nevertheless say that we're going through a revolution because this reductionist viewpoint isn't working. Now, when we say reductionism, what do we mean by that? Reductionism means that you sort of assume what really matters is a small scale interaction of atoms. So in biology that means that you really care about chemical interactions, proteins interacting with other proteins at a molecular level. And you think all the higher level organization is more of an afterthought. It's more of just something that emerges accidentally versus an engineering perspective where you realize what matters is the high level logic. And the high level logic is what determines what happens at the smaller levels. So many people are saying that this reductionist perspective has slowed progress, but they use terms like holism instead of irreducible complexity. But it's the same idea. But an anti reductionist holistic systems level understanding is what's essential to move biology forward.
[00:13:14] Speaker B: Very interesting. Yeah. Top down rather than bottom up. Yeah. And the way that you, the approach that you choose can have a fundamental impact on how you see living things.
Well, Emily, let's turn it to engineering a little bit more. You know, when we build things as human beings, there's a forward engineering approach to it. Right. You start with a, what's called a functional requirements list.
You draw that up to guide the process of engineering and building. But if something's built already, a different approach is required. Tell us about reverse systems engineering and how it can be used by ID proponents.
[00:13:52] Speaker C: Yeah, so ID proponents have really been the ones spearheading this effort to develop reverse systems engineering methodology, which is kind of a long phrase to be used in biological systems. And that's because if you take a very explicit design perspective, then to do biology really is to do reverse engineering. Because reverse engineering, like you said, it's not forward, but you're already looking at the design system itself. And so you're just trying to understand how was it built, how was it constructed?
[00:14:26] Speaker B: Okay, now share with us some of the work that you've done in this area. I know you've spearheaded quite a bit lately.
[00:14:34] Speaker C: Yeah, yeah. So I want to say something first about just a little bit more about how the methodology works. So as we talked about with systems biology, really in the steps of the scientific method, you're using design assumptions in the observe and hypothesize steps. But in reverse systems engineering methodology, you're kind of taking it a step further and you're saying, okay, after the observe step, you can actually add two things that can help you do that can help you ask hypothesis and build hypotheses and predictions in an even more design based way.
And those two things are first, system modeling and second, requirements analysis. So system modeling is basically taking all the information about, you know, that's in the literature about a system that you're studying and putting it into a systems engineering qualitative model. And the reason that's so helpful is because it kind of forces biologists to think like engineers. You have to assign relationships between objects, you have to designate subsystems. And so these different things kind of force the biologist to think like an engineer, which is helpful, you know, because downstream you want to be able to generate design directed hypotheses. So that's the first step. And then requirements analysis is tricky for the reverse engineering process because when you're doing forward engineering requirements are just like what the stakeholders want. Like they come to you and they want, you know, they tell you like, oh, we want a system that looks like this, that does that and, and what the stakeholders ask for gets turned into the requirement specification.
[00:16:31] Speaker B: Okay.
[00:16:31] Speaker C: Now when you're going the other way, it's more hard because you have to look at the system and kind of infer what, from the top down, what you think those requirements are.
But the reason this is important is because it can really help you connect like downstream details to upstream system level function. Like I'll use the example of the human appendix. So as all of us know, the human appendix for many years was thought to just be like vestigial, right?
And this came straight out of evolutionary thinking, like, oh, it's just some leftover remnant from a common ancestor that we don't need anymore. But from a top down design perspective, there's actually a requirement that if your gut gets completely washed out of good bacteria, that you need some way to replenish those bacteria. Right. And so that's actually what the appendix does is it's a little pouch of bacteria that when your gut gets washed out, it's, it reseeds it. So it's kind of like a built in probiotic. And so the reason it's important to think about requirements is it helps you avoid making mistakes like that where we look at things. And because we don't understand the top down design, we think like, oh, there's no reason for this part of the system.
[00:17:56] Speaker B: Okay. And you recently co authored a paper that unpacked this approach, did you not?
[00:18:03] Speaker C: Yes, yes. Yeah. So in 2024, Dr. Jerry Fudge, who is at Texas A and M Commerce, he and I published A paper that developed a reverse systems engineering methodology for biologists, and then we applied it as a case study to glycolysis.
[00:18:23] Speaker B: Okay.
