Boom, what's up everyone? Welcome to Simulation. I'm your host, Alan Sokian. Super excited to be talking about cell-free molecular discovery. Very excited to have Dr. Lewis Metzger joining us on the show, hello. Really excited to be here, thanks Alan. Thanks so much for coming on. Very pumped, very pumped. This is the edge of biotech. We're gonna be talking about that. We're gonna be talking about your journey. Very excited for those that don't know Lewis's background. He initially did a PhD at Duke in biochemistry. Then he went on into postdoc at UCSF. Then he became a principal scientist at Novartis for about six years, discovering novel antibiotics. Then most recently he's been a year with as the chief scientific officer at Tierra Biosciences where he's doing cell-free synthetic biology, searching for molecules. Very, very excited to be talking about this. And so much more. You know, there's so much more in the background as well. It's all in the bio. Okay, Lewis, we'd like to start things off with this big history perspective on civilization. What is your current take on the synthesis of the human experiment? Well, I mean, these are the best of times in some ways and they're scary and dangerous times in other ways. But I would say we're in an interesting place because I don't think that humanity has fundamentally changed in its desires and motivations for thousands of years. But our technology has certainly changed. And one perspective I have is that we're at the point where we can precisely engineer biology. Now, to be clear, humanity has been engineering its world for millennia.  And also doing genetic experiments with low precision. So look at all the breeds of dogs that we have today. You know, that is the result of human selection and, you know, engineering of biology, as it were, and farm animals and crops and everything else. So, but now we're at this point, everyone, many people have heard about CRISPR and numerous other technologies that allow us to precisely engineer biology. And I think that's going to be the hallmark of the coming century. I think if we had, you know, an industrial revolution, a silicon revolution, those revolutions have set up a biological revolution. And I think that this is an interesting time to live. And at the same time, I think that we, humanity oscillates, especially with modern communications, between being pulled closer together in some ways and, you know, other forces trying to move people into tribe. So it's easier to find your tribe on social media or in the workplace or wherever you look for your tribe. And it's easier to communicate with people than ever. But in many ways, perhaps because of that, we're more isolated than ever in some ways and more connected than others. So it's all these, you know, these opposites that we have make this time really interesting. And I hope that humanity can traverse this period and find sort of a humanistic outcome for people. For instance, all of this automation that we see in biology and in so many other areas does eventually eliminate certain types of jobs, but it creates others. And is humanity going to reach some sort of technical singularity where we can take care of everyone, give people a good quality of life?  Is it freedom to be creative, to have that sort of most limiting resource time? So anyway, my take on humanity is that we can engineer everything better and better. We're entering a time where, you know, we reach a point where humanity can probably provide for its needs. If it finds a way to do so, that doesn't leave anyone behind. And so I think that's the risk that goes along with the benefit of how we're evolving. This is a $100 trillion opportunity as we've been circulating in the media now with the IndieBio article and stuff. It's very exciting what biotechnology and the engineering of everything that our world actually is made of can potentially bring in civilizational flourishing. It's super exciting. I'm glad you really are so passionate about that. And we're gonna break down a lot of these regards in terms of like the foundation of scientific knowledge. We're gonna talk about that later as well. I'm really excited. Okay, take us down the, kind of like you're a little bit of on the childhood what got you into biotech, what got you into what you were doing in Duke and whatnot, take us through that. So I don't know some of the people who are watching this may have read a book by Oliver Sacks. I think it's called Uncle Tungsten. And among other things, it makes the point of how childhood chemistry sets can be useful. For me, it was actually a kid's microscope set that my parents bought me for, I don't know, my fifth or sixth or seventh birthday, long time ago or so it seems. And they're not scientists, my parents, but they're very bright people who had a great love of nature. And we grew up in the desert Southwest. So we went on hikes. We really were encouraged to explore. And I think that that wet my appetite for, not only could we observe interesting things through telescopes on the sort of the large end of the scale, but we could observe things that were invisible under microscopes and there was all this biology and chemistry happening all around us that we can't even see without special equipment. And so I think that microscope did that for me. But in addition to that, I went to a public high school. I really, really had a good time there. It was Cibola High School in Albuquerque, New Mexico. I had two, I have many teachers who influenced me quite a bit. And one was Bonnie Crumpler, a biologist and really got me interested in genetics and how genes made proteins or encoded proteins and how those did chemistry. And I really liked chemistry in those original classes that I took, so I thought, well, can I find a career or a source area of study that combines biology and chemistry and so biochemistry does although it means so many different things ranging from molecular biology to mainly enzyme catalyzed reactions and everything in between. And so that was a direction I wanted to go. In undergrad, I considered being a pre-med but really stuck with the trajectory towards research