Episode Transcript
[00:00:00] Speaker A: Foreign.
[00:00:05] Speaker B: The Future, a podcast about evolution and intelligent design.
Well, welcome to the ID the Future podcast. I'm Andrew McDermott, and today I'm welcoming to the show Dr. Emily Reeves once again to discuss a paper that she recently co authored that lays out a methodology that, based on systems engineering, that can assist the everyday biologist. That's right, I said biologist. In the study of living things. Now, Dr. Reeves, in case you don't know her, is a biochemist, metabolic nutritionist, and aspiring systems biologist. Her doctoral studies were completed at Texas A and M University in biochemistry and biophysics. Emily is currently an active clinician for metabolic, nutrition and nutritional genomics at nutraplexity. She's also working with fellows right here at Discovery Institute and the greater scientific community to promote integration of engineering and biology. It's exciting stuff. Emily, welcome back to the show.
[00:01:07] Speaker A: Thank you, Andrew. Thank you so much. And as you know, scientists love to talk about their research and I'm no exception. So I'm really excited to be here today.
[00:01:17] Speaker B: Yeah. Well, welcome. So today we get to talk about a paper you co authored that was published in 2024 in the IEEE's Open Journal of Systems Engineering. It's titled A Model Based Reverse System Engineering Methodology for Analyzing Complex Biological Systems with a Case Study in Glycolysis. And we'll get to that part a little later. But first, your paper is about taking principles from systems engineering and applying them to the study of biology. So let's get a few terms going first just so our viewers and listeners can stay on board. How do you define systems engineering?
[00:01:59] Speaker A: Yeah, so systems engineering is really the field that's involved with planning, developing, and designing and constructing human engineered systems. So it's what is required to ensure that all components of these systems work together effectively to achieve goals that are set by the designers. And this is required for building cars, cell phones, and computers.
[00:02:26] Speaker B: Okay. Yeah, because when you're building things, you're not just focusing on one subsystem. You want to look at how it all comes together as a whole and make sure that matches up, like you say, to what the stakeholders envision. So it's, it's really a top down, whole approach. Holistic. Well, your paper notes that traditional reductionist paradigms in biology have proven insufficient for understanding and accurately predicting complex biological systems. And it turns out there's a good reason for this. It's got to do with how the great majority of biologists are trained. Tell us about that.
[00:03:03] Speaker A: Yeah, so biologists and biochemists, they, you know, we typically receive little formal Training in methodology beyond just like the scientific method. And the scientific method, it emphasizes the, you know, key steps like observing things, questioning, you know, how things are working, hypothesizing, experimenting, analyzing data and interpreting those results.
So, you know, while this scientific method is invaluable, it doesn't offer a specific framework for studying complex systems. And there's this biologist, Yuri Las Benick, who highlighted this limitation. He said, you know, not specifically with the scientific method itself, but with the typical methodology that biologists use. And he, he introduced this limitation with a kind of thought provoking question. And that question is, would the methodology used to explore biological systems be effective at showing how a transistor radio works? For example, like if by, if scientists gathered a collection of working radios, then they broke them in different ways or isolated individual components, you know, having separate research groups focus on different parts, would this, that approach be an effective way to grasp and understand how the radio works and how to fix it? And Lasbanek argues that it wouldn't be, and I agree with him. So it seems that we need better ways to observe, question, hypothesize, experiment and analyze a complex system. We need some kind of methodology that will help us know what to expect when reverse engineering a complex system.
[00:04:48] Speaker B: Yeah, that makes a lot of sense. Now biology, of course, is the study of living things. We know this, how they work and how they emerged. But when biology is wedded to only one explanation for the origin of biological systems, namely an unguided evolutionary mechanism such as natural selection acting on random mutations, it necessitates it, requires, if you will, a bottom up reductionist approach to the study of living things. You can't really get around it. In other words, an organism is merely the sum of its parts through an unguided evolutionary process. And studying living things through this lens, it turns out, doesn't allow biologists to adequately understand or explain how things work and the mechanism by which they originated. But on the other hand, if you believe that organisms are more than the sum of their parts, you need a top down approach to investigate them. Now you and Dr. Fudge have adapted proven systems engineering methodology to enhance discovery in loving things. This approach is motivated by a few key observations. Can you share those with us?