[00:18:23] Speaker C: Yeah, and one of my, one of my favorite examples of what we kind of showed in that paper is we started with the design premise that.
Yeah, we started with the design premise that you should, if you, if you have an architecture that's being used over and over and over again in biology, you should be able to find requirements, functional requirements, that explain why that architecture is used over and over again.
And so glycolysis is this, it's. It's a metabolic architecture that you see in like bacteria, dogs, humans, it's all across life.
And traditionally people have thought, oh, it exists there because of common, it was in a common ancestor.
[00:19:15] Speaker B: Yeah.
[00:19:16] Speaker C: But when we really dug into this, we wanted to see is there a system or functional requirements for why this might be present in cross living systems. And what we saw is we. So we dug into the literature, we built a system model, we did requirements analysis, and sure enough, we found that there's actually all these studies that have already been done that show glycolysis is optimized for all these different things. Like for instance, is optimized to not produce any toxic byproducts that would otherwise like kill the cell. Or it's optimized so that it's very energy efficient in the sense that it doesn't cost the cell any more proteins than are necessary because making proteins is expensive. So it has like 10 key enzymes, but it uses just the least amount of them as possible.
So it's optimized around all these things. And there's actually like a fascinating paper which says it's actually Pareto optimized, which means you can't make any single part of it, you can't change any part of it without making some other part worse.
And so now we have this functional reason for why glycolysis is the same across life other than common descent.
It actually exists because it's optimized for its purpose in the cell.
[00:20:47] Speaker B: And you unpack some of this in the paper we're talking about today, right?
[00:20:52] Speaker C: Yes, a little bit.
[00:20:53] Speaker B: Yeah. Okay.
Well, Brian, let me pivot to you with just a few questions about what else is in this paper with regard to what we're talking about. First, were the similarities between human engineering and biology predicted by evolutionists?
[00:21:10] Speaker A: Oh, that really is a central question. And the answer is obviously not, because in fact, you have quotes of a very famous quote by the author of why Evolution is True and what he said was that the systems, in fact, he said that the systems we study in biology look nothing like human engineering. They look like what are called Rube Goldberg machines. And a Rube Goldberg machine is sort of a comical machine where you take a lot of different entities that aren't designed to work together, like a parrot and a cracker and a, and like a spoon, and you put them together in this weird, confusing contraption to accomplish some simple task. That's the joke behind it. But that's what biology predicts or evolution predicts, because if there is no guidance, there's no designer. It's just random changes. What happens is something will form. In theory, that's useful, but it wasn't designed to work in a larger system. But occasionally they argue that these different pieces come together and serendipitously, they do something that's more complicated, like propulsion. And what happens is they expected.
And even often today, they'll say they believe biology is inefficient, it's suboptimal, it's clumsy. A lot of things are just remnants of the past.
In contrast, what engineers do is they plan things in advance. It's not like a Rube Goldberg machine, which is bottom up.
You have the bottom layer of the hierarchy of design that appears first and then it comes together, but it's top down. An engineer plans out what they want to accomplish. And what they want to accomplish, they have specific goals, like going, let's say you want to create a rocket that can colonize Pluto in one trip. That's your goal, that's your purpose. And your goals would be you've got to have life support, you've got to have cargo, you've got to have propulsion, you've got to have navigation. And then what happens is an engineer will design each system to meet each goal, roughly speaking. But the goals are usually really hard, like propulsion. So you break that into sub goals and you have subsystems dedicated to each sub goal. So you start with the goal, you start with a high level design logic, and then you work your way down, and everything is designed with the whole in mind. In addition, what happens is to ensure everything operates coherently, you've got to have a lot of auxiliary systems that helps everything coordinate with each other, share resources, and operate in a coherent way to accomplish the goal. That's exactly what we see in biology, and that's not what was expected from the evolutionary theory.
[00:23:44] Speaker B: Okay. And yet the similarities can better be explained through a design framework.
Tell us how.
[00:23:52] Speaker A: Well, what's amazing is that when Systems engineers in particular study biology. They see the same patterns, the same principles, the same concerns.
So if you read a systems engineering book put out by people like NASA, I looked at one by NASA, one put out by the Department of Defense, and what you find is systems engineers care about a lot of things. They care about carefully designed interfaces.
