貝圖爾·卡查爾：生命起源、古 DNA、胚種論與外星人

Lex Fridman Podcast #350 – Betül Kaçar: Origin of Life, Ancient DNA, Panspermia, and Aliens Machine transcription by Qwen3-ASR 0.6B (MLX, local, Apache-2.0). May contain recognition errors. The following is a conversation with Batul Kachar, an astrobiologist at University of Wisconsin studying the essential biological attributes of life. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We got House of Macadamias for a satiating and delicious snack, Mizanameen for style, A Sleep for naps, ExpressVPN for privacy and security, Give Directly for philanthropy and Blinkist for nonfiction. Choose wisely, my friends. And now onto the full ad raids. As always, no ads in the middle. I try to make this interesting, but if you skip them, please still check out our sponsors. I enjoy their stuff, maybe you will too. This show is brought to you by House of Macadamias, a company that ships delicious, high quality and healthy macadamia nuts directly to your door, which they did just today. Another shipment. I got in my hand an unopened box dipped macadamias chocolate flavor. This is the one I went through first last time, and it was delicious. It is an incredible source of happiness for me. I knew for a long time that macadamia nuts were really healthy because I remember when I was first exploring. Okay, so on keto, what is the way to get fats into the diet? Because that's to be a high fat diet. And I was looking at nuts and I was thinking of it. Okay, which which nuts are the healthiest? And I remember macadamias always coming up to the top of the list. I'm going to try not to eat all of them so that I have some for guests when they come over. Because it's like a perfect guest snack, it's healthy, is delicious, is just pure perfection. They also sent me some macadamia bars, which were also delicious last time. I probably prefer the whole nuts. I'm nuts for nuts. Anyway, go to houseofmacadamias dot com slash lex to get 20 off your first order. This show is also brought to you by Mizanameen, they make of comfortable stylish dress shirts and other menswear. I wear their black dress shirt and I love it. I have a ton of them. Sometimes when I do these ad raids, I struggle to try to convey exactly why I like this particular piece of clothing. Which is where I wish there's like a brain computer interface way to directly communicate the actual experience of wearing the clothing. And if you get a large number of people with that data, you get a sense of okay, well 96 percent of people feel good in this. That's a good chance that I will also feel good and that's fascinating. Of course, you could get that through self report surveys, but it will be. Nice to get direct data because you can't lie, but the feelings don't lie. But then again, there could be other kinds of variables like what else is going on in your life. You might be just in a bad mood about something else or watch a really sad movie while wearing the shirt. So I don't know. It's an imperfect system that we're going to work out right now. You get 35 off any regular price order when you go to mizanameen dot com and use promo code lex. This episode is also brought to you by A Sleep and its new Pod three mattress. They've been a sponsor forever, and they've been my bed mattress cooling system when I sleep forever, and it's source of a lot of happiness. I recently tweeted about the number of hours there are in a regular lifetime. You know, if you live 70 to 80 years old, there's about 600 to 700 thousand hours in a life. And I think a large percent of those hours is spent sleeping. Of course, you can think of sleeping as a important process for health to regenerate your energy and all that kind of stuff. But it is also in itself a source of happiness. And I think you want to use the best bed for the job. There's just not a better feeling of a cool surface with a warm blanket. And a a day full of battles won, and you return to rest and reminisce about the battles. So you can again the next day return to the battles that are in store for you. Check it out and get special savings when you go to a sleep dot com slash lex. This show is also brought to you by ExpressVPN. I've used them for many years. They've had the big sexy button that just works when you press it, and I use it. You know it's the first layer of protection of your data of your privacy on the internet ISPs and basically a lot of different services and tools. Collect your data and a good VPN is should be your first layer of protection in those situations in most situations. You know you can also change your location in the world. Now this is useful for different services like Netflix and so on. But mostly I just like it from a software engineering design perspective. I love tools that just work. They do one thing and they do it really well and they do it across different platforms. They never break down. Everything just works. Everything's intuitive from the UI perspective and from the backend perspective. Everything's just works smooth. Android, Linux, everything. I love it. Go to expressvpn dot com slash lex pod for an extra three months free. This show is also brought to you by Give Directly, a nonprofit that lets you send cash directly to people in need. It's perhaps a counter intuitive notion, but the giving of money to alleviate some of the suffering in the world or to improve the quality of life for a bunch of people in the world. Is not an easy