How 3D Genome Mapping is Changing Everything We Know About DNA | Ivan Liachko (Part 3/4)

Intro - 00:00:06: Welcome to The Biotech Startups Podcast by Excedr. Join us as we speak with first-time founders, serial entrepreneurs, and experienced investors about the challenges and triumphs of running a biotech startup, from pre-seed to IPO, with your host, Jon Chee. In our last episode, Ivan Liachko shared how studying DNA replication in yeast sparked a deeper interest in comparative biology, the move that brought him to the Pacific Northwest, and how the culture at UW helped him transition into high-throughput genomics. If you missed it, check out part two. In part three, Ivan talks about the scientific collaborations that led to the founding of Phase genomics and how falling in love with 3D genome mapping shifted his perspective on what sequencing technology could do. He explains how a clever idea, not a brute force one, unlocked a new type of biological information and how that breakthrough paved the way for discoveries in structural genomics, microbiome research and genome assembly.  

Jon - 00:01:23: So tell us about the lab that you're in at Cornell and the research you're doing.  

Ivan - 00:01:27: I worked in a lab. My professor's name was Bic Tye. We worked on DNA replication and chromatin. I looked at proteins. So her attribution to the field was she discovered a lot of the proteins that unwind the DNA when it gets replicated. So when DNA, you see those replication forks, when DNA is getting unzipped and copied, well, you need something to unwind them. And it's these proteins called MCM proteins. And so she was one of the key, like, she was like, kind of discovered them in the early days. And so I did a lot of work with MCM proteins and related things and how they impact chromatin organization. You know, that was my PhD. Did a lot of side projects. I started doing some DNA sequencing, not like genomics, but kind of like old school. I did a lot of screening. I screened lots of plasmids. I screened lots of replication origins. I started doing a lot of work in different species of yeast because it was yeast. But you know, there's different species and each one of them is basically different machine. So if you go like, okay, this is how this yeast replicates its DNA. And I'm studying this machine. You're like, well, here's another species of yeast. How does it do it? Not because I care about yeast, but because it's a different biological machine that's similar, but different. Some parts are conserved. Some parts are different, right? Like it's a little bit like if you're trying to understand how a car works and you're a geneticist, right? The way the geneticist does it is you like remove one piece, see what happens, remove another piece, see what happens, study like the organization of the pieces, right? But what if you suddenly have three different cars, right? What if you have, you know, like a little Fiat, an SUV, and like a Tesla, right? They all do the same thing, but in different ways. And so you can look at how the processes are different and similar to try to understand, like, how does DNA get replicated in general, right? Different bugs have figured out different ways of doing it, but they all have something in common. And so the commonalities and differences... You know, actually tell you a lot about biological processes. And at that point in time, you know, everybody really works on one species of yeast, the brewer's yeast. And I started doing very similar basic things, but in other species. And it was like lots of cool science because you discover a new way bugs figure out how to do the same thing. And so there's like different proteins, there are different structures. It's very fun. And they're easy to tinker with. That's why people use yeast, because they're kind of easy to work with. And a lot of the biology kind of holds true to humans and to other models.  

Jon - 00:03:56: Very cool. And I love that analogy. Just like it's like very much just like illustrates it in my head. It just like clicked. And you mentioned that you stuck around for a kind of a fake postdoc. When did you know it was time to leave? You mentioned that's when your wife and you decided to come to the Pacific Northwest.  

Ivan - 00:04:15: Would just like stay there forever. Like I would, I would like, you know, I would like die with a pipette in my hand. But so a friend of mine who was a collaborator of mine, she was in Princeton. She was a few years of my senior, but I was collaborating with her lab on some projects with her. She was like a postdoc at Princeton, but she got a faculty job here in Seattle at the University of Washington. Her name is Maitreya Dunham. I've been to Seattle one time at that point for some conference. And, you know, when you live in the Northeast, Seattle is kind of a place that exists in your imagination. Like, like you don't think you don't really go there ever. You don't know anybody who's there. You know, it exists, you know, about Amazon and Starbucks and whatever, right. You've seen Sleepless in Seattle, but like, it's sort of like in this fog of war, right?  

Jon - 00:05:03: Yeah, yeah.  

Ivan - 00:05:03: You just don't think about it. And, and I was there once and I, one of these people who like loves trees and mountains and mushrooms and like just nature stuff. And I was like, God, this place is incredible. And so when I found out that Maitreya was moving to Seattle, I just called her. I was like, Hey, can I be, what if I'm like your first postdoc? What if I get it?  

