Hacking the genome with Crispr

So, shout out to Janine. So a little bit about my background. So my name is Kathy. Once again, I'm a biotech and healthcare entrepreneur. I've had a pretty unusual journey.

I first started my scientific journey when I was really young, at 14 years old. I started working at university labs. I published my first paper when I was 16 years old. And then when I was 18, I became the youngest founder of a biotech company that was VC-backed. So that was my first company, Renomics.

And then I dropped out of University of Toronto to be part of the Thiel Fellowship, which is when you get $100,000 to drop out of school for two years. And then afterwards, I started a company called LockBio, which is sort of like an online digital health platform where our clients sell prescription medications, telehealth consultations, etc. And most recently, I joined Cold Spring Harbor Labs as part of their board of directors. Cold Spring Harbor Labs is probably one of the most prestigious labs in the world where they discovered DNA, and eight Nobel laureates live there currently. So that's kind of the work I do between healthcare and biotech.

My first company was in the genomic space, and I'm also working on a gene editing project now, which is something that's really interesting, and I'll get into it afterwards. And I also want to give a shout out to Leo here, who I met on this trip. Leo has helped me with setting up the experiments and also finding plans, et cetera, and just being very helpful in general. So give everybody a clap for Leo. So let's start with the genomics industry.

I want to talk a little bit about Moore's Law. So this is a Moore's Law graph about semiconductors. We see that the energy efficiency of computers has doubled every 1.5 years over the last 60 years, and that kind of follows this trend of what makes an innovative technology be very efficient and scalable. This Moore's Law also applies to the cost of the human genome for sequencing.

And so as you can see here, earlier in this millennium, And so as you can see here, earlier in this millennium, one sequence human genome used to cost $100 million to do. Now it costs less than $1,000. So what this represents is that there's a massive opportunity to better read our DNA, and not just our DNA as humans, but also the DNA of animals and plants. And there's a lot that we can do with that data, whether it's reading it better for diagnostics and prevention of diseases, or we can also now edit them, which is now writing our own DNA or writing the DNA of organisms. And one of the things that we're going to do today and tomorrow is we're going to gene edit a real organism.

It's a plant, it's not gonna be anything crazy, but of course if there's interest we can always scale it up to animals and other cool things in the future. So this is another example of the applications of what happens when you can gene sequence really cheaply. So this is an example of using the BRCA gene, the detection of the BRCA gene to prevent and diagnose the early onset or preventing breast cancer and ovarian cancer in women. So if you have a mutation in BRCA1 and BRCA2 that's known to cause disease, a.k.

a. pathogenic, then you're able to prevent it much sooner so that's one of the applications of genetic testing another application maybe some of you guys have heard of this is embryo gene testing so that means you can actually test the genes of your embryo to make sure that they're not going to be carrying diseases and then of course there are always some very I don't know, controversial but also exciting use cases where it can test for features that you want to see in a future kid. And of course, you can get into all sorts of realities where you can edit and change the genomes of the future generations or future animals that would exist in the ecosystem. So this is another visual example of genetics being used in a natural way. So this is like the GFP protein.

This is naturally observable and seen in jellyfish. And it's a bioluminescence that you can actually use CRISPR to put into the genomes of other animals and make them glow-in-the-dark to make them have the same protein, the GFP protein, and characteristics of the jellyfish. So here you can see that you can do it in fish, flies, worms, plants as well. You can actually buy some glow-in-the-dark plants and fish. There's a company called Glowfish, G-L-O-fish, that just makes GFP fish, and they actually make tens of millions of dollars a year just by selling GFP fish, which is actually not hard to make.

We actually have some really cool experiment kits here that some of you might be able to do later in the month, that puts the GFP protein into E. coli, so bacteria, and you can make the bacteria glow in the dark because you're putting the gene from a jellyfish into the bacteria. That's going to be a little harder than the plant one, but we're going to start with the plant one, see how people feel, and then we can do some more fun and complicated stuff like that. But those are the cool, visible, easy applications of gene editing, but it can really go anywhere from here. You know, if you can just imagine, you can take the genes that encode for, let's say the horn on a narwhal or rhino, it can take those genes and put it on a horse and it can actually make a unicorn.

And that's how it would work. You know, we can make any reality happen because we now understand the genome much better than before and we can also have the ability to rewrite the genome by inserting pieces of the genetic code from other species into other species where it doesn't really belong, but, you know, we'll see what happens. That's part of the project I'm working on now, which I'll talk to you guys about in a bit. A little bit more about the genomics industry. So we just talked about the technology, some simple applications, some more, you know, serious applications, and some fun applications.