problem. I mean, there is so many challenges, and there's just a lot of studies behind the principles that Give Directly operates under, which is if you actually give money to the people most in need, they're going to themselves. Spend that money to maximally improve their life. This too is a counter intuitive notion, but it works time and time again. Like many things, have so much to learn in this world about challenging problems like this. So philanthropy is definitely one of them. So I should also mention that a bunch of guests previous guests of this podcast have used Give Directly to donate money. Jack Dorsey, Elon Musk, Vitaly Buterin and and several others. Visit givedirectly dot org slash lex to learn more and send cash directly to someone living in extreme poverty. That's givedirectly dot org slash lex. This show is also brought to you by Blinkist, my favorite app for learning new things. Blinkist takes key ideas from thousands of nonfiction books and condenses them down into 15 minutes that you can read or listen to. I set the goal of reading one book a week. I set a classic book, but really what I mean is a book that really challenges you and really makes you think. So I look forward to seeing sort of using Blinkist to help me review the books, actually also choose the books and maybe help others that are interested in following along to with my reading to get deeper and deeper insights into these different books. I think my perspectives on many books I read are non-standard. I get lost on a single quote here and there, and it just kind of takes me down this whole tangent of thought. These ideas that get inspired and get sparked, and then I I just construct this whole world from a single quote sometimes. But that's what makes it interesting. It's a very subjective experience like appreciating art, appreciating books and so on. Anyway, you can claim a special offer for savings and blinkist dot com slash lex. This is the lex Friedman podcast to support it. Please check out our sponsors in the description. And now dear friends, here's Batul Kachar. What is the phylogenetic tree or the evolutionary tree of life and what can we learn by running it back and studying ancient gene sequences as you have? I think phylogenetic trees could be one of the most romantic and beautiful notions that can come out of biology. It shows us a way to depict the connectedness of life and all living beings with one another. It itself is an ever evolving notion. Biologists like visualizations. They like these graphics, these diagrams, and tree of life is one of them. So the tree starts at common ancestor. It's actually the other way around. It starts from at the end. It starts from the from the branches. It starts from the tip of the branch actually, and then the further depending on how what you collect it. To build the tree, so depending on the branches, depending on what's on the tip of the branch, and I will explain what I mean. The root will be determined by what is really sitting on the tip of the branch of the tree. So we could study the leaves of the tree by looking at what we have today and then start to reverse engineer, start to move back in time to try to understand what the rest of the tree, what the roots of the tree look like. Exactly. So the tree itself by just taking a few steps back and looking at the entire tree itself can give you an idea about the connectedness, the relatedness of the organisms or whatever again you use to create your tree. There are different ways, but in this case I'm imagining entire diversity of life today is sitting on the tips of the branches of this tree. And we look at biologists, look at the the tree itself. We like to think of it as the topology of the tree to understand when certain organisms or their ancestry may have merged over time. Depending on the tools you use, you might use this tree to then reconstruct the ancestors as well. And so what are the different ways to do the reconstruction? So you can do that at the gene level, or you could do it at the higher complex biology level, right? So what what in which way have you approached this this fascinating problem? We approached it in every way we can. So it's the gene could be protein, the product of the gene or species, or could be even groups of species. It will depend. It totally depends on what you want to do with your tree. If you want to understand certain past events, whether an organism exchanged a certain DNA with another one along the course of evolution, you can build your tree accordingly. If you rather use the tree to reconstruct or resurrect ancient DNA, which is what we do. Then in our case, for instance, we do both gene protein and species because we want to compare the tree that we create using these different information. Okay, well let me ask you the ridiculous question then. So how realistic is Jurassic Park? Can we study the genes of ancient organisms and can we bring the those ancient organisms back? So the reason I ask that kind of ridiculous sounding question is maybe gives us context of what we can and can't do. Yeah, by looking back in time. Yeah, so dinosaurs or all these mammals in in at least for us is the exciting thing already happened by the time we hit to the larger organisms or to eukaryotes. Oh, to you the fun stuff is before we got to the mammals. The fun stuff is what people think is boring. I think the the phase that well these two different times in the geologic