Jon - 00:05:23: Hell yeah.  

Ivan - 00:05:24: So that's basically what it was. And so-  

Jon - 00:05:28: Cool.  

Ivan - 00:05:28: That's how I got my postdoc. And my wife came with me and she found her postdoc on Craigslist.  

Jon - 00:05:34: Yo!  

Ivan - 00:05:35: Yeah.  

Jon - 00:05:36: Cool.  

Ivan - 00:05:37: And now she's a faculty in that department.  

Jon - 00:05:40: That's so cool.  

Ivan - 00:05:42: She wasn't kidnapped.  

Jon - 00:05:45: That's so cool.  

Ivan - 00:05:46: Because the lab was so cheap, they only wanted to advertise locally. And so they put the postdoc on Craigslist and she like she went there and it was like totally legit. And then she did a postdoc and she leveled up into faculty. And-  

Jon - 00:06:03: That's so cool.  

Ivan - 00:06:04: My whole life is basically like this is a long like interview. My whole life is basically a bunch of random tumblings.  

Jon - 00:06:12: No, but that's the thing. I think it's more common than people realize. I mean, I can think about it as like me kind of like stumbling around into opportunities that, you know, that had major inflection points. And I think two things that stood out to me. One is like relationships matter, right? Like your collaborator turned into an opportunity to come to the Pacific Northwest. And you just never know. Like it's not only you don't know what opportunities present themselves to you, but also then your colleagues, which kind of then, you know, there's a there's a degree of separation. That can be a shared opportunity. Like and that's why like relationships matter. And also the this is a Craigslist kind of experience. It wasn't for work, but like obviously some Craigslist scams are proper scams. So I'm not saying that they're all legit. But when we were first moving into San Francisco, we're looking for apartments in San Francisco on Craigslist. And it's hard to find housing. And so we were like really just going through the Craigslist ads and classifieds. And there was one that looked like a proper scam, like look like a proper scam. There's one picture, but it was from like this. It was like on a flip phone that was like probably like super, super old. It had a funny watermark. And it's like you could it was like of this corner of the apartment that you just like that doesn't help me envision what this place looks like, actually. So my now wife, we're like, screw it. We're just going to go show up, see what's up. And it was listed as a one bedroom. Show up. The place looked completely different. It was like I got like proper like reverse scammed. It was like I got reverse scammed. We're like, what the heck? We asked and also, it was listed as a one bedroom it was actually a two bedroom.  

Ivan - 00:08:00: They're like, we just keep our shoes in this one, so we're calling it a closet.  

Jon - 00:08:03: Yeah, yeah, yeah, really. The landlord was like, well, I didn't feel comfortable calling it a two bedroom because there's only one window. And I was like, this is a proper room.  

Ivan - 00:08:15: Like, sir, this is the Bay Area.  

Jon - 00:08:17: Yeah, yeah, yeah. This is a public-  

Ivan - 00:08:19: There's like a woodshed outback that's rentable.  

Jon - 00:08:21: Yeah, you call that a room. And so we got like reverse scammed. Kind of the same way, like your wife got a sweet roll. We were just like, I thought we were going to get robbed. Like, but-  

Ivan - 00:08:32: This is a trap, right?  

Jon - 00:08:33: Yeah, this is a trap.  

Ivan - 00:08:34: Like, where's. Like, saw or something.  

Jon - 00:08:35: Yeah, something. There's got to be a catch here. And we're just like, oh, my God. And so we're just like, we're just like, all right, take the deposit. We're like, we're ready. But is this something where you can find really interesting things, even, you know, even though on the surface, they might look scammy. But again, some are scams. So don't just apply for everything. So you're now at, you know, you're at UW. Talk a little bit about that experience, you know, for you and your wife.  