science. I don't think you'd want me to be your physician because I might say, oh, can we leave that control tumor in to see how everything's working? So medical work was probably not what I was cut out for but I wanted to have an impact and engineering biology, studying biochemistry is a way to do that. So it's kind of where I've come from and what got me started. This is a reoccurring theme for the guests that we feature on the show is that there is a mentor or influence of sorts all the way from the childhood biochemistry sets, microscopes, et cetera, all the way to your biology teacher in high school also being a major catalyst for your interest as well. This is really important. These are the ways that we can help unlock the full potential in the youth across the world is to have these mentorships, have these risks, these opportunities, these doors open up for people, have them really pursue them. Okay, let's go to how did the interest get you into your PhD and into what you were doing in your postdoc work? Like tell us about that part. So when I was an undergrad, I did a few things that gave me some perspective. I worked at Knights at a clinical reference laboratory so that sounds exciting but it really involved centrifuging people's blood samples and aliquoting urine samples and things like that and making sure that labels matched on test tubes. But it was actually an interesting window into the medical field. And while I had a good time working there and it actually was a good nighttime undergrad compatible job, it made me think, not sure I really want to go into the medical profession but it made me think a lot about cell biology and biochemistry because in essence that's what clinical laboratories do on these specimens. And at the same time, I did undergraduate work in computational chemistry. So computational simulation of small molecules binding to proteins and causing them to change how they do their chemistry. And that was with David Vanderjacht at the University of New Mexico. And as I was wrapping up entering my senior year, I had this dilemma that I think many undergrads have was go into grad school, apply to med school, do something else. And for better or worse, I decided to apply to graduate schools and I didn't know how competitive I would be so I applied to various tiers and then ended up with a lot of interviews. I really liked Duke. I thought their biochemistry department had very interesting research and at the time a professor who was chair of the department, Christian Rates, who ended up being my thesis advisor, had me do an extra interview during my visit to Duke with him. And I had no idea I would later join his lab but there was this infectious enthusiasm for science that he had and he loaded me down with reprints of his papers. Way too many to read and I had this big stack of reprints that I took home on the airplane and I ended up going there, ended up working for him and ended up traversing this interesting area from going to essentially theoretically thinking about biochemistry from a computational docking approach to actually working with the proteins in the lab. And for me, that was an eye-opener because it's one thing to model things, it's another thing to do the wet lab experiments and so I really appreciated that chance and I must admit, I wasn't that good at it at first. I had to get my pipette hands working and really there's a science to that sort of work but there's also an art and I think that's perhaps underappreciated. So that's how I ended up in graduate school. I actually didn't join my thesis advisor's lab originally. I joined the lab of an x-ray crystallographer because I specifically wanted to learn how to determine the molecular structures of proteins where all of the atoms are or likely are in these molecular models that I'd used in computational docking. It turns out the professor whose lab I joined was denied tenure probably for political reasons not because of productivity or science and so after a year I had to switch to another laboratory and the guy who was loaded me down with reprints of his papers, Chris Rates, convinced me to give a try at working in his lab and he did work on, among other things, bacterial membrane biogenesis. So how do bacteria make the coating on them that separates the outside environment from the inside? And if you think about that, that's how I got towards the antibiotic work, so. Oh cool, that's a good link. Yeah, that's a good link to that. Okay, so bacterial membrane biogenesis. Very interesting. Okay, now this is also cool how you had this, both like a computational chemistry side and then also a hands-in with the protein and enzyme sort of manufacturing side of things in the lab as well. I thought that was good. I'm trying to take my kindergarten understanding of biology and try and up it up to first and second grade over the next couple of years. If I can get to a better understanding of the central dogma of biology and I think if we can better teach more young people about the central dogma of biology and then get them to make it applicable in the biotech engineering fields that we have, this is gonna help with this $100 trillion opportunity. Okay, so now teach us about how that transition happened with Novartis as you were saying with the bacterial membrane biogenesis and stuff. So I did work in graduate school that characterized some of the enzymes, the proteins that do chemistry, that where that chemistry was necessary for bacteria to make their membranes and all cells have membranes and they separate the chemistry that happens inside the cell from whatever's going on on the outside and it's really one of the defining features of life because it contains these self-perpetuating chemical reactions and so you have to have a division of outside versus inside and membranes which are usually made mostly of lipids perform that role, they perform that separation function. So that's what I learned about that in grad school and I went and did a postdoc at UCSF and I had an opportunity to work with Robert Stroud, a very productive crystallographer and was able to solve the crystal structure