[00:05:55] Speaker A: Yeah, so one, one of the key motivations for us was to, you know, to use this top down methodology to look at biological systems is that the systems appear as if they're designed, you know, if you think about the bacterial flagellar motor, the light harvesting complex, you know, of Photosystem 1 or ATP Synthase. Each of those systems is better engineered. Those molecular machines really is better engineered than the human counterpart. Meaning the bacterial flagellar motor is better engineered than an outboard boat engine. The light harvesting complex is better engineered than the solar panels we have on our house. The ATP synthase is better engineered than the hydroelectric generators that are creating electricity at the bottom of Hoover Dam. And even Francis Crick, who doesn't share my worldview, he, you know, is the person who co discovered the DNA double helix. He acknowledged that there were serious challenges in relying too heavily on evolutionary assumptions, despite, you know, believing in evolution himself. He actually, he warned, and I quote him, that it was highly dangerous to trust them too much, referring to evolutionary intuitions. And you know, perhaps this caution came from his own experiences of forming hypotheses based on evolutionary assumptions that ultimately proved incorrect.
So that's one motivation. And then the second motivation is that biological systems have already been demonstrated to have so much in common with human engineered systems. Biological systems are hierarchical, they're integrated, they're modular, they're optimized, and they're robust. So these are all characteristics of top down design systems. So it makes sense to use, you know, the tools that engineers use to make those systems and to adapt those to better understand biology.
[00:07:49] Speaker B: Yeah, it does sound like a great, a great tool we can use to understand complex systems. Well, can you give us an idea of the standard systems engineering process? You know, how engineers go about producing something that stakeholders want?
[00:08:06] Speaker A: Yeah, so it usually starts where something like this, where engineers, they meet with the stakeholders and those stakeholders give them a rough outline of requirements. And those requirements are like verbal or written details about what the stakeholders want. The product that the engineers are going to create to do this rough outline will eventually be formalized into a set of specific requirements and then often modeled using a systems engineering tool. And then once that is done, when more specific models about the different subsystems are developed, and then, you know, more refinements for those models happen. And once everything you know is working according to the requirements and the stakeholders are happy, then construction begins. And it usually happens in systems engineering that the smaller parts are, are constructed first and then those are assembled into subsystems and then those are tested and then those subsystems are eventually assembled into the final system. And then all through this process, everything is being compared with that initial list of requirements the stakeholders and the engineers agreed upon to make sure that the completed product actually meets the stakeholders expectations.
[00:09:24] Speaker B: Okay, and as you were explaining that, I was just thinking of, you know, all the things that humans make, I mean, as small as an iPhone and as large as an ocean liner. You know, it starts with the small stuff and then it builds. But it's got a very organized, you know, pattern about it. Top down, of course.
[00:09:45] Speaker A: Yeah. It's designed top down and then kind of built out bottom up.
[00:09:49] Speaker B: Yeah. And we see this all across, you know, human endeavor. And not once do we say, well, maybe that airliner did come about accidentally. I mean, that, that wouldn't even enter our minds. And yet when we get to the realm of biology and we see even greater instances of these types of designs, we are supposed to, you know, rule out design. I mean, it beggars belief. Well, you've adapted this methodology to biology, and you call it model based reverse systems engineering, or mbrse. Can you explain that for us a little bit?
[00:10:24] Speaker A: Sure. So we call it model based because it utilizes a system model as a map to keep track of relationships between the objects of the system and the processes that are making the system do whatever it does.
And then we use the term reverse because the goal of biology is to understand and predict how organisms function, not. Not necessarily to do a forward engineering phase, which is where something would be built. Right. We're just trying to understand how biology works. And then systems, we call it, because this approach utilizes requirements and modeling to tie components, the little tiny things we're studying, into the larger system level design. And that's what really illustrates how the whole is more than the sum of its parts.