Ivan - 00:09:00: Yeah, UW is awesome. I mean, Seattle is awesome. The Pacific Northwest is an amazing place. Those who haven't been, please do yourself a favor and come here. It was an amazing department. One of the things that I really loved about it was, I mean, just the quality of the science without the slavish approach to work. I think there's a little bit of a culture shift. You know, I was coming from Northeast where, you know, like my old department and I was part of this system, right? Like if you show up at like midnight to work, like there's a whole bunch of lights on. Like there are people are working in labs around the clock, right? That wasn't really the case here, despite the fact that they are so good and like so high tier, right? They're rated it like the Department of genome Sciences here is just like super elite. Without that feeling of like factory farm. And people are just so good and friendly. And like, it was great. And that's why I started learning genomics. And again, I was doing a lot of DNA science stuff, but it was all using like old school genetics methods. So low throughput, if I want to find something, I'm going to clone it out myself. I'm going to sequence it myself. I'm going to do everything. And then somebody was like, why don't you just do that to like every gene in the genome at the same time? You know, and like my friend, Doug Fowler and Carlos Araya, who were in Stan Fields' lab, they were originally like, dude, just like do this the high throughput way. That's where the world is going. And they taught me how to do it. So I combined sort of the kinds of things that I was doing in the past with sort of sequencing methods and same approach. I kind of said like, look, this thing works in a particular species. Let's crank it up with the power of genomics and let's just clone it in like all these other species. And so it's the same car analogy where it's like, let's understand not just like how the alternator of this car works, but how the whole car works kind of in its parts. And now we say we have another car and a different car and a bit car and a little car, right? And so that became sort of a merge of molecular genetics and comparative biology where you're sort of, you're saying, okay, here's a protein that does a function. Here's how it works in 15 different other organisms that are kind of similar to each other, but they're all different. And so we started, I started doing that. I started doing a lot of work in like other yeast species. Again, not because I necessarily cared about those yeast species, but because they are similar, but different machines that do the same thing using slightly different parts, right? And if you understand all the parts, all the ways that this can be done, you get a better understanding of how things work. And that was the general flavor of what we did. We also did a lot of really cool experimental evolution science. You know, Maitreya's lab does a lot of experimental evolution where you grow continuously. You can grow continuous cultures of yeast in these machines called chemostats or turbidostats, where you're basically creating conditions where things evolve continuously for particular features. And then you see how they modify their genome to adapt to something, right? Like you evolve, you involve some yeast over thousands of generations, let's say, or hundreds of generations in the limiting of some nutrient. You're like, okay, now I'm going to give you, I'm going to give you no phosphate.  

Jon - 00:12:15: Yeah.  

Ivan - 00:12:15: See how you evolve now.  

Jon - 00:12:17: Yeah. Yeah.  

Ivan - 00:12:18: And then you see what it figured out how to do. Like it's revived, it amplifies some genes and mutates some stuff. And you're like, what did you do? And you're like, aha, that's how it adapted to low phosphate. Therefore, these genes are important for, you know, phosphate. So that's the idea. Is that like, let's evolve these things, see what they do. Again, figuring out how molecular machines work, but not, you know, the way a biochemist would do it is you take a molecular machine, you figure out how many molecules of this type, like how many, how much rubber is in this car, how much metal, how much steel, how much plastic, how much petroleum, right? You categorize all the parts. Whereas a geneticist will be like, I'm going to take this thing out and see what happens to the car now. And so you're, you're kind of like combining that approach with high-throughput genomics and applying it to different types of cars and to try to get a more holistic understanding of how all these machines work. That was kind of the, the, the grand thinking behind it. And so some of it came from the screen. Some of it came from doing evolution experiments to see how things circumvent problems and stuff like that. So, so it was super fun science. We collaborated with lots of other labs and lots of other projects. And, you know, that kind of led to Phase and sort of spinning off Phase. Some of the innovations that came out of that.

Jon - 00:13:31: I can tell how much you love the Pacific Northwest because I also love it, too. And I'm like, hell yeah. Everything you described, I was like, my wife and I were thinking about moving to Seattle. Some of our closest friends went to UW and SU. And this is a complete aside, but love Jamjuree, the Thai restaurant. That's God, it's so good. And then going to Montana for picklebacks, just like, a perfect night out. That's like whenever we can leave the Bay, we always love coming up to the Pacific Northwest, especially when it's sunny out. It seems like it's sunny right now. It's just like the dream. It is like the dream where everyone's out by the lake.  

Ivan - 00:14:11: People don't get the right idea of the Pacific Northwest weather patterns. They think about the rain, but it's not the rain. It's like you get this drizzle for six months. But when it's not raining, you get the best summer on Earth.  

Jon - 00:14:26: It is insane. Like actually, properly insane. And I just remember like brought some friends from the Northeast to Seattle during that sunny time.  

Ivan - 00:14:38: It's like a surreal place. They're like, this can't exist.  