of one of the enzymes that I discovered in grad school. So I did eventually get to do X-ray crystallography, just not when and where I thought I would and I remember how exciting it was when we collected the data sets up on Berkeley Hill up at LBL, the particle accelerator up there and I remember getting a data set that I knew would be good enough to finally write up the work and publish and the sun was rising over, it was an all night shift at the synchrotron and then I wandered outside to take a coffee break and the sun was rising and I thought, oh good, we have a full story here but so I'd always stayed, I stayed a bit with this theme of membrane biogenesis and enzymes that do work on lipids and my postdoc was a little under two years. In my field, postdocs are taking longer and longer and we can talk about that maybe later because I don't think it's a good thing but the time came for me to decide what to do next and I could have stayed as a postdoc for much longer and work on some projects that I had cooking in the lab on other proteins but I had an opportunity to interview at Novartis. Novartis is a Swiss company, a big pharma company but it has its infectious diseases program in, or did have its infectious diseases program in Emeryville, California and had just moved to that site in 2011. Its infectious disease program moved from Cambridge, Massachusetts to Emeryville so they were hiring up and for me it was a very lucky thing because I could still be a postdoc now but I was in the right place at the right time and was lucky enough to get brought onto the team there and then among other things I supported antibiotic discovery projects. I founded one project which I ultimately led and participated in many others and also did tech development and discovery so especially in the areas of protein chemistry and in that process it was fun. I got to be both a principal investigator in a disease area that I'd studied and really care about which is antibiotic discovery which I think we desperately need and at the same time I got to learn all these other things about virology, about fungal diseases, about oncology in pharma because the department I was in supported all of those and it was really a great six years. I enjoyed it and my group, my former group will be publishing our work so you probably know that Novartis pulled the plug on its infectious diseases programs last June or July I believe and a few months after I left and the good news however is a lot of that work will be published so people will be able to follow on and build upon what we did there. So yeah, so that's kind of how my work evolved into the ID world. This is, yeah, there's that other connecting link which is the discovery. You're working on antibiotic discovery and now it's again this self-remanent molecular discovery. So give us a quick bit on the antibiotic discovery as we get into the next one. Just teach us about kind of like what is exactly the cutting edge of that field? Well, it's both cutting edge. There's things that are cutting edge and there's things that are pretty old fashioned. I mean in the end of the day you need compounds that kill the bacteria that you're after first and foremost and I guess supposed to put it bluntly those same compounds can't kill or badly injure the patient. And the good stuff, the good stuff. And so in its first principle what you're trying to do is take bacteria, treat them with molecules, many molecules, millions individually if possible and see do those molecules kill the bacteria and if the answer is yes, then you do those experiments more carefully. Sometimes you figure out why they're killing the bacteria. Sometimes you never know exactly what the mechanism is and then eventually you have to find out if the molecule is toxic to all types of cells and that would include animal cells or human cells then animal studies, then human safety studies, then human efficacy studies. So sort of the normal progression of drug discovery. But starting with bacteria and sometimes those projects didn't start with the bacteria. Sometimes they started with an enzyme or a group of enzymes that we knew did essential chemistry for bacteria that did not have equivalence in humans because if you're thinking of developing a drug that's an antibacterial, it's best maybe to search for ways to mess up the chemistry that the bacteria do but that humans don't have an equivalent process because then you're less likely to find compounds that are toxic to humans. And that was always the difficulty. So finding those molecules and this really leads into what I'm doing now. So what we found in Big Pharma, antibiotic discovery, antibacterial discovery, all of Big Pharma's efforts do involve on one hand screening of biologics and antibodies and I'm sure many of the viewers know about that but not all organisms can be targeted with biologics. There's been some work targeting bacteria with biologics and with things called phages but still if you look at the antibiotics that we use there's small molecules that have to get into the bacteria. They get inside, they mess up the bacteria's biochemistry, they kill the bacteria or prevent them from growing and dividing. And there's a whole targeting. And there's a whole targeting issue. But then those same molecules have to have properties that are amenable to being drugs. So they can't be toxic to all cells, especially not the host.  