But where we are as an industry is pretty early. I think this is one of the biggest challenges in the genomics space. You know, we're taking a very innovative technology and we're applying a very antiquated playbook to that technology by pushing it into, you know, drug discovery. Like, we're trying to make gene therapies where, you know, we have not yet found a proper and safe way to deliver it for a lot of these gene therapies. And, you know, we're doing a lot of testing for genes that, you know, there's a lot of VUSs, variants of unknown significance.

So there's a lot of data problems. There's a lot of issues with kind of putting the new technology in an existing playbook and just kind of forcing it to happen. That's why insurance doesn't cover, like healthcare insurance in the U.S. doesn't cover a lot of the genetic tests anymore.

They cover BRCA1 and 2, but not a lot of the other genes. So it just prevents the growth of a really impressive technology. What I'm hoping here is, what I'm hoping will happen in this session, but overall in a group like this, is for everybody to feel like gene editing is something that they can have an intuition about and actually have ideas about and then feel like they can actually do something about it. Like I don't think it should be something that's locked up in a lab with you know PhDs and academia and grants and pharmaceutical companies all the time. I think this is now a technology that should be seen as something that belongs to the people because we're all made out of biology and biology surrounding all of us.

And I think the opportunities are huge. I think it's an over $1 trillion opportunity if we're able to really reimagine reality and the ecosystems around us, redesign how forests, agriculture, animals behave, even humans, whether it's disease prevention or new medicines, new types of animals. Those are all things that can happen once we really have a good grasp of this technology. And there's gonna be so many creative uses of this, even, you know, for example, luxury pets, you know, who doesn't want a unicorn or, what's another one, jackalope, you know, those are really cool mythical animals that never existed until we can actually now create them for the first time as humans. But it's not easy building in bio.

I think one of the reasons why, you know, when I was in the Teal Fellowship, you know, Vitalik, you know, built Ethereum and people built cool, you know, OYO rooms. Those are all software applications or even a little bit of hardware. But no one really took off in the biotech space because it's just so hard to enter the space as a newcomer, as a young person, as someone with a lot of creative ideas because you have to get lab equipment, you have to have access to sterile facilities. A lot of times you have to have a PhD or above to be able to lead a lab with a research focus that you find interesting. And of course, there's a lot of mundane structures like the funding, publishing, other capital incentives that prevent real innovation.

But I think that's all about to change. As you guys are seeing here, we can actually do a gene editing experiment in the middle of Thailand randomly. You know, we don't have to be confined to our computers just coding applications all day. We can actually be doing hands-on experiments and changing physical realities. And this is where network states or pop-up cities, I use it interchangeably, but Janine always corrects me, so I don't know what the right term is.

Yeah, so this is where I think it's really interesting. What does biology look like in a network state or in a pop-up city? How can those two ideas really merge? I think we're going to see a very small example with a simple gene editing experiment, but what can we really escalate that to, and how do we scale it up to really interesting experiments that will benefit all of humans, all of the humans that are part of the experiments, network states, or just around the world. And one thing to really notice here is I think that gene editing in biology is an especially interesting area to focus on because it's the centralizable.

In the past with deep tech in general, it's something that's been very centralized. If you were to build a nuclear plant, nuclear weapons back in the day, you would have to have a very centralized facility. Now really anyone can have a lab, anyone can get access to CRISPR, anybody can get access to cells, DNA, order DNA online. My first company was making variant libraries for pharma companies, synthetic biology companies, basically selling DNA. So you can really buy this stuff online.

You can really buy all the materials you would need and just start gene editing stuff. So that's why it's really decentralized and quite interesting. Biology typically had the characteristic of high technical risk and low market risk, basically meaning that it's the opposite of software. Software is something that has low technical risk and high market risk. Like you would build an application.

It's pretty easy. Like if you code it, it'll be live. But you don't know if people are going to use the application. So there's like a high market risk when it comes to adoption. But biology, on the other hand, typically in the form of pharmaceutical products, it has a high technical risk and a low market risk because it's so hard to find a drug.

Most drugs do not, sorry, most drugs don't get to the end of the clinical trial process. They usually fail. And that's what makes it a high technical risk. But if it does work, if you're able to cure some disease with a drug, then obviously there's no market risk because everybody wants it. But what's really interesting right now is that gene editing has a pretty low technical risk and a low market risk because it's not really about the science anymore.