[00:11:12] Speaker B: Okay, so altogether we have model based reverse systems engineering.
[00:11:18] Speaker A: Yep.
[00:11:18] Speaker B: Yeah. Okay, well, here's another term that I want to make sure our listeners and viewers are clear on. I've heard quite a bit about what's called systems biology for a few years now. From what I can gather, it's a new approach to studying living things holistically, analyzing biological systems as a system, a complex system, rather than a simple collection of parts. This new discipline seems to be about 25 years old, roughly tracking with the technology and computational modeling that allows the scientists to actually take this approach. So it's quite a. Quite a new approach, but very exciting. Now, how do you. What would you add to that definition and how does it intersect with the approach you're taking?
[00:12:03] Speaker A: Yeah, well, I must say, Andrew, you summarize that quite well. And you know, as you mentioned, it, systems biology, it is a young field and it seeks to understand how parts of biology are integrated to form a system, how the individual components, you know, work together to make that system. And then another way you could talk about Systems biology is that it attempts to, you know, understand the, its focus is on understanding the interactions of the system rather than just, you know, studying this protein here and this protein over there.
And yeah, as you mentioned, systems biologists also use big data. They use genomic data, proteomic data, transcriptomic data to try to gain insights into how the system works.
[00:12:48] Speaker B: Okay, I can imagine that some of them might want to roll in AI as well to maybe use as a tool to study a lot of the data.
Well, in your paper, you and Dr. Fudge explained that the first step to your approach involves observations. Give us an example of the kind of classic systems engineering questions that would guide these observations.
[00:13:13] Speaker A: Yeah, so the first step is observations, which also happens to be the first step of the scientific method. However, where our approach goes beyond the scientific method is in that it provides some classic systems engineering based questions to really guide the observation process. So let me, let me give you some examples. So one is like, what does the system accomplish? In what context is the function accomplished? Like, what's the, what's the greater context of this system? And then third, like, how is the system architected, constructed to perform its functions and what are its subsystems? You know, how well does the system perform its function? What are the system's constraints and the external dependencies? Or what are the system interfaces? The external interfaces as well as the internal subsystem interfaces.
So these are, these are some of the core guiding questions that we want biologists to be thinking about when they're, you know, looking at a system. And then other questions getting more into the engineering aspects are like, what level of a hierarchical system are we looking at? You know, what are the impositions from the hierarchical layer that's above this, the system that you're focused on?
What are the requirements of that system and what predictions can we make, whether at that level or at a different level based on, you know, derived requirements? And this is getting into a little bit more of our methodology. But yeah, in biology, the observations really begin in the scientific literature. So that's where information, where other scientists report information about biological systems. And then from those observations, our methodology, you know, you make the observations, you learn what you can from the literature, and then it shifts quickly into a more traditional reverse systems engineering approach where we infer requirements from those observations and start building out a system model.
[00:15:23] Speaker B: Okay, yeah, makes a lot of sense. And I do like those questions because they're exploring with foresight, you know, what, what you would need to get to where you envision.
Now I noticed Your paper mentions requirements quite a lot. What are those and how are they used in engineering and in your specific approach?
[00:15:45] Speaker A: Yeah, so requirements are statements about what's necessary in a subsystem to accomplish the goal. So as we mentioned, stakeholders, they set overarching requirements in human engineering, right? They say, we want the, we want a cell phone to be able to hold a battery charge for at least 24 hours. That's like a requirement. Okay. Or they might say something like, we want a cell phone to be able to be dropped from five feet and not break. Those are kind of, those are sort of requirements.