So one of the great difficulties in drug discovery is are the properties of your molecule. So I think that many people who did medicinal chemistry work both at Novartis and other companies would probably agree that the small molecules that humanity searches through when looking for drugs, not just anti-infectives but all kinds, tended to be not necessarily the types of molecules we would want. They tend to be flat and greasy. There's a great paper called Escape from the Flatlands talking about the chemical flatlands. And enzymes do chemistry that often do chemistry that humans have a hard time doing. And these enzymes are encoded by DNA and have evolved in deep time in nature. And so this enzymeology sort of allows it maps to a whole universe of chemistry, some of which we know, a lot of which we probably don't know. And that chemistry has properties that makes the molecules sort of the opposite of being flat and greasy. It makes them in many ways more drug-like. So we're, and to be more specific, to have a certain molecular shape, so a three-dimensional shape. And that type of chemistry in general is hard for humans to do specifically anyway, but it's easy for enzymes to do because they've evolved to do specific chemistry and they don't typically cause side reactions and mixtures. So that was really what got me interested in synthetic biology as a vehicle to find new chemistry to drug targets that we've had difficulty, and we being humanity has had difficulty targeting. And that's sort of the central thesis to Tierra Biosciences where I am now is can we use small scale transcription and translation reactions? So reactions that take DNA and that DNA is turned into RNA and that RNA is encoding protein. Can we take protein that's encoded by DNA that we put into these reactions and can those proteins do chemistry as enzymes that allows us to find new molecular entities? So we're doing a few different things. We're discovering new enzymes and this is called functional genomics. And then when we mix together groups of enzymes, they do chemistry and our long-term goal is to find new chemistry in the therapeutic space. And so it's really just an evolution of what I started with, but it's addressing this unmet problem, unmet need I think in biopharma discovery, which are small molecules, the non-antibodies, the non-biologics that have properties that are different than what we typically have in our small molecule libraries. And I think nature is full of these. And the question is can we use this platform to discover them and can we make sense of what we've found? Yeah, okay, so there's a massive amount of evolution that's happened three and a half billion years, especially of bacterial evolution. And that gives us a huge catalog of evolution to look through and to see exactly what sorts of evolutionary strategies exist for us to be able to take from what molecular production systems occur in evolution for us to be able to take and apply our healthcare, agriculture, et cetera. And we have that image to Ron that we can bring up. So this is you guys leveraging computation and automation into these tiny little micro factories that are then brewing up the different, seeing how the molecules are affecting what you teach us about this. So to be clear for the viewers, these are droplets that contain food coloring and water because most of our reactions are clear. And someone once asked me what I did and I said, well, how deeply do you want me to describe it? And I said, it's indirectly observing things that happen when I mix some small volumes of clear liquid with other small volumes of clear liquid. And if you explain it that way, people think you're crazy. But this is for artistic effect. But yes, in seriousness, what we do is we make these sort of cell-free factories and we make them by grinding up cells, separating their components out. So we take living cells, so they can be bacteria, they can be other things, and we grind them up so they're no longer living. They no longer have that separation between external environment and internal environment. But we do so in a way that preserves their ability to take DNA that we put in, that we add exogenously and make RNA from that DNA and then make proteins from that RNA. And there's a few interesting, there's a few reasons why this is interesting and why these cell-free factories, if you want to call them that, are helpful. One reason is that we can add whatever we want to the reaction. And this allows us to prototype what might be called unnatural natural chemistry. So sometimes enzymes can use, can act on molecules that they wouldn't normally see in nature, but we can add those molecules from the outside to our reactions because they're open. We don't have to get them into an organism. The other thing is that we can print pieces of DNA and we can layer many of those inside one of these reactions, also because they're open. So we can mix and match which proteins we're making. And introduce diversity in the products of those enzymes by doing that. The other thing that's really important and it's subtle but it bears repeating is that because these are not living organisms but they maintain this ability to make proteins from DNA, we are able to discover enzymes that might be toxic to the host. So if you engineered yeast or bacteria to make a certain enzyme and many companies do this and it's a really good way to discover biochemistry, what you don't find is you don't find the enzymes that their mere expression in the host makes them toxic. And so that's sort of like a dark area of the enzymatic space of biochemistry that is not easy to uncover if you say you take a gene from algae that you've gotten from molecular bioprospecting of the oceans for instance and you put that into E. coli and you say okay, can E. coli make this gene and is it functional? Sometimes the answer is yes but sometimes you just don't get anything or the E. coli die because just the expression of that gene is bad for them. So because we don't have any, we preserve some components of the living system but it's really not alive and thus it doesn't have a way of kicking out a plasmid or slowing down its growth in response to making something toxic. This allows us to uncover things you wouldn't normally see. And so this is a hypothesis. We're still in the process of testing this but we think that this will allow us to prospect in areas where some other platforms can't. And of course as a business model it's an experiment too. Will this be valuable to the synthetic biology world is sort of a question that as a company we're testing by our very work. Yeah, yeah. Dean, linking this all the way, let's see if I can take some steps all the way back to where you were initially teaching us about what you were discovering when you got into the first sort of labs where you were putting blood samples into