They care about signal processing. They care about sending information.
They care about risk management. They care about things like adaptation, if things change in the environment. And when you look at life, what do you see? Those exact same concerns are dealt with in life in brilliant ways. Let me just give you one quick example. And this is out of Steve Laughman and Howard Glicksfenberg book youk Design Body, which goes into this.
When sound goes through air, it has a certain resistance or impedance in the air. Well, in your ear, what happens is a sound is sent to this canal that has a liquid, and it's in that liquid the sound is converted to nerve impulses. Well, the challenge is the impedance or the resistance of sound. And air is different than in a liquid. So if you simply had the sound and air touch that canal with the liquid, most of the energy would bounce back. But you have three specially designed bones in mammal hearing that will match the impedance. So all the energy goes from the air into the liquid. And that only happens because of careful engineering where you plan it all in advance and you see risk management, you see very advanced signal processing in hearing, exactly as you'd expect in systems engineering.
[00:25:33] Speaker B: Wow, that is a great example, actually.
Now, this paper also addresses the work of Stuart Burgess, bioengineer.
Can you tell us just what observations of intelligent agents related to his research?
[00:25:48] Speaker A: Yeah, and that's really. Emily talked about this also. But an important goal of this paper was to give a roadmap for future researchers who know precisely how they can apply design thinking to research. And that roadmap really follows a scientific method, which is talked about several times in the paper that you make observations in the world, like what do intelligent agents do? What do engineers do? What are the patterns that they employ?
You then ask the question, well, how does that lead to certain expectations that we have or predictions we have about life? What should we see? Where should we look? What questions should we ask?
What model should we attempt? And then you do the experiment or you do your observational research and you see if your predictions match the expectations. And that's exactly what Stewart Bridges did. Stewart Bridges, actually, what's remarkable is because he assumed that life was designed, he actually would copy the designs of life into human engineering. And that led him to become one of the leading engineers in the entire country. He actually got an award for being the leading mechanical engineer.
Well, what did he do? Well, he said, what do human engineers do? Well, if you're dealing with something like limbs, and a lot of his research is on vertebrate limbs, what you do is you want to use the most efficient general design possible. If you look at anything from a digger in construction equipment or to a robotic arm or to, let's say, an artificial arm used in surgery, you see typically three hinges. You've got a hinge that attaches the arm and to the base of the machine. You typically have a hinge in the middle to help direct your arm. And you typically have a hinge at the very end where you have your digits that will pick things up. You also have very carefully matched control systems to that arm. So your control system has to be carefully designed with the arm details, architecture in mind. You've got to have carefully designed engineering in terms of electronics, in terms of delivering lubricants, if that's necessary. What did he expect? He expected that we'd see the exact same thing in vertebrate limbs. We expected to be highly optimized.
You should see a general design pattern, but you'd expect every implementation of that design pattern to be perfectly matched to the organism's needs, to its goals, to its lifestyle, to its environment.
He expected that you're going to see a perfect integration of bones with muscles, with nerves.
So those are sort of the expectations he came from. An engineer, from the perspective of engineer.
[00:28:19] Speaker B: Yeah. And in his research, you know, what was confirmed? Did an ID framework result in a richness of discovery for him?
[00:28:30] Speaker A: Exactly right. And what happened was every expectation he had based on his engineering assumption matched reality. What he found, and he's done some beautiful papers about this, is that every single limb of every vertebrate appears highly optimized, perfectly designed for the needs of that organism.
This is really important because, again, as I mentioned before, the evolutionary framework predicts suboptimal forms. People said, well, we think the ACL is badly designed because it's not large enough, or we expect, we think that a lot of the bones in the foot are not really that useful. It's just remnant of its evolutionary past. Stuart Burgess said that's absolutely not the case.
That when. And this gets into Emily's talk about Pareto optimization, because a mistake that many, many people make who are biologists is they look at maybe one or two concerns like speed or efficiency, but what happens is there's multiple concerns that the organism has. Like in, let's say the human foot ankle complex, a human has to run, they have to walk, they have to walk over uneven terrain, they have to squat. And what Stewart Burgess showed is that when you look at every single activity that humans do, the particular design of our limbs is optimized. It's perfect for everything it has to do. He found that every bone is perfectly integrated. The interfaces are perfectly matched. What he found also is that every tendon, every ligament, every nerve, every sensor is perfectly engineered to perfectly match the architecture of the bones. And there's an incredibly complex control system that's optimized for the specific details of every single limb.