Now, if you believe in a designer of nature, you might say that designer set the overarching requirements for biological organisms. And those would include what their roles are in the ecosystem, what they're doing. And then from those overarching requirements come requirements for organismal body plans. And from the requirements for body plans, then come requirements for tissues. And then from the requirements for tissues come requirements for cells. And then from the requirements for cells come requirements for proteins. And then from the requirements for proteins comes the genetic information for genes. And you kind of might notice that we're going down, right? We're going from like, what is the purpose, what is the goal of this organism? How is it going to function in this, in the ecosystem? And then we're going, the requirements under that are what dictate the subsystem and the components, what, what, what the components have to do to, to meet the overarching requirements.
[00:17:24] Speaker B: Okay, yeah, that makes a lot of sense. Well, the next part of the process then involves building a system model, which in your article you describe as sort of creating a map that sets relationships between objects and processes. Now, how is this step helpful? And, and what's the overall goal of the approach?
[00:17:43] Speaker A: So building a system model helps you organize and kind of formalize your observations. It, it forces one to think about how a system works and what the different role of that the components are playing in the system.
Now, object process methodology is a tool for doing this. And it was developed by a, a scientist, Dove Dory. And it's my, it's my tool of choice. I think it's super simple but profound. The very basis of it is to separate things into three types of entities, objects, processes, and states.
Objects exist, and then processes transform objects by generating or consuming or affecting them. So just as an example, if you were. And Dov Dorey gives this example in his, his introductory book, he says, you know, if you were modeling the human marriage relationship, you might have two, two Objects person one and person two. And then the process of marrying changes the states of those two objects from single to married. So that's just a little tiny insight into how this works. But you know, if you're a biologist that's never used this tool before, but you're interested, I'd say go to YouTube, they're a great tutorial, great basic tutorials on using OPM.
And then the OpCloud development environment is free for academic use. And you can use that to start building your biological models where, you know, the, the end goal here is really to build a model of how you think the system functions based on, you know, your gleanings from the scientific literature, your experiments and your requirements analysis. And then one aspect of system modeling that I find particularly appealing is its use of formal relationships and structured language. So once you're familiar with the tool, it actually becomes a lot easier to say, look at a completely different model and immediately identify connections between subsystems or constraints. Because the model, the, this modeling tool, you always have to use the same relationships and things when, when you're using it, no matter which system you're, you're modeling.
And this really offers a major advantage over what I call inconsistent and sometimes free form diagrams that we see in biology research papers where, you know, each tends to differ from the next. I, I used this example a while ago in a talk, but for the protein p53, there's like 15,000 papers and there's about that many different diagrams for how that protein works in, in these signaling networks. And you know, no matter how brilliant of a researcher you are, it's impossible to keep track of 15,000 papers of where each diagram is slightly different.
However, you know, if you're using similar, the same modeling tool, the same diagram, it's really, really, it's so it's standardized and that makes it easier to quickly understand what the other researchers have learned or what they're contributing. So I think it's really remarkable that while these modeling tools are standard in engineering, they're largely absent from biological training, despite having clear benefits for, you know, overcoming these inconsistency problems.
[00:21:08] Speaker B: Yeah, I can definitely see how it would help to reduce those inconsistencies and sort of standardize diagrams to make them more accurate. And you're saying they can be easily shared amongst researchers too? That's, that's another key benefit. Well, along the lines of my next question. How do you think biologists will respond to these suggestions?
[00:21:31] Speaker A: I think many biologists don't know what to picture when they hear the word modeling. When I was strictly in the biology biochemistry world, I didn't even know system modeling existed and I certainly didn't know what it was.
I think I'd explain what it is to biologists maybe in this way that systems modeling is like the last figure of your paper. It's, it's, it's your model of how you think the system works. And the benefit of using a tool like object process methodology and in the development cloud of, or in the development environment of OpCloud is that the diagrams will be consistent and that helps you over time more accurately describe the system.
And then the other thing is that having a qualitative system model is actually a key precursory step to quantitative modeling.
I think most biologists, when they hear the word modeling, they think about quantitative modeling. But you really need a good qualitative model before you can do quantitative modeling. So you got to get those basics right beforehand, otherwise the quantitative modeling just won't represent reality.