centrifuges and stuff. So and then bring it up all the way here. So when you're taking blood and putting it in a centrifuge, centrifuges, the main producer is alumina, right? Of centrifuges? Oh, you're thinking about sequences. Aluminum is a sequence, yeah. So centrifuges just spin things fast. They spin things fast. And there's many producers of those and that work was not very scientific. It was more procedural. It was separate. Yes, so here's where I'm interested. Okay, this is kind of what I want to break down. So alumina's main is doing the newest gen sequencing. And they do that through a process of breaking down the double helix and writing up the nucleotides. Somehow they do that. We'll just leave it at that. We'll leave it at that. Centrifuges are spinning around things like blood and then making it so that you can take a specific part of the blood and go and sample that. Precisely. Okay, so then now when you're doing things like, you know, you were talking earlier about you take like an actual bacterial cell and you're giving this example of, you were teaching me initially when you were about how DNA and RNA and proteins that this process is kind of all happening in a chaos theory way. It's not necessarily step to step to step at a time. They're all influencing each other at the same time. And yes, I've, okay. Well, we learn it in a step-to-step way. We learn in a step-to-step way but they're constantly influencing each other. In real living organisms, it's much different. I gave a talk that you attended called something along the lines of DNA is not computer code. And I think this is an important point. So yes, DNA is a template for RNA, which you can think of as a temporary message, which is a template for making proteins. And, but really it's not one-to-one mapping. And there's, and so I think that's what you're referring to. Yes, yes, correct. And then the, when, what is, what's encoded in the DNA is then the RNA will take what's encoded in the DNA and act as a messenger to the ribosome. And then the ribosome will take that message and then make a protein. Yes. And then the protein is an enzyme. Sometimes it's an enzyme. Sometimes it's a structural component of a cell. So an enzyme is a protein that catalyzes chemistry. It catalyzes further chemistry. And so usually proteins are enzymes because they want to catalyze something. Often, but they can be very often structural components of cells because cells have structure. And so there's proteins that are just, they're literally structural building blocks. So not all of them catalyze chemistry. Not all of them catalyze. Okay. So now we take what you were describing earlier about taking a single cell or cells and you're like, you're kind of like running them. Yeah. You're trying to grind them up. Grind them up. However easy to turn on a blender. By different methods, well. How do you grind up the cells first? Well, or lyse the cells. We do it a number of different ways. Sometimes just by putting them under high pressure and squeezing them through a small aperture. A small aperture. So then you have shear forces that rip them apart. Yeah, that rip them apart. Now, but the cell can't actually, it's not living anymore at that point. At that point, it's not alive. So you can take the DNA, RNA, ribosomes, protein enzymes within it and take those and then bring them into these. Yes. And then you can add your own DNA, exogenous DNA. Exactly. And then now you're saying, okay, mixture. Do. Do. Chemistry for us. Chemistry for us. But if it's toxic to a cell that's living, we might still see it in those reactions where we wouldn't see it necessarily in a cell. So that's, it's, we've lost some of the regulatory machinery Yeah. That cells use to keep themselves happy. Okay. But. So then you'd have to re-test In the organism. In an organism. Okay. Because something may end up being non-toxic here but toxic in the cell's environment. So here we use this for rapid prototyping. And. Yes, rapid prototyping. Because there's what, it's like a thousand of these little factory cells. Yeah. So when we can do these in, in, you know, five microliter and smaller volumes.  Five microliters. Wow. And it's, you know, so it's a way we can, we can do many experiments quickly. And then you imagine there's a potentially really interesting data backend on that. Yeah. Because we know what DNA sequences we've put in. We know other things that we've done to these reactions. So, and because the experiments are well controlled and, and are done in, you know, multiples and, and, you know, in proximity to each other and with, with many things controlled, we can learn things about what are determinants of a protein's function. So for instance, you know, from the sequence, we can, we can test many, many sequences to say, you know, in this sequence space, this portion of it encodes, you know, functional acyl transferases, a certain type of enzyme, but maybe this actually does something different. And so we can quickly test those hypotheses. And that's, that's on the route to using these mixtures to make drug-like molecules is understanding what the enzymes actually do. Because you can predict computationally and that's one of the things that's driven forward synthetic biology is the ability to look at a protein sequence that's encoded or a DNA sequence, figure out what protein that might encode. And if that proteins an enzyme, predict what chemistry it might do, but still a large amount of that is not known. So there's a really a big need to empirically test these hypotheses and our platform is one way of doing it. Yes, yes. Now, why would you then not take just a bunch of, why do you do the grinding up process and put it into these five microliter containers rather than maybe just have cells themselves and then put your DNA in the cell environment? So, yeah, so people do that. But one thing is that how you assemble the DNA that goes in those organisms is often a bit more complicated than what we do. Often people put, say, a replicating plasmid into, say, an E. coli and our plasmid is a piece of circular DNA, copies of which can be made and it's used to drive