There's of course a lot of science to be improved on, but as of right now, you can actually start making any organism. So it's actually more of an engineering problem than a scientific one, and it has a low market risk because if you make something really cool, it'll sell. So that's why it's really interesting right now. And there's an opportunity here to create a network of independent labs, you know, just kind of in a decentralized framework to think about it a little bit differently than traditional academia and pharmaceutical companies. and pharmaceutical companies.

And one of the things that makes biology interesting as well to make it in different areas around the world through this idea of network states or pop-up cities is that you can take advantage of the different environments that you're in with different ecological characteristics and diversity. Here, we could not bring any plants to Thailand because that would violate a lot of customs rules. But what we're going to do is find plants in Thailand from plant shops and edit them, gene edit them. So that's really cool. And just for me to rant a little bit more, in the traditional academia system, you know, it's really just funded by taxpayers but when research is published in a peer-reviewed journal what happens is you have to pay for that research article you have to pay to read the research article and then when there's a drug that was developed based on the research that you funded as taxpayers you have to pay a lot of money for the drug and it just doesn't make sense to me so what I would love to do is create a new validation structure and through network states or pop-up cities have people really and it just doesn't make sense to me.

So what I would love to do is create a new validation structure and through network states or pop-up cities, have people really directly benefit from the innovation happening in deep tech and especially bio where you can have new agriculture or pets that are gene edited or medicines and have people directly benefit from that. So changing really the entire structure of how we think about validation incentives and how researchers are incentivized to do the best research possible. So this is what we can do today. What I'm trying to show here is I want to create sort of a mini independent lab. What that looks like is we're going to start with the plant gene editing stuff.

That's to show that if you can code a web application or even not even that, I can't even code a web application, but if you can do like a website, you can definitely gene edit a plant. It's actually not that hard. So I want to show everybody just how easy it is. And if you're interested, there's, course more complicated things you can gene edit, but just starting here, I want everybody to feel like it's something they can do, it's something they feel like they have some intuition and knowledge about, not just something that's locked up in a lab far away and you need a PhD to really understand anything. So that's what we're trying to do.

So these are, so basically what we have here, I have a few of those and we have a bit more and we also have it online. So I'm happy to share it via telegram to everybody. But what we're trying to do here is take these three genes. I'm not going to try to read them, but you guys can see them. These three genes code for enzymes that essentially are the same enzymes you would see in beets, the vegetable, that make them red.

So we're taking those three genes and putting them into a new plant. So what we're trying to do is find a plant that looks like this. It's a green. This is a tobacco plant, but you can really do it on any plant, as long as the plant doesn't have a waxy leaf. And what we're going to do tomorrow, so today we're going to have to do some work as well, but tomorrow we're going to inject these three genes into a plant, an unsuspecting plant in Thailand.

We're going to turn it red. So that's what we're going to do. I feel like this is like a magic trick or something. So these are the three steps. I've just boiled it down to three really simple steps.

So first you grow the Agrobacterium, then you find a plant, and then we're going to inject it and transform it. Injection and transformation are basically interchangeable. I just don't want to use any fancy words here. But Agrobacterium is essentially what we have brought over that contains those three genes, and when we put the Agrobacterium into the plant leaves, those three genes will penetrate the cell wall of the plants and then essentially turn the plant those plant leaf areas red and for the first step there's the agar place that we have to make that would so that's like it's just a plate like this it's a circular plate with agar gel inside and we've already made this from a couple of days ago shout out to Leo once again so these agar place is what allows the acrobacterium to grow and today what we're going to do is have a few volunteers come up and help us put the agrobacterium onto the plates and inoculate it, basically spread it around so that it can grow on the plate over the next day or so and then tomorrow we're going to take the bacteria that has grown that contains the three genes and put it in a media solution, which is just, you know, a solution that allows us to penetrate that, inject that into the plant cell wall, and then over the next two to seven days, we'll see some growth of the redness on the plant leaves. Yeah, and then I'll go back to this slide in a little bit.

This is really just about the instruction part, like the instructions for the step, but what I also need back to this slide in a little bit. This is really just about the instruction part, like the instructions for the step. But what I also need people to do, if you're interested in getting a plant gene edited tomorrow, my one assignment is that you have to go out in Chiang Mai and find a plant. And the only two criteria that I would ask for is one, it cannot be a waxy leaf. Like it can't be, you know, glossy, that type of leaf has to be like this, and it has to be potted so that it has room to grow, and it's not going to die immediately, so we're going to do the first, we're going to do the first step right now, and then we're going to have everybody find a plant tonight, and then tomorrow we're going to do this part, and then over the next few days it should turn red like this.

But you can also make any design you want, so it doesn't have to be like this. You can draw a letter or something. So yeah, let's go back to here, and Leo, would you like to?