And so often, you know, when you're building this model in OpCloud, you're going to find knowledge gaps, you're going to come up with questions and that stimulates you to read the literature to see if you know, your questions have already been answered or, you know, if they haven't. Those are great research directions, experiments you can run in the lab, et cetera. And then once you find the answers, you can revise and update your model.
[00:23:07] Speaker B: Okay, now in your paper you, you do illustrate the utility of this approach with glycolysis as a case study. Now why is this a helpful way to demonstrate the usefulness of a systems engineering approach to biology?
[00:23:22] Speaker A: Sure. So, so we actually chose glycolysis because it's the first metabolic biochemical pathway that most students learn about. It's the most often memorized and probably the most well understood biochemical pathway. So it's a, it really is a great way to introduce the MBRSE approach.
[00:23:40] Speaker B: Yeah. And you point out that higher level system needs determine the architecture of the subsystems below them. Is this why it's important to study living things with a top down approach such as yours?
[00:23:52] Speaker A: That's right. In our paper we give this example that if we compare a Toyota Highlander car with a Toyota Corolla car at the organism level, we can observe that the Highlander seats more passengers, has a significantly greater, you know, interior volume, it has larger tires, it's reconfigurable to trade off passengers against cargo volume. And from those observations we can infer something about the architecture of this, of lower subsystems for example, the suspension. So the suspension of the Highlander, because it has, you know, it's. It's designed to carry more passengers, it needs to be heavier duty to support the greater weight, for example. And so that's one example of going top down. Now, when you're studying a lower level of a system, you might also run across evidence of a previously missed higher level requirement. So it can kind of go the other direction too, where, you know, if we return to the Highlander and Corollic example, we might notice that, you know, in. In analyzing these vehicles and others, that they share the same type of fuel interface. Right. They both have a spot where you can fuel them up, they both have gas tanks, etc. And that could lead us to hypothesize a broader ecosystem, let's say, requirement for, say, a standardized fuel system. Then if we zoom out and look at the ecosystem level, we might find that at gas stations, there's actually two fuel types, gasoline and diesel. And so then finding that insight, we would then adjust the original hypothesis to account for both fuel systems. So this can go both ways, down and up.
[00:25:39] Speaker B: Yeah. And that's a good example of. Of how observations can help you understand the requirements of the system.
[00:25:47] Speaker A: Yes, exactly.
[00:25:49] Speaker B: Well, this approach can be helpful in studying how living things work, as we've said, and how they originated, but it's also valuable when probing why they don't work as they should. In other words, disease and breakdown within the system, within living things. So you and Dr. Fudge propose a new hypothesis for the Warburg effect, which is a phenomenon observed in many cancer types. Can you tell us briefly what you're proposing and why this opens up a new important area for further investigation?
[00:26:19] Speaker A: Yeah, absolutely. So one key knowledge gap that came out of our analysis concerns this Warburg effect. And in case our listeners haven't heard of this before, it is a. The Warburg effect is a metabolic phenomenon that occurs in many, but not all types of cancer. And it's when glycolysis, which is sometimes called anaerobic metabolism, is used to primarily produce energy instead of oxidative phosphorylation, which is oxidative phosphorylation, is primarily used in the presence of oxygen.
So while the Warburg effect is often discussed in this context of cancer, our analysis using this methodology, this reverse systems engineering methodology, suggested that this phenomenon may actually be a normal physiological response.
And let me explain about this. So we anticipate that the Warburg effect is just a natural upregulation that happens during phases of development or injury repair or other things that require fast cell proliferation.
And that's because glycolysis, when it's upregulated, it can actually provide a higher rate of ATP generation. And we're getting into some weeds here in some details that you might not understand unless you know, this is your field. But glycolysis provides a higher rate of ATP generation, even though it's less efficient at making ATP per said glucose molecule.
So a high rate of glycolysis can deliver a really rapid. Can really rapidly deliver ATP as well as biomass precursors. And that's why we expect that it is upregulated under natural, normal conditions.