expression. And as cells divide, if, say, it's a bacteria, the daughter cells get copies of this plasmid. And one can make knock-ins to genomic DNA and things like that. So is it easier to add any DNA you want to when the cells grind it up versus when it's in its... Yeah, it's generally easier because we don't have to play tricks to get it into the cell. Play tricks? Well, and I wouldn't call them, the tricks are standard in the field. There's nothing weird about it, but it takes that layer of complexity out. And it also takes out the layer of complexity where what you've put into the cells makes the cells not happy for some reason or another. And then they either don't make what you want them to make or they die. They push out the exogenous DNA easier in the non-crushed-up environment. Yeah, if they're a living organism, they'll do whatever they have to to make themselves as fit as possible. But here we don't, you know, our reactions aren't evolving. They're just doing transcription and translation and other chemistry too. Now, okay, now there's a couple of questions that came into my mind, okay. First question is, how do you decide what exogenous DNA you're adding to these?  So those are both great questions. So what we decide to put in can really be any sort of hypothesis we want to test. So I'll give you an example. Maybe we have a whole class of enzymes called cytochrome P450s. So they're enzymes that do a particular type of chemistry and we have a panel of them that we know their sequences because they've been published or they're from organisms that have been sequenced by someone somewhere. And we want to test that whole panel of enzymes each individually for their ability to do something to a molecule that we're interested in. So we put the DNA in coding those enzymes, one piece of DNA for one enzyme into each reaction. And then we have the small molecule that we're interested in in each reaction. And then on the back end, we let the reaction run, we have a way that we can determine if we've made RNA message. And then depending on how we set it up, we can determine if we've made the protein that we're interested in. Because only new proteins that get made in these reactions will be tracked. And so we can differentiate between what's new that's been made versus what came along with the cells.  Okay, so let me see if I can get started. So you already know the enzymes chemistry that's happening. Well, it's predicted chemistry. It's predicted chemistry that's happening. So you have a predicted chemistry. Then you add a molecule, then you're adding something into the different enzymes, the different enzymes. And then when you're computationally mapping what's happening in each one of the experiments, you're logging only what the novel protein. Yeah, the new proteins that are made, we can see. You can see those. And then somehow you turn goo into code. Well, sort of, yeah. Into knowledge. To knowledge. Well, right now it's actually an exercise in data handling in a way because it gets really complicated. You have to track everything that's going into each reaction, track what's going on in that reaction, and then track your readout at the end which will be specific often for what you're trying to make. So there's general learnings that we get from every reaction we run, which is how well and how much of our protein of interest that we're trying to make in that given reaction, that enzyme of interest, how much of it are we able to make relative to its cousins or orthologs that we're testing. But on the other side of it, what small molecules or what chemistry those enzymes are doing, that readout will be different from class of enzyme to class of enzyme. So it's all these layers of data and we think that there's interesting and not obvious things to learn. Well because if you take the massive catalog of enzymes and enzymatic chemistry that occurs and then whatever small tiny, tiny percent that we understand is probably less than 1% of all of it that we actually get. And then we're trying to understand what the other 99% does and then apply that to agriculture and healthcare. Exactly. That's very exciting. That's the stuff that also gets me, it gets me salivating about what biology knows that we don't know and how we can apply that to our lives and that's the $120 opportunity that exists. But to be clear, it isn't easy. And even the big players, there's big players in the field who've done really amazing work, Ginko Bioworks and Zymergen and Amaris and a lot of the companies that have pushed the molecular discovery in synthetic biology forward have been doing it in organism and we hope to add to that toolbox that they've started. We hope to add self-re prototyping and see where we can go with that in terms of prospecting for new chemical matter. Interesting. It's, but technically there's engineering challenges, there's data handling challenges and biology is hard because it's unpredictable. So. Yeah, yeah. And then you have to replicate this because if it works one time you want to replicate it again to exactly out the same result. And so this is the, this kind of segues us into what we wanna talk about next. But I just find it interesting that on one side it's called, you use cells for discovery. On the other side, you don't use cells but you actually took cells and blended them up. And use their components. And use their components that they're dead. And then, yeah, it's funny, the cell-free discovery. Okay, so, let's segue then. So we're talking about this in a fascinating way right now where if you come out, if your computation's producing, your test is producing a certain computation and your readout is something and you're like, aha, interesting. Then what you have to do is you have to do that again. Because you want to verify. If you verify three, four, 20 times you're like, okay, we can probably say with like 99% accuracy this is right. And so that's how you do science. That's the scientific method. That is testing your hypothesis over and over again. And we've seen so many instances where this foundation of knowledge, you're very passionate about this and I'm excited to talk about this, foundation of scientific literature and knowledge that we've built on has these like little cracks and they're kind of like sometimes the way that we handle academia isn't in the most conducive way. So teach us about the way that you want to inspire people to rewrite the scientific playbook and rewrite the incentives, because I love this. So it's one of motivations. And so if you view science as an ecosystem, you have many different players. You have universities, you have private companies, you have not-for-profit institutes and NGO type organizations and a lot of things in between. And you have amateur scientists who are operating on their own or in groups and all of this is really good. But they all have different strengths and weaknesses and have different motives. And in academia, especially in academic science, I think the motive is often priority.  