history one is the first life past origin of life how did first life look like. And the second is why do we think that over certain periods of geologic time, no significant innovation happened to the degree of leaving no record behind. So what do we not have a record of? Which which part? So you say the fun stuff to you is after the origin of life, which we'll talk about after the origin of life. There are single cell organisms. The the whole thing with the photosynthesis, the whole thing with eukaryotes and multicellular organisms and what what else is the fun stuff? The whole oxygen thing, which mixes in with the origin of life. There's a bunch of different inventions all they have to do with this primitive kind of looking organisms that we don't have a good record of. So I will tell you the more interesting things for us. One is the origin of life or what happened right following the emergence of life. How did the first cells look like? And then pretty much anything that we think shaped the environments and was shaped by the environments in a way that impacted the entire planet that enabled you and I to have this conversation. We have very little understanding of the biological innovations that took place in the past of this planet. We work with a very limited set of I don't want to even say data because they are fossil records. So let's say imprints either that comes from the rock and the rock record itself, or what I just described these trees that we create and whatever we can infer about the past. So we have two distinct ways that comes from geology and biology and they each have their limitations. Okay, so right so there's an interplay. The geology gives you that little bit of data, and then the biology gives you that little bit of kind of constraints and the materials you get to work with to infer how does this result in the kind of data that we're seeing. And now we can have this through the fog. We can see we can look back hundreds of millions of years, a couple of billion years and try to infer. Even further and and I like that you said fog it is pretty foggy, but we're and it gets fogier and fogier the the more you the further you try to see into the past. Biology is you you basically study with study the survivors broadly speaking and you're trying to pitch the sort of put together their history based on whatever you can recover today. What makes biology fascinating also let it it erase its own history in a way right. So you work with this four billion year product that's genome that's the DNA. It's great. It's a very dynamic, ever evolving chemical thing, and so you will get some information, but you're not going to get much unless you know where to look. Because it is responding to the environment. Yeah, so what we have is fascinating. What we have is the survivors, the successful, successful organisms, even the primitive ones, even the bacteria we have today. So bacteria is not primitive and we. Sorry, sorry to offend the bacteria. It's we should be very grateful to bacteria. First of all, they are our great great ancestors. I like this quote by Douglas Adams: Humans don't like their ancestors. They rarely invite them over for dinner, right? But bacteria is in your dinner. Bacteria is in your gut. Bacteria is helping you along way. We might well they get themselves invited in a way. Yes, and so we and they're definitely older and and definitely very sophisticated, very resilient than anything else as someone working at. As a as a bacteriologist, I feel like I need to defend them in this case because they don't get much shout out when we think about life. So you do study bacteria. So which organisms gives you hints that are alive today that give you hints about what ancient organisms were like? Is it bacteria or viruses? What do you study in the lab? We study variety of different bacteria depending on the questions that we ask. We engineer bacteria. So ideally, we want to work with bacteria that we can engineer. Seldom we developed the tools to engineer them, and it depends on the question that we are interested in. If we are interested in connecting the biology and geology to understand the early life and and fundamental innovations across billions of years, there are really good candidates like cyanobacteria. So we we use cyanobacteria very frequently in the lab. We can engineer its genome. We can pervert its function by poking its own DNA with the foreign DNA that we engineer in the lab. We work with E. coli. It's the most simple in in terms of model systems go goes organism that one can study well established sort of a pet lab pet that we use it a lot for cloning and for understanding basic functions of the cell given that it's really well studied. So and what you do with that E. coli? You said that you injected with foreign DNA. We inject pretty much all the bacteria that we work with with foreign DNA. We also work with diazotrophs. These are azotobacteria. They're one of the prime nitrogen fixers nitrogen fixing bacteria. Can you explain what that is nitrogen fixing? Is that is the source of its energy? So nitrogen is a triple bond gas that's pretty abundant in the atmosphere, but nitrogen itself cannot be directly utilized by cells given it is triple bond. It needs to be converted to ammonia that is then used for the downstream cellular functions. And that's what counts as nitrogen fixing. Yes, so nitrogen needs to be fixed before our cells can make use of