So just to kind of summarize here, you know, we, we hypothesize that this Warburg effect is actually a normal adaptive system response to either local system, like local injury, or maybe development or a different situation that requires rapid tissue growth.
[00:28:30] Speaker B: Okay, wow. That is a very compelling hypothesis and definitely a room for further research.
[00:28:39] Speaker A: Yeah. To do like an initial check of this, we, we looked in the literature and we found very few papers that mentioned anything about this, especially in the context of injury or tissue repair. So, yeah, we think it's a great place for more research to focus.
[00:28:58] Speaker B: Yeah. And a good example of the fruitfulness of this type of approach. Well, I read in your abstract that using this MBRSC methodology, you were able to derive 22 requirements, uncover five gaps in knowledge, and generate six predictions for the core metabolic pathway of glycolysis. We've talked a little bit about requirements. What's your favorite knowledge gap or prediction that your study led to?
[00:29:25] Speaker A: Sure, that's a great question.
My favorite, I think, has to do with making sense of the similarities and differences observed in glycolysis across organisms. So let me begin by giving a background of what we know regarding glycolysis in this. This regard. So glycolysis is often said to be a highly conserved pathway, but, you know, what does that mean? What's conserved? Is it the topology, meaning the overall metabolic sequence and the intermediates that are produced are the same? Or does conserved. When they say glycolysis is conserved, does this mean that the enzymes carrying out the chemical reactions are all exactly identical? And just so everyone understands, let me define topology again. So topology is the order of the pathway steps that lead from, say, glucose to pyruvate, and the intermediates that are produced in between those. Those points. So from observations across the ecosystem, many different organisms, we know that most use one of two topologies, and those topologies are called either the ED or the EMP topology.
And the EMP topology is the one that most people learn for glycolysis in high school biology textbooks. And that's the typical glucose goes to glucose, 6 phosphate, goes to fructose, 6 phosphate, blah, blah, blah.
So while most organisms use one or the other, very importantly the enzyme sequences are not well conserved. And historically the high degree of similar topology for the pathway made many people assume that the reason for this uniformity was because there was a common ancestor which had the same pathway and just passed passed it along. But this assumption really overlooks key system engineering reasons for the topology to be similar, which may actually better explain why these similarities why nearly all organisms either use the ED or EMP topology. So, so here we go, let me see if I can explain this. So the system engineering based reasons for for glycolytic pathway topology to be, to be uniform, the first is that the topology is optimal relative to other possible topological configurations for extracting energy from glucose. Now, many people don't know this, but the glycolytic pathway is actually highly optimized based on key metabolic constraints. For example, the architecture of the preparatory phase, which is the first part of glycolysis, where the molecule of glucose is prepared and then that's followed by a payoff phase. That structure is actually highly efficient based on kinetic and thermodynamic analysis. And then it's also been shown that the payoff phase has a maximally efficient throughput rate. Meaning like compared to many other alternatives, it's actually very, very efficient at moving a lot of flux metabolites through through it.
And then in 2010 it was demonstrated that the 10 step glycolytic pathway was actually minimally complex. Now that means that every single intermediate, those glucose, 6 phosphate, fructose, 6 phosphate, fructose, 1, 6 bisphosphate, all of those intermediates, they're either essential for building biomass precursors or they're essential for the ATP production aspect of glycolysis. So it, it turns out that glycolysis is Pareto optimized. That means you can't make any single thing better without making something else worse.
And in 2019 a group published a beautiful analysis of this by looking at over 10,000 possible routes between glycolysis and pyruvate. And they found that only that the two primary glycolysis variant pathways, ED and emp, are Pareto optimized to balance ATP production against biomass, while also minimizing what that what it costs the cell to build the proteins that carry out the reactions. So all that to say, the first systems engineering reason for glycolysis to have a uniform topology is that that topology is optimal given the constraints the pathway needs to meet.