And I feel that that, even without ill intent, that tends to motivate people to oversell what they've discovered and maybe not be as careful about reproducing it before they publish it as they should. And then companies like Novartis, where I worked and companies like Tira Biosciences, where I currently work, and other academic labs and people all over the place will sometimes depend on that literature to guide them and won't be able to reproduce the data. And no one really has put a number to this to my knowledge, though I may be wrong, but I think it represents a huge opportunity cost in time and money. And I really think that we should incentivize people in academia to publish derivative works as well as novel ones, where those derivative works show within them that a whole nother study can be reproduced. And there's movements to do this, and I think open access publishing in general is a good thing. Most of the basic research done in the US anyway is funded by the public in one form or another. And so I think that it's really key that publicly funded science be available to the public and be as reproducible as possible because there's huge sections of the economy that depend upon that. And then this gets to education. I mean, I won't be shy about it. I think that academic education in the sciences is a pyramid scheme. And again, it is a pyramid scheme. It's not because of bill intent, but the way the funding model works is if you're a faculty member and you want to keep the grants coming in, you need people to do the research that makes the papers that help make the case for your grants. And so the model works when there's more trainees than there are terminal people at the top of the pyramid, professors or deans. And so you see this huge number of postdoctoral fellows and graduate students being trained for a very small number of professorships that are open in any given year. And it's a matter of narrative, which I think is really important and we need to change in science that becoming a research professor is not the only thing that you can do with that training. On one hand, and on the other hand, can we be more efficient about how we educate and train scientists? And I know education and training are actually a bit divergent, but on one hand, can we train scientists to be passionate about their science at the level of philosophy, which a PhD would imply, but doesn't often confer. And on the other hand, can we train people to do what they need to in the laboratory and have touch different techniques to push specific research forward? And I just really think, I think rethinking that is really important and that's why I'm involved among other things with IndieBio. Because I think that if I could go back again, if I was finishing grad school and even though I had a good experience in my postdoc, I would definitely probably go to a startup accelerator or incubator of some sort, just because you learn a lot that way and it's in many ways comparable to doing a postdoctoral fellowship. So I think we need to incentivize reproducible research in academia. And in an industry, it's sort of different. Like within your own company, it's essential that people can reproduce work. And so if anything, the motivation goes a bit far the opposite direction is there's an emphasis on reproducible protocols, but then there's less bandwidth to do novel work. So I think that what academia is good at and what industry is good at in science are not always the same thing. They're often the same thing, but not always. And for a healthy ecosystem, you need both to be everything to be healthy. So we need to rethink, can we train graduate students in the sciences with a funding mechanism that doesn't require a pyramid scheme to make it work? And can we build bridges to industry? And I think that's being done. But can we think of creative ways to train people and educate people in the sciences that don't take all of their 20s and don't land them in a postdoc where they get paid like 40K a year regardless of where they live and do their research, and then are starting their career in their early to late 30s doing something after that. So I really think we need to be more respectful of students' time. Yes. And then the other thing is this- There's a better way for the edge of knowledge to be explored more effectively, reproduce the results, incentivize that, as well as when you're at the edge of knowledge, how do you get the kids even in their teens to start kind of playing around at that edge of knowledge with the right mentors and with the right strategies, and then get them to the point where you're not in your mid 30s before you're even starting to dabble potentially in industry related progress. And it's also, it ties into how we communicate science I mean the public, by and large, doesn't realize what one of my colleagues once called the postdoc apocalypse, a postdoc ellipse, and this just isn't generally I think known about, and it should be discussed because this comes at a great cost to human potential. But I think the other thing to talk about and what I'm personally interested is changing the narrative of science. So scientists are often portrayed as a certain way in the media, a certain way in literature. And I think