Jon - 00:14:42: Exactly.  

Ivan - 00:14:44: There's like a whale and a bald eagle right over flying around. There's flowers. You're like, it's nuts.  

Jon - 00:14:50: Yeah. How? And you got a rainier just like in the back too. And you're just like.  

Ivan - 00:14:55: And then you're like, we can just drive to it. Like we can just get to the mountain. We can just climb on top of a volcano.  

Jon - 00:15:00: Yeah.  

Ivan - 00:15:01: Just drive over there. Like-  

Jon - 00:15:02: So insane. Everyone was just like, I never knew this existed. Granted realize that this is for a few months. Like there's like, there's a, there's a few months where you can capture this.  

Ivan - 00:15:13: When I get employees who come here from the Northeast, when I'm in, when I'm trying to recruit somebody, what I do is I drive them up to Whidbey Island again, like an hour drive. And there's a beach there where you can pick clams, mussels, and oysters just like on the same beach.  

Jon - 00:15:26: Yeah.  

Ivan - 00:15:27: And you can just like grab them and you can like just eat an oyster right off the – you're like in the middle of the ocean.  

Jon - 00:15:31: So good.  

Ivan - 00:15:32: Like, an island. So good. It's like it's the amount of nature you're just not used to. You're like, what?

Jon - 00:15:38: Yeah, yeah. What the heck?  

Ivan - 00:15:40: There's this mountain forest thing. There's whales. You're eating an oyster that you just picked up off the – like – and it's like not even a big deal. It's just like some – just like houses. It's super cool.  

Jon - 00:15:50: Yeah. I'm like a big fan of the Pacific Northwest. Exactly for what you've just described. And so, you know, you're talking about, you know, it's a natural progression of your PhD but now applying it to high-throughput genomics. And you mentioned like the collaborations that became ultimately Phase. Can you talk a little about that collaboration? And, you know, you mentioned you're like, I thought I was just going to be in the lab and doing lab things. Like, you know, you didn't really have that, you know, business, like, oh, business in my sights. Talk a little bit about how you're like, okay, I'm going to get into business.  

Ivan - 00:16:27: So, I at some point fell in love with this technology that's called Hi-C. It's a trick for doing sequencing. Not so much to sequence the genome but to do something really unusual sounding. To try to understand how a genome folds in three dimensions. So, the idea is, right? So, when people talk about what is DNA, what is a genome, they talk sometimes about like it's like a book or it's like a word. But I think the most apropos description is it's like those old telegraph, like ribbons, right? That little Morse code, like the little piece of paper that comes out and it's like beep, beep, beep, beep, beep, beep, beep, beep, right? And it just keeps getting longer and longer and longer. So, imagine you have those little telegraph ribbons but it's like long enough to stretch around the earth or whatever, some absurd length. And the little dots and lines on it are basically your ATCGs, right? So, you have this super long string, super long like ribbon or thread or spaghetti noodle or whatever with just like letters all across it. At some point, you have to squash all of it into like a sphere, right? Like a cell is like a ball or a sphere. And you're just cramming this thing in there. So it folds up in a three-dimensional shape. And like a lot of things in biology, it's not a random shape. Like stuff, like biology happens and that shape matters and it does things to like how genes get expressed and things like that. And the way you figure out how genome folds in three dimensions, and this technology came out of Yob Decker's lab in UMass in Worcester. The way you do it is you have a cell and you basically put in formaldehyde, which you can think of as like kind of like a molecular, like a glue, right? And what it does is it goes into the cells and that spaghetti noodle, that telegraph ribbon, whatever, that string, it's folded up. There are pieces of it that are touching other pieces. And this glue will basically glue together DNA sequences or, you know, whatever the metaphor is, ribbon pieces that are touching each other. Right. So if two parts of that ribbon are touching each other, the glue will glue them together. And then you take out all the little glued bits, all these little junctions, and you just read those. And so what it does is it gives you a readout of like this sequence of DNA was touching this sequence of DNA. And you know where they are in the genome. Because the way that high-throughput sequencing works, right, is you're getting millions and millions and millions of these counts. So you're not just saying sequence A was touching sequence B. You know how often it was touching that sequence. And you know that for every combination of sequences. You know how often every part of the genome was touching every other part of the genome. And from that, you can extrapolate the three-dimensional structure of the genome. And I just thought that was amazing because you're using like two-dimensional sequencing to figure out three-dimensional confirmation of the genome. It was like, almost like artistic, right?  