it, and and it's no offense to nitrogen either. Well, it's actually a very important element. It's one of the most abundant elements on on our planet that is used by biology. It's in ATP. It's in chlorophyll that uses that relies on nitrogen. So it's a very important enzyme for a lot of cell functions. And there's just one mechanism that evolution invented to convert it is. So far we know there's there's only one nitrogen fixation pathway as opposed to say carbon you can find up to seven or eight different carbon based microbes invented to fix carbon. That's not the case for nitrogen. It's a it's a singularity across geologic time. We think it evolved around 2.7 maybe roughly three probably less than three billion year old billion years ago, and that's the only way that nature invented to fix the nitrogen in the atmosphere for the subsequent use. Would we still have life as we know today if we didn't invent that nitrogen fixing step? I cannot think of it. No, it's it's it's essential to life as we know you you and I are having this conversation because life found a way to fix nitrogen. Is that one of the tougher ones if you put it sort of oxygen. Nitrogen, carbon. What are in terms of being able to work with these elements? What is the hardest thing? What is the most essential for life? Just to give context. Well, we think of this as the cocktail. You may hear. What's in the cocktail? It's the schnapps, right? Carbon, hydrogen, oxygen, nitrogen, sulfur. So there are five elements that life relies on. We don't quite know whether that's the only out of many options that life necessarily needs to operate on, but that's just how it happened on our own planet. And there are many abiotic ways to fix nitrogen, and like lightning, right? Lightning can accumulate ammonia. Humans found a way about 100 years ago, I think around World War One, the Haber Bosch process that we can abiotically convert nitrogen into ammonia. Actually, 50% of the nitrogen in our bodies comes from the human conversion of nitrogen to ammonia. It's helped. It's the fertilizer that we use urea comes from that process. It's it's in our food, so we helped we found a way to fix our own nitrogen for ourselves. Yeah, but that you know that's way after the original invention of. Oh, absolutely, absolutely, and without that, we wouldn't have. We wouldn't have all the steps of evolution along the way. Oh, absolutely, it's we tried to replicate in the most simplest way what nature has come up with. Right, we do this by taking nitrogen using a lot of pressure and then generating ammonia. Life does this in a more sophisticated way, relying on one single enzyme called nitrogenase. It's the nitrogen that is used together with eight electron donor and ATP together with a lot of hydrogen. Life pushes this metabolism down to create fixed nitrogen. It's quite remarkable. So the lab pet E. coli inject them with DNA. Those are so E. coli does nitrogen fixing in part, or is that was that a different one? So some biological engineers engineered E. coli to fix nitrogen. I believe not not us. We use the nature's nitrogen nitrogen fixing bug and engineer it with the nitrogen fixing metabolism that we resurrected using our computational and phylogenetic tools. How complicated are these little organisms? What are we talking about? Depends on how you define complication. Okay, so I could tell that you appreciate and respect the full complexity of even the most seemingly primitive organisms because none of them are primitive. Okay, that said, what what kind of what what are we talking about? How how. What kind of machinery do they have that you are working with when you are injecting them with DNA? So I will start with one of the most fascinating machinery that we target, which is the translation machinery. It is a very unique subsystem of cellular life in comparison to I would say metabolism. And we used to you know when we are thinking about cellular life, we think of cell as the basic units or the building block. But from a key perspective, that's not the case. That one may argue that everything that happens inside the cell serves the translation and the translation machinery. There is a nice paper that called this that entire cell is hopelessly addicted to this main informatic. Computing biological chemical system that it is operating at the heart of the cell, which is the translation. It is the translation translating from what to what. So RNA to enzymes. It converts a linear sequence of mRNA into a folded later folded protein. That's that's when the that's the core processing center for information for life. It's has multiple steps. It initiates, it elongates, it terminates, and it recycles. It operates discrete bits of information. Its itself is like a chemical decoding device, and that is incredibly unique for translation that I don't think you will find anywhere else in the cell that does this. So even though it's called translation, it's really like a factory that reads the schematic and builds a three dimensional object. It's like a printer. I would divide it into actually even four more additional steps or disciplines than what would it take to study it by the way you described it. It's a chemical system. It's the compounds that make it up are chemicals. It's physical. It tracks the energy to make its job to do its job. It's informatic. What is processed are the bits. It's computational. The discrete states that the system is placed when