brilliant, brilliant people that have just so passionately been aiming to push the things that we have around us, these brilliant devices, what we're using right now to stream the vehicles that we're driving, the medicines that we get at the healthcare institutions, et cetera. This is all aspects of brilliant scientific burdens of geniuses that people have taken on but an important point of that is almost all of those discoveries were team efforts. There were team efforts, exactly hundreds, thousands of people. And I think we have to get away from this idea of a heroic scientist doing heroic research in the lab that opens up a whole field. I mean, it sometimes happens, but it's really better to think of it as a team exercise and then if you think of it in that way, one should ask oneself, well, how cognitively diverse are scientists really? Are we losing people from the field who our discoveries and our creativity would be better for them, but maybe their personalities and minds work differently than what we're selected for in science. So to get through a PhD in biochemistry, you have to be willing to live on a lot of ramen noodles and there's a certain selective pressure, right?  So it selects for a certain type of person the current way we're doing it. And what I'd like to do outside of my work with Tierra and with some friends and colleagues, we started bio captivate a not for profit that we're in the process of launching to sort of bring these conversations to broader discussion is, how do we nurture cognitive diversity in the sciences? And how do we find inspiration at, for instance, the intersection of art and science, the arts and the sciences, which are not in opposition, but in fact should work together. And so anyway, this is what I'm interested in beyond just biochemistry and I think that there's an important place for that. I love it. And give me your one thought because we'll have to have you back to talk more about an update on Tierra as well as more on the scientific foundation knowledge. But give me your thought on how to best, we find ourselves again back to the beginning as stewards of Earth, 8 billion of us exponential technology. We evolved so quickly, what is the ideal well functioning republic for you? In the US or as a first species? Yeah, first species. Like what would be some of these principles?  Well, I mean, I think that this freedom to, within certain parameters, live life how one wants to, use one's time how one wants to, especially to be given permission to be creative. I think that this is really important. And I think that a responsibility for society also should temper that individualistic impulse. So I think that we should, individuals should have a great freedom to explore, to be creative, but that at the same time, we recognize certain principles that have to be upheld for the good of everyone as a group.  But I really think one thing that I fear is slipping that an ideal republic would have would be the ability of any child, no matter if they're born wealthy or at the lowest end of the spectrum of family wealth that that society might have to have equal access to the same quality of education. That is a man. And if they choose to take advantage of it and they should be encouraged to, I think we really have to do that because think of who we might be losing. We are losing so many brilliant thinkers. Just because they weren't in the right place to get that education. And so to me, that's really important. Me too. That's why we're on that same wave like there. I'm glad you said that people born into the world get to pass their time, how they so choose to absolutely, and also that equality of opportunity for people to pursue whatever they want with their time, with that creative potential. So at least if the baseline is there for everyone to pursue. Okay, we'll unpack that more soon. Okay, the two quick questions on the way out. This is simulation. Are we in a simulation? Oh, I don't know. Not enough data. Class excited. Wait, give me your best imagination on if we're in a simulation. Makes me think of like the brain in the VAT type experiment. I don't know. Well, then how do you explain dreams? Yeah, that's right. Are those simulations inside a simulation? Yeah. I don't know. It's a pretty hard hypothesis to test, right? It's a good one to poke with the scientific probe.  That's interesting. That's how we like to think about it as well. Okay, last question. Sure. What's the most beautiful thing in the world? Most beautiful thing in the world. You know, I think the most beautiful thing in the world is honestly waking up wherever you are and knowing that you're going to have another day that's going to be different than the last. I think if everything was the same, it would be boring. And I think that there's a beauty in the unknown and in possibilities. So I think, yeah, the most beautiful thing are the possibilities that one might see. Oh, that's great. This is a good answer. I like that one a lot too. All right, this has been such a pleasure. I feel like I've upgraded myself to at least the first day of first grade. Nice to thank you for having me. Really appreciate it. Yeah, this has been such a pleasure. Really appreciate you. Keep up all the great work with Tierra Biosciences and everything else, BioCaptivate. Everyone, check out the links below to Lewis's work. Also, follow us on Twitter. Follow him on LinkedIn as well. Go check out his work. Also, a huge shout out to Ron Vargas for producing and directing. Major, thank you. We greatly appreciate you. Everyone, give us your thoughts in the comments below. We'd love to hear from you on the topics that we talked about. How can you pursue the frontier synthetic biology as well yourselves? Go and crack at that edge. Go and build on it, everyone. Also, support the artists and entrepreneurs that you love, that you want to see succeed. Go and support them. All our links are below. Keep supporting them in your communities. Also, build the future. Manifest your dreams into the world, everyone. Inspire others in your communities and around the world. We love you so much and we will see you soon. Peace.