Jon - 00:19:35: Yeah, yeah, yeah.  

Ivan - 00:19:36: This is like a woo-woo, philosophy kind of thing.  

Jon - 00:19:40: Yeah, yeah, yeah.  

Ivan - 00:19:41: Whoa, you can use one-dimensional reads.  

Jon - 00:19:44: Yeah.

Ivan - 00:19:44: Measure the third dimension, bro. Right? And it was so cool. I learned how to do it. And this is, I think, the narrative behind Phage in general is what it was is it was we were extracting a new kind of information. It was, yeah, we're using sequencing, but it's not, we're not, we don't care about the sequence per se, right? The information we're extracting is this who is touching whom three-dimensional proximity information. And the narrative later over time became society takes steps and revolutions. Technically, like scientifically, when they master some new kind of information. We learn how to make the wheel. We learn how to make fire. We learn how to like not poop in the street, right? Learn how antibiotics, like those are all information bits that we've mastered. Like right now, we're undergoing a huge information, like with AI, we're learning how to like master information in a whole different way, right? So information is life. Like life is just basically information being transmitted from one generation to the next. And genomics seems to extract as much information as it can from these living systems. And so what if we suddenly found a new kind of information we could extract? Like, what could we do with it? And so originally, this technology was for looking at the three-dimensional structure of the genome. But while we were working on it, we came up, and this was my work with, I was collaborating with the lab of Jay Shendure in genome sciences, this talented student of his, Josh Burton. And we basically came up with this idea, like, you know, Josh had this thought that basically, hey, you know, you can figure out which sequences are touching each other. You can figure out the three-dimensional structure of the genome. But here's the thing. Imagine you now have, like, a million of these cells, where each one, you cram a bunch of DNA into it. And you do this a million times. Two parts of that DNA molecule, two parts of that spaghetti noodle, if they're close together, like linearly, they're going to be more likely to touch each other in three-dimensional space because they're just closer. Now extrapolate that to the whole genome. Sequences that are close together on the chromosome are going to be more likely to be close together in three-dimensional space. And now you have thousands of cells, millions of cells of the same whatever organism, blood sample, whatever. And you're measuring how much touching there is between everywhere in the genome. And if you basically average out that signal across all these cells, what you're going to find is you're going to be like, if things are close together, they touch more. If things are further apart, they touch less. So they go, hey, we basically are getting a genetic map here. We're basically, right, reverse the formula. Instead of saying what's close together, what's far, you're saying if this touches more, then it's close. If this touches less than as far, we suddenly have figured out how far and how close every part of the genome is on the linear scale. And back in those days, this is about 10 years ago, you could sequence a genome. You could read all the letters of the genome. There are all these machines, all this stuff. The hard part was putting them together, right? So imagine you take a book, you shred it into little pieces, or my little telegraph analogy. You shred it into little pieces, and you copy those pieces a thousand times. You go, I just sequenced this genome a thousand times, a thousand X. And that is true, you did. But you have a jumble of sequence. Like, it's not the genome. It's just the contents of, like, just the letters of the genome. So actually putting it together is much harder than sequencing it. And so you try to take little pieces, stitch them together into longer pieces. But eventually what you're trying to do is get the whole genome, the whole chromosome. And the process of putting little pieces into big, like, chromosomes is called scaffolding, right? Kind of like how you, you know, like scaffolding is, you're making these scaffolds where you put the pieces into place. It's a little bit like putting jigsaw puzzle pieces into the puzzle, right? Like, I have all these metaphors.

 Jon - 00:23:51: No, no, it's good, it's good, it's good. It's good, it's good. 