the information is being processed that's itself is computational and it's biological. It said there is variability and inheritance that come from imperfect replication even an imperfect computation. So you're and that's so good. So from the biology comes the like when you mess up the bugs of the features that's the biology. Informatics is obvious in RNA that's a set of information there. The different steps along the way is actually kind of what the computer does with with bits. It's own computation, physical. There's a. I guess like almost like a mechanical process to the whole thing. It requires energy and actually you know it's manipulating actual physical objects. And chemical is because you're. Have to ultimately it's all chemistry. Yeah, and it tracks this information, so it is almost a mini computer device inside our cells. Yeah, and that's the oldest computational device of life. It's it's likely the key operation system that had to evolve for life to emerge. It's more interesting or it's more complicated in interesting ways than the computers we have today. I mean everything you said which is really really nice. I mean I guess our computers have the informatic and they have the computational but they don't have the chemical the physical or the biological. Exactly and and the computers don't have don't link information to function. Right, they are not tightly coupled. No, are close to what translation and or the way translation does it. So that's the number one I think difference between the two. And yes, it's it's informatic and we can discuss this further too. One hundred percent. Let's please discuss this further. Which part are we discussing? For each one of those are fascinating worlds. Each each of the five. Yeah, so well we can start with the more I guess the the ones that are more established which is the the chemical aspect of the translation machinery. It's the specific compounds make up the assembly of RNA. Chemists showed this in many different ways. We can rip apart the entire machinery. We know that at the core of it there's an RNA that that operates not only as an information information system itself or information itself but also as an enzyme. And and origin of life chemists make these molecules easily now we know we can manipulate RNA we can make even single pot chemistry is we can create compounds. What's a single pot chemistry? That's I would say when you add all the recipes that you know that will lead you to the final product. This is what original lab chemists do is they they come up with this pot they throw a bunch of chemicals in and they try to try to they're basically chefs of a certain kind. I'm not sure if that's what they call it, but that's how I think of it because it is all combined in a test tube and you know the outcome and and it's it's very mathematical. Once you know the right environment and the right chemistry that needs to get into this container or this pot, you know what the outcome is. There is no luck there anymore. It's a pretty rigid, established input output system and it's all chemistry. So you actually wear a lot of hats as one of them, origin of life chemists. My PhD is in chemistry, but I don't do origin of life chemistry. But you're interested in origin of life. Yes, absolutely. So some of your some of your best friends origin of life chemists. Just make sure that you have good chemist friends if you're interested in origin of life. Yeah, that's a hundred percent required. It should be mandatory. Okay, so chemistry. So what else about this machinery that we need to know chemically? Well, chemically I think that's it. You have enzymes, you have proteins. Enzymes are doing their thing. They know how to chew energy using ATP or GTP. They they they know what to do on their in their own way. They do their enzymatic thing. So it's not just the the ribosome that is at the heart of the translation, but there are a lot of different proteins. You're looking about 100 different components that compose this machinery. Well, let me ask kind of maybe it's a ridiculous question, but did the chemistry make this machine or did the machine use chemistry to achieve a purpose? So like, um, I guess there's a lot of different chemical possibilities on Earth. Is is this translation machinery just like choosing picking and choosing different chemical reactions that it can use to achieve a purpose? Or did the chemistry basically like there's like a momentum like a constraint to the thing that can only build a certain kind of machinery? That's basically is is is chemistry fundamental or is is just emergent? Like how important is chemistry to this whole process? You cannot have life without chemistry. You cannot have any cellular process without chemistry. What makes life interesting is that even if the chemistry is imperfect, even if there are accidents along the way, if something binds to another chemical in in a way it shouldn't, there is resilience within the system that it can maybe not necessarily repair itself but it moves on however imperfect mistakes can be handled. That's where the biology. That's where the biology comes in, but in terms of chemistry, you absolutely cannot have a translation machinery without chemistry. And so you you as I said there are four main steps. These are the core steps that are conserved in all translation machinery. And I should say all life has this machine, right? Every cell, everything on Earth, on