[00:33:54] Speaker B: Okay. And so there's system engineering principles in action to show the top down approach of glycolysis, as opposed to just assuming that certain things came about through common descent and sort of a bottom up, you know, here's how it evolved over time approach certainly seems to be a more fruitful way to study this particular process. Okay, so that's a bit on how a systems engineering approach makes sense of glycolysis having these similarities. What about the differences across organisms for, for glycolysis? How does systems engineering make sense of that?
[00:34:31] Speaker A: Yeah, so the, the differences in the glycolytic enzyme or transporter sequences amongst organisms is probably due to the lower subsystems of those organisms having different requirements and constraints. And so in our paper we discuss the example of mammalian glucose transporters. And there's 14 different subtypes, only four of which are well characterized. Of those four, each seems to play a unique role in system level glucose control. So like the liver isoform is doing something different than the brain isoform and the muscle isoform. Thus, evidence is really accumulating that differences in glucose transporters which you see at the gene level are explainable by tissue specific requirements. And this makes sense why differences between glycolytic enzymes themselves are poorly correlated with common ancestry. And because the correlation is actually so poor core, there has been complete dismissal of the previous assumption that the pathway had a single evolutionary origin. And I want to emphasize here that, you know, that this complete dismissal is not my statement, but is published in a paper by Revis, Becerra and Lascano in 2018, so. And you can check out my blog to more specifically if you want to track down that reference. But to wrap this up here, evidence continues to accumulate that glycolytic enzyme differences between organisms play functional roles due to unique subsystems in which those enzymes work.
[00:36:16] Speaker B: Wow. Well, glycolysis is, as you say, a useful and pretty fascinating case study as applied to your paper here. Yeah, I think it's a good way to, to help biologists understand the fruitfulness of this approach. Well, final question for you today. Do you think a model based reverse systems engineering approach will allow more biologists to see the explanatory power of intelligent design as an explanation for living things?
[00:36:43] Speaker A: I do. I think that a methodology which improves interrogating complex systems based on tools from, you know, the design based Field of engineering is a great example of how ID can help biologists do better science. Our work introduces new perspectives and approaches into this field that often acknowledges being stuck when it comes to system understanding and trying to figure out why biological systems are the way that we observe them to be. And my favorite way that our paper illuminates design is by by highlighting that the similarities and differences between glycolysis across organisms are well explained by system engineering principles like optimization and interface constraints. There isn't need to invoke the they are all the same because, you know, the last universal common ancestor had them and pass them down. So I'll conclude by, you know, noting that many people who don't share my worldview also strongly support cross disciplinary research between engineering and biology. And it's great, right? We approach this from different perspectives and but in the end we're all supporting science. And so I, I just believe that philosophically the design worldview actively encourages this kind of cross disciplinary research, whereas I'm not convinced that can be said for a materialistic worldview.
[00:38:08] Speaker B: Yeah, yeah, agreed. More, more perspectives equal a greater view of a particular topic and I think you're proposing that. Well, Emily, this has been a fascinating discussion and sort of diving into this methodology that you have helped to bring about. I want to thank you for unpacking that for us. Now you mentioned a blog where, where can listeners and viewers turn for for more on this?
[00:38:34] Speaker A: Sure, yeah. So on evolutionnews.org if you go there, click on the writers tab and then you'll see my name and picture. I blogged about this paper there. You can, you can view that as well as see my other articles.
[00:38:48] Speaker B: Yeah, that's all@ovolutionnews.org, a super resource for following the debate and the evidence for intelligent design. And you will definitely find Emily's writing there. And if you liked what you heard on today's episode, feel free to share with a friend. Enlarge the perspective of a family member or associate, someone you know who is into science but might be following a reductionist framework in their thinking that might be able to benefit from this new and exciting fruitful approach. Well, for ID the future, I want to thank you for watching and listening. I'm Andrew McDermott, this is Dr. Emily Reeves.
Visit us@idthefuture.com and intelligent design.org this program.
[00:39:34] Speaker A: Is copyright Discovery Institute and recorded by its center for Science and Culture.