Ivan - 00:23:54: Right? But so another metaphor for this is to say, okay, I have the jigsaw puzzle pieces, but, like, two-thirds of this jigsaw puzzle is the blue sky. And there's no corners. And some pieces are missing. Like, what do I do? But imagine you knew how far every two pieces were from each other. Imagine you had the physical distance between every pairwise combination of pieces, right? You can tell a computer, put them together, like the ones that are close, put them close. The ones that are far, put them far. And all of that would zip together and would make a genome. And so let's go back to this 3D genome thing, right? We have the ability to know what's touching more, what's touching less. If it's touching more, it's close. If it's touching less, it's far. We have the jigsaw puzzle pieces. We have these pieces of the genome. And now we know what's close, what's far. Let's use a computer to just put them together. And that was what Josh came up with. That was the idea. And there were a couple of other labs at the same time. They had a similar idea. And they were doing, there's lots of, you know, we're not the only lab who kind of worked on it. But that was the principle. And so now we're going from one-dimensional reads, create three-dimensional structure. And that tells you how to put your genome, like it's this crazy. And it's all because of this quirk of new information, right? So this is the thing. You get a new kind of information, suddenly you can do something with it that you couldn't do before at all. And so we started working on a bunch of projects with people. We were collaborating. And one of the funny stories was a group from the NHGRI and the USDA, they were going to do the greatest of all time genome to show off like all the newfangled technology of the day. They picked the humble goat that was going to be the flagship organism. And I'm fairly sure it was just so they could say it was the greatest of all time genome. Loudly proclaimed subsequently. And I went to leadership, I went to my PI and I said, hey, I'm going to sequence this goat. Do our special thing on this goat.  

Jon - 00:26:04: Yeah.  

Ivan - 00:26:04: This was not met with excitement, they were like listen bro, you're a yeast biologist, we're a yeast lab. Like maybe goats is a bit-  

Jon - 00:26:15: Yeah.  

Ivan - 00:26:15: Anyway, eventually that work did get done, and we did have the greatest of all time you know, like they go back to that publication, right? Like, you like Nature Biotech in like 2013, I think, or 2014. Again, it was a way to do something that was previously virtually impossible. At that point, there were only a handful of genomes that were in full chromosomes. It was like yeast, Drosophila, bacteria, that kind of thing. And so we did something with way less effort and resource that just couldn't be done. And we did it again and again and again. And we figured out another thing. Okay, so let's go back to this information, like DNA sequences touching each other. If you have one of these cells, right, and there are sequences inside of it that are like folding over and touching each other, and you're capturing the little touch points. What if you have two different species and you have two cells from two different organisms? If you just ground the DNA up and sequenced it, like two books, whatever, like two telegrams, like, right? You shred them into pieces. You mix the pieces up, two jigsaw puzzles, whatever, right? You take two jigsaw puzzle pieces. You dump the pieces together. You mix them up. Now, you don't know what the puzzles look like. You also don't know which piece belongs to which puzzle. But suddenly, you have this piece of information that tells you this sequence and that sequence were touching each other. This sequence and that sequence were touching each other. So if they're touching, they must have been inside the same puzzle, the same cell in the first place. So now you look at your mixed bag of sequences and you go, okay, well, these two were touching each other. They belong to one of the organisms. These two were touching each other. They belong to another one of the organisms. So you can actually separate those genomes out. Why is this useful? If you work in a completely different field, which is the field of the microbiome. You're dealing with ungodly numbers of microbes that nobody has ever sequenced before. You don't know how many of them there are. If you just take a microbiome sample and you sequence it, you're getting DNA, right? Microbiome is like, like when I talk to lay people, I go, you think that you're human, but actually most of the cells in your body are not human, right? And it's all bacteria, various microbes, right? If you take a scoop full of like a tiny scoop full of dirt from the outside, there's more microbes in there that all the humans that have ever lived, right? And this is everywhere in your water, in your air, like we're breathing it. We're walking scaffolds for bacteria and they're doing everything. They're doing health stuff to us. They're digesting our food. They're like 20% of the proteins in our blood are in some way modified or made by bacteria. Like it's just like a huge thing. It's everywhere. But you're dealing with unknown numbers of unknowns. And so let's say you take that scoop of dirt and there's all these bacteria in it. How do you learn anything about them? You can't grow them. They only grow together in dirt, right? You can't put it on a Petri dish. Like some of them you can, but it's a tiny proportion. You sequence all the DNA at the same time. You grind the thing up. You get all the DNA out. You sequence it. But now you have an unknown number of jigsaw puzzles, unknown structures. You don't know who is in there.  

Jon - 00:29:34: It's a gobbledygook.  

Ivan - 00:29:35: Yeah. It's gobbledygook.  

Jon - 00:29:37: Yeah.  