Earth. Yeah, yes. When you think of this machine, do you think very specifically about the kind of machinery that we're talking about, or do you think more philosophically a machine that converts information into function? It's I cannot separate the two. I think what makes this machinery fascinating is that those five components that I listed are they coexist. So for instance, if we let's just talking about the chemistry part, we we know the certain rate constant all these proteins that operate in this machinery needs to harbor in order to get the mechanism going. Right, if you are bringing the the information to the translation machinery and you are the initiator of this computation system, you need to have you can only afford a certain range of mistakes. If you're too fast, then the next message cannot be delivered fast. If you're too slow, you may stall the process. So there is definitely a chemistry constant going on within the machinery. Again, it's it's not perfect far from it, but they all have their own margin of error that they can tolerate versus they cannot otherwise they collapse the system collapses. So it's like a jazz ensemble, the notes of the chemistry, but you can be. I like that you said jazz. It's definitely true. It's a party and it's like everybody is invited and and and they need to operate together, right and and they what's really cool about it I think there are many things that are very interesting about this thing but if you take if you remove it from the cell and put it in a cell free environment it works just fine right so you can get cell free translation systems put this translation in a test tube and it is doing its thing it doesn't need the rest of the cell to translate information of course you need to feed the information at least so far but because we are far from evolving a translation maybe not so far evolving a translation in the lab or a machine that can process information as it generates it we have not done that yet. It's a pretty complicated machinery. It's hard for for those origin of life chemists to find a pot that generates because it's far more than chemistry. You need you need biology obviously you need biochemistry you need to think as I think a network systems folk you need to think about computation you need to think about information and and that is not happening yet except we are trying to bring this perspective but the more you understand how information systems work you cannot once you see it you cannot unsee it it's one of those things. So but you can still rip it out and the chemistry happens. Yes, and chemistry can happen even if even if you strip some of the parts out. It can you can get very minimal level of information processing that does not look anything like the translation that cells rely on but that but chemists showed from linear you can generate information that arrives to a processing center in the form of a linear polymer. The informatic part of this system that I think sets the part from computation and from metabolism comes in if you think about the information itself right so we have four nucleotide letters that compose DNA and they are processed in the translation in triplets. So you have in in triplet codon fragments. So you have four times four times four. So you have 64 possible states that can be encoded by four letters in three positions. All right, so it's so amazing. Yeah, it's so amazing. There is only one code that says start. That's that there is only one, and there is two if not three that says stop. So that's that's that's what you work with, but you can have 64 possible states, but life only uses 20 amino acids. So we use life uses 64 possible states minus four of the starts and stops to code for 20 amino acids in different combinations. That is really amazing. If you think about there, there are 500 different amino acids life can choose. Right, it's narrowed it down to 20. We don't know why a lot of people think about this genetic code is quite fascinating. It's all right. I mean it didn't do it for 4 billion years. I don't know. We may wait for another 4 billion years, but but you didn't have those amino acids in the very beginning, right? Like we don't know. So we we would be fooling ourselves if we said we know exactly how how many amino acids existed early on, but there is no reason to think that it it wasn't the same or similar. Yeah, we don't we don't have a good reason, but but because roughly 20 out of 60 states are used, you're using one third of your possible states in the in your information system. So this may seem like a waste, but informatically it's important because it is abundant and it is redundant, right? So so this code degeneracy you see this in that's implemented by this translation machinery inside the cell. So it means we can make errors, right? You can make errors, but the message will still get through. You you can speak missing some letters to the information can miss some parts, but the message will still get through. So that two thirds of the not you states gives you that robustness and resilience within this system. So at the informatic level, there's room for error. There's probably room for error, probably in all five categories we're talking about. There's probably room for error in the computation. There's probably room for error. Yes, exactly. Everywhere there's room for error because because the this informatic capacity is made possible together with the other