Wow.
[00:30:13] Speaker B: Yeah. What a fruitfulness of research has come out of his work. And in his book Ultimate Engineering, he unpacks this at length, doesn't he?
[00:30:21] Speaker A: Yes.
[00:30:22] Speaker B: Shows how that sort of approach is bringing great discovery in his fields.
Well, so you've also talked about Mike Egnor and what he's done in his research. Let's just touch on that. What observations of intelligent agents shaped his expectations as he looked at neurology and the blood brain pathway and things like that.
[00:30:47] Speaker A: And Michael Egnor's research is really going to be one of the iconic examples. It's going to be a model for so much research.
It was absolutely beautiful because Michael Egnor is just, he does brain surgery. He's, you know, just a top level expert on blood flow in the brain. And what he recognized is something very odd because your heart pumps with a cycle. You pump, you pump, you pump. So the blood pressure is high, then it's low, then it's high, then it's low. Well, the problem is if the blood went directly into those very, very tiny capillaries in your brain, they'd explode.
So there was a system that causes the blood to go in a constant rate. So it's this constant flow in the brain. There's no pulsation.
What happened is Michael Egnor, who believed that our bodies were designed, said, well, I bet if I talk to engineers, they can help me figure out what's actually going on.
We talked to a bunch of engineers and what they said is, well, in engineering we typically use what's called a stop band filter. There's usually some mechanism designed in devices that deal with oscillations to prevent the system from being damaged by the oscillations.
Basically, it blocks the oscillations from affecting the system.
And a beautiful example is this building we're in because if you go to this building, what happens is a big concern is earthquakes. And what happens in an earthquake is all this energy is being sent in the building, it will then move with the resonant frequency of the building and it'll collapse. We have special devices in this building with what they do is they'll channel energy from the building during an earthquake so it doesn't damage the building. And that's a general feature that if you look at electronics, you look at buildings, you typically have a side structure that will channel energy from this oscillation into the side structure that removes the power and helps to stabilize it. So there's a constant flow of energy into the system, which is not disruptive. So he said, okay, well let's go with that. And these engineers, he said, let's look at the, the interstitial fluid. They'll look at the fluid in the brain, they'll look at the arteries, they'll look at the veins. And what they found is exactly that. They found that your skull and every part of your cardiovascular system acts like a stop band filter. That what happens is during the blood flow the arteries will change shape, you'll see the fluid pressure changes in the brain, the veins change shape so that a lot of the push is the energy from the initial push is redirected and then when the heart stops pushing, the blood will be re released from this repository where it's held to allow for a steady flow of blood. He even created a model where he showed how sort of a conceptual model that looks like a circuit diagram perfectly matches what engineers do, both in the logic and the mathematics. Here is a beautiful example where he applied this engineering intuition to help gain insights that other people missed because they did not recognize it was designed. What's happened is Michael Egnor has become a leader in his field because he had a better understanding, a deeper of understanding what was happening. He proposed better approaches to seeing things like head trauma. So he's actually reshaping his field in helping to improve the lives of patients.
Again, that's the point I really want to make is what's happened in the intelligent design movement is initially it was we want to really come up with ways of detecting design that's like built in Demski, Michael Behe to some extent, Steve Meyer. But then what's happened in the last decade is like, well, we really have a strong handle on how to detect design. Let's show how design makes better predictions. It gives you deeper insights. And now we're at the point where our Researchers are becoming leaders in their field. So Stuart Burgess is a leader in his field. Michael Egnor is a leader in the field. We also have another research that I can't talk about due to concerns of anonymity.
Who is looking at. We actually have two researchers now, I think about it, that are looking at areas in biology that people thought were non functional or useless, but because they believe design, they're actually finding function and are starting to lead their field in that. So that's kind of where we've arrived.