Ivan - 00:29:37: Suddenly, you know which DNA pieces were touching each other before the cells were broken. And you know which ones belong together. So you can look at the gobbledygook and you can draw a matrix through them. You can say that one and that one and that one and that one. We're all touching each other. They belong to one organism. These belong to, right? And so you can separate. Like I talked about two. Same idea, but now with like a thousand or whatever. So you can actually start getting genomes for bacteria without having to grow anything, without having to separate and sequence single cells. And the other cool thing is within bacterial genomes, there are what's called mobile genetic elements. So plasmids, which are little circles of DNA that oftentimes carry antibiotic resistance or toxicity genes, and they can jump from organism to organism. They're not part of the genome. They're like little separate circles. And the other thing is, and this is something that's a huge focus of ours now, is bacteriophages. Bacteriophages are viruses that infect bacteria. They don't hurt us. They just infect bacteria. But these viruses are the most prevalent biological entity on Earth by like orders of, you know, thousands of times. Every day, 80% of all life on Earth is killed by viruses. Like it's like mostly like stuff in the ocean, right? Like bacteria, algae, plankton, that kind of stuff. But these bacteriophages are in everything. Like they're super pervasive. For example, they're related to controlling the global carbon cycle because, right, you have a bunch of cyanobacteria in the ocean. They absorb light and, you know, CO2. And then they phages kill them and they fall to the bottom of the ocean, right? And so they actually, phages actually control carbon absorption of the ocean.  

Jon - 00:31:26: That's so gnarly. That's so gnarly.  

Ivan - 00:31:29: But let's say you have two bacteria and one of them has a phage and the other one doesn't because phages select are very selective about who they infect, right? If you sequence that mishmush, how would you know that that phage was in bacteria A and not in bacteria B? You would never know. And so there's all this sequence information of phages. Nobody knows where they come from. Nobody knows who they infect. And what's the most interesting thing about a phage? It's like who it's targeting, right? And they can only live in the host. They don't like just live on their own. You can't grow it in a Petri dish. But suddenly, you know which DNA sequences are touching each other inside of cells. So suddenly you say, okay, well, you know what? This phage was touching that bacteria's genome. So the DNA of this phage was touching the DNA of this bacteria. Therefore, that phage was infecting that bacteria.  

Jon - 00:32:15: So dope. 

Ivan - 00:32:16: And suddenly you have the ability to separate all the genomes out and map the plasmids and the phages where they come from without having to culture. And it's literally like something that's just impossible to do with regular sequencing. It's not more sequencing won't solve this problem. Like longer reads won't solve this problem. Right. And we solved it like it was super easy. Like it's just like it's just like does it touch this one or does it touch that one? Like it's not a super fancy like algorithm. That's what I really loved about it is that it was it was literally a simple trick that extracts a new kind of information. Who would have thought that this like three-dimensional genome mapping thing would let us discover viruses where they go? And so it was like a fire in my belly. I was like, we were doing all these projects. We were discovering all these new pathogens and stuff. We did a bunch of beer microbiomes. We were discovering new species left and right. You know, we were assembling these goat genomes and other stuff that were just like doing the impossible with a relatively simple technology. And I like went, I was like, guys, if we don't, like I went to the leadership. And I said, if we don't commercialize this, it's going to be a crime against science. This is like this is solving a major, several major problems in the field. There's a God knows how many applications for it. Right. And that's basically how we started Phase. 

Jon - 00:33:34: That's freaking awesome. 

Ivan - 00:33:36: It's super awesome. Right. 

Outro - 00:33:40: Thanks for listening to part three of our conversation with Ivan Liachko. In the final episode of this series, Ivan walks us through Phase genomics growth journey, how the team bootstrapped their way from a closet lab to a global platform, and why the company's future spans oncology, antibiotic resistance, and synthetic biology. He also shares how Phase is leveraging its massive microbiome and phage datasets, and how scientific creativity, not scale alone, is shaping the next chapter of biotech innovation. If you enjoyed this episode, be sure to follow the show, leave us a review, and share it with a friend. See you next time! The Biotech Startups Podcast is produced by Excedr. Don't want to miss an episode? Search for The Biotech Startups Podcast wherever you get your podcasts and click subscribe. Excedr provides research labs with equipment leases on founder-friendly terms to support paths to exceptional outcomes. To learn more, visit our website, www.excedr.com. On behalf of the team here at Excedr, thanks for listening. The Biotech Startups Podcast provides general insights into the life science sector through the experiences of its guests. The use of information on this podcast or materials linked from the podcast is at the user's own risk. The views expressed by the participants are their own and are not the views of Excedr or sponsors. No reference to any product, service or company in the podcast is an endorsement by Excedr or its guests.