components and not only that but also the the product yields. Function no, in this case enzyme or protein, right? So so that's really amazing for me. It is. I mean, I mean, in my head, just so you know, because I'm a computer science AI person, the the parallels between even like language models that encode language. Or now they're able to encode basically any kind of thing, including images and actions, all in this kind of way. The the parallel in in terms of informatic and computation is just incredible. Actually, I have a image. Maybe I can send you. Can we pull it up now? If you just do genetic codon charts, we can pull that up. Yeah, it's a very standard table. So I can I can explain what why this is so amazing. So you're looking at like this is life's alphabet, right? And so I also want to make a very quick link now to your first question: the tree of life. When when we link when we try to understand ancient languages, right or the cultures of the or the cultures that use these extinct languages, we start with the modern languages, right? So we look at Indo European languages and and try to understand certain words and make trees to understand you know this is what a Slavic word is for snow something like snake. Now we jump to languages that humans spoke. Human history exactly. So we make trees to understand what is the original ancestor. What did they use? To say snow, and if you have a lot of cultures who use the word snow, you can imagine that it was snowy. That's why they needed that word. It's the same thing for biology, right? If if they have some, if we understand some function about that enzyme, we can understand the environment that they lived in. It's it's the similar, it's similar in that sense. So now you are looking at the alphabet for of life. In this case, it's not twenty or twenty five letters. It's you have four letters. So what is really interesting that stands out to me when I look at this. On the outer shell, you are looking at the twenty amino acids that compose life, right? The one, the methionine that you see, that's the start. So the start is always the same. To me, that is fascinating. That all life starts with the same start. There is no other start code. So you send the AG, you know AUG to the cell. That when that information arrives, the translation knows, all right, I gotta start. Function is coming. The following this is a chain of information until the stop. Code arrives, which are highlighted in black squares. So for people just listening, we're looking at a standard RNA colored table organized in a wheel. There is an outer shell and there is an inner shell, all using the four letters that we're talking about. And with that, we can compose all of the amino acids. And there is a start and there is a stop, and presumably you put together. The the with these letters you walk around the wheel to put together with the words the sentences that. The words the sentences and. Um, you again you get one start, you get three. There are three different ways to stop this. One way to start it, and for each letter you have multiple options. So you say you have a code A. The second code can be another A, and even if you mess that up, you still can rescue yourself. So you can get a for instance, I am looking at the lysine K. You get an A and you get an A and then you get an A that gives you the lysine. Right, but if you get an A and if you get an A and I get a G, you still get the lysine. So there are different combinations. So even if there is an error, we don't know if these are selected because they were erroneous and somehow they got locked down. We don't know if there is a mechanism behind this to or we. We certainly don't know this definitively, but this is the informatic part of this. And notice that the the colors in some tables too, the colors will be coded in a way that the the type of the nucleotides can be similar chemically. But the point is that you will still end up with the same amino acids or something similar to it even if you mess up the code. Do we understand the mechanism how natural selection interplays with this resilience to error? So which errors result in the same output like the same function and which don't. Which actually results in a dysfunction or which are. We understand to some degree the how translation and the rest of the cell work together. How an error at the translation level this is the really core level can impact entire cell. But we understand very little about the evolutionary mechanisms behind the selection of the system. It's thought to be one of the hardest problems in biology and it is still the dark side of biology. Even though it is so essential. So this is yeah you are looking at the language of life so to speak and how it can found ways rather to tolerate its own mistakes. So the entire phylogenetic tree. Can be like a deconstructed with this wheel of language. Because all the final letters, those are that's the twenty amino acids that's our alphabet. They are all brought together with these bits of information, right? So you when you look at the genes, you are looking at those four letters. When you look at the proteins, you are looking at the twenty amino acids. Which may be a little easier way to track the information when we create the tree. So using this language, we can describe all life that's lived on Earth. One perspective. It's we are not that good at it yet, right? So in theory, this is one way to look at