[00:35:02] Speaker B: So far from being the sign stopper that some of our detractors have called ID in the past, it's very much a sign starter. Right. And we're taking it from design detection of historical events and looking at different models that best explain those events to the operations now in effect in nature and being able to better understand them, better reverse engineer them. What I'm hearing then is that it's essential that those who study life must have this perspective, must have an engineering perspective. Is it just a case of getting the engineers and biologists together or do we need engineering training for biologists or a bit of both, Emily?
[00:35:45] Speaker C: Probably both.
[00:35:46] Speaker B: Yeah.
[00:35:47] Speaker C: Yeah, probably both. I think getting engineers and biologists together is often the first step and then the training, you know, whether it's one on one or development of courses or stuff like that, I think will kind of naturally follow.
[00:36:00] Speaker B: Okay, well, as we close this discussion, I just have one more question for, for both of you. This paper is highlighting that ID is moving into an experimental phase, what we call applied Design Research, or ID 3.0, depending on who you're asking. So looking ahead, what is the single most exciting research horizon or practical application in your chosen field that you think is going to open up some new discoveries and some fruitful research?
[00:36:32] Speaker C: Yeah, I think for me I'm really excited about the reverse systems engineering methodology because like I said before, I think it can help us do better research. And then the area that I think maybe I'm most excited about is just that of human health and medicine. So I'm working on a project right now looking at antibiotic resistance and I'm using this methodology and I'm just.
Yeah, but I'm excited how in the future like this can be applied to things like cancer research or genetic disease and helping us find both ethical and.
Yeah, I think like Brian said earlier, having this perspective really can give you a foot forward because when you're, you know, making assumptions of like optimization and good design, it just helps you generate better hypotheses. And for instance, not be afraid to look into fields that if we just look at biology and assume that there's purpose, and then, for instance, we'll also investigate things we haven't investigated before, rather than just assuming, oh well, that's some evolutionary leftover. So I definitely think ideas of science starter.
[00:37:46] Speaker B: Yeah, Brian.
[00:37:47] Speaker A: And I apologize, but there's just a couple I have to mention, but one I think is anatomy, physiology and embryology.
Because what happens if you think about, what does an engineer have to think about? They have to think about the construction of a building, they have to think about maintenance, they have to think about commissioning.
But what do you find? Life does it all. Life does it all at once. It builds itself, it constructs itself, it guides itself. And I think this idea of systems engineering, when you look at the higher level principles and patterns and concerns, when you apply that to life, I think that's going to lead to incredibly fruitful research. The second thing that I'm really excited about is ecology. Because what you find in life is not just that organisms are brilliantly designed, but whole ecosystems appear designed. That organisms are designed to communicate with different species, even in very different taxa, to coordinate to maintain a stable ecosystem. I think that's incredibly fruitful. Then finally, what excites me is whenever technology advances, that opens up new understandings. It helps us to understand life at a deeper level. And I really think the issue of quantum technology, nanotechnology is exciting because what people are just starting to realize is that quantum mechanical effects are at the heart of how much of biology operates. So if you look at the bleeding edge of biology and the bleeding edge of engineering, you're seeing the two start to converge.
[00:39:15] Speaker B: Wow. So lots of levels to unpack in life that we've barely scratched the surface of and that an ID framework can bring about fruitful discovery and research. Well, thank you for your time.
And I will mention to our audience that we have part one of this discussion, which is talking with Casey luskin and Jonathan McClatchy about this paper. And they unpack sort of a big picture view. And Jonathan helps us understand the whole case of junk DNA and how the ID framework revolutionized our thinking of that. So if you haven't caught that, that will also be available to you. But Emily, I appreciate your time and Brian, thank you for being today.
[00:39:57] Speaker A: It's been a pleasure.
[00:39:58] Speaker C: Thank you for having us.
[00:39:59] Speaker B: Yeah. And I will say, audience, if you're excited about this stuff, you know, if this, if this gets your blood, blood boiling and you're just really enthusiastic, well, Feel free to share this episode or the podcast in general with a friend.
We want to spread the news about this amazing phase of ID research that's going on and we need your help to do it. So really appreciate that. And again, catch part one, if you haven't already, of our discussion. Well, until next time, I'm Andrew McDermott and we'll see you next time.
[00:40:35] Speaker A: Visit
[email protected] and intelligentdesign.org this program is copyright Discovery Institute and recorded by its center for Science and Culture.
