# Patrick Hsu: A Trailblazer in Digital Biology

An episode of [Ground Truths](https://invisiblenetwork.ai/podcast/1728526108), 13 Oct 2024, 47 min.

Audio: https://api.substack.com/feed/podcast/150150947/198258b5c9a208b3fc1a6aafc087cb0e.mp3

When I think of digital biology, I think of Patrick Hsu—he’s the prototype, a rarified talent in both life and computer science, who recently led the team that discovered bridge RNAs, what may be considered CRISPR 3.0 for genome editing, and is building new generative A.I. models for life science. You might call them LLLMs-large language of life models. He is Co-Founder and a Core Investigator of the Arc Institute and Assistant Professor of Bioengineering and Deb Faculty Fellow at the University of California, Berkeley. Above is a brief snippet of our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Here’s the transcript with links to the audio and external links to relevant papers and things we discussed. Eric Topol (00:06): Well hello, it's Eric Topol with Ground Truths and I'm really delighted to have with me today Patrick Hsu. Patrick is a co-founder and core investigator at the Arc Institute and he is also on the faculty at the University of California Berkeley. And he has been lighting things up in the world of genome editing and AI and we have a lot to talk about. So welcome, Patrick. Patrick Hsu (00:29): Thanks so much. I'm looking forward to it. Appreciate you having me on, Eric. The Arc Institute Eric Topol (00:33): Well, the first thing I'd like to get into, because you're into so many important things, but one that stands out of course is this Arc Institute with Patrick Collison who I guess if you can tell us a bit about how you two young guys got to meet and developed something that's really quite unique that I think brings together investigators at Stanford, UCSF, and Berkeley. Is that right? So maybe you can give us the skinny about you and Patrick and how all this got going. Patrick Hsu (01:05): Yeah, sure. That sounds great. So we started Arc with Patrick C and with Silvana Konermann, a longtime colleague and chemistry faculty at Stanford about three years ago now, though we've been physically operational just over two years and we're an independent research institute working at the interface of biomedical science and machine learning. And we have a few different aspects of our model, but our overall mission is to understand and treat complex human diseases. And we have three pillars to our model. We have this PI driven side of the house where we centrally fund our investigators so that they don't have to write grants and work on their very best ideas. We have a technical staff side of the house more like you'd see in a frontier AI lab or in biotech industry where we have professional teams of R&D scientists working cross-functionally on higher level organizational wide goals that we call our institute initiatives. (02:05): One focused on Alzheimer's disease experimentally and one that we call a virtual cell initiative to simulate human biology with AI foundation models. And our third pillar over time is to have things not just end up as academic papers, but really get things out into the real world as products or as medicines that can actually help patients on the translational side. And so, we thought that some really important scientific programs could be unlocked by enabling new organizational models and we are experimenting at the institutional scale with how we can better organize and incentivize and support scientists to reach these long-term capability breakthroughs. Patrick, Patrick and Silvana Eric Topol (02:52): So the two Patrick’s. How did you, one Patrick I guess is a multi-billionaire from Stripe and then there's you who I suspect maybe not quite as wealthy as the other Patrick, how did you guys come together to do this extraordinary thing? Patrick Hsu (03:08): Yeah, no, science is certainly expensive. I met Patrick originally through Silvana actually. They actually met, so funny trivia, all three Arc founders did high school science together. Patrick and Silvana originally met in the…

## Transcript

[0:06] SPEAKER_00: Hello, it's Eric Topol with Grand Truce, and I'm really delighted to have with me today Patrick Hsu.
[0:12] SPEAKER_00: Patrick is a co-founder and co-investigator at the ARC Institute,
[0:17] SPEAKER_00: and he's also on the faculty at the University of California,
[0:21] SPEAKER_00: Berkeley,
[0:22] SPEAKER_00: and he has been lighting things up in the world of genome editing and AI,
[0:26] SPEAKER_00: and we have a lot to talk about,
[0:27] SPEAKER_00: so welcome,
[0:28] SPEAKER_00: Patrick.
[0:29] SPEAKER_01: Thanks so much.
[0:30] SPEAKER_01: I'm looking forward to it.
[0:31] SPEAKER_01: I appreciate you having me on, Eric.
[0:33] SPEAKER_00: Well,
[0:34] SPEAKER_00: the first thing I'd like to get into,
[0:36] SPEAKER_00: because you're into so many important things,
[0:38] SPEAKER_00: but one that stands out,
[0:40] SPEAKER_00: of course,
[0:40] SPEAKER_00: is this Art Institute with Patrick Collison,
[0:45] SPEAKER_00: who I guess if you can tell us a bit about how you two young guys got to meet.
[0:50] SPEAKER_00: and developed something that's really quite unique that I think brings together
[0:55] SPEAKER_00: investigators at Stanford,
[0:58] SPEAKER_00: UCSF,
[0:58] SPEAKER_00: and Berkeley.
[0:59] SPEAKER_00: Is that right?
[0:59] SPEAKER_00: So maybe you can give us the skinny about you and Patrick and how all this got going.
[1:05] SPEAKER_01: Yeah, no, sure.
[1:06] SPEAKER_01: That sounds great.
[1:07] SPEAKER_01: So we started ARC with Patrick Stee and with Silvana Kahneman,
[1:13] SPEAKER_01: a longtime colleague and biochemistry faculty at Stanford,
[1:18] SPEAKER_01: about three years ago now,
[1:19] SPEAKER_01: though we've been physically operational just over two years.
[1:23] SPEAKER_01: And we're an independent research institute working at the interface of biomedical
[1:28] SPEAKER_01: science and machine learning.
[1:30] SPEAKER_01: And we have a few different aspects of our model,
[1:33] SPEAKER_01: but our overall mission is to understand and treat complex human diseases.
[1:37] SPEAKER_01: And we
[1:38] SPEAKER_01: have three pillars to our model.
[1:40] SPEAKER_01: We have this PI-driven side of the house where we centrally fund our investigators
[1:44] SPEAKER_01: so that they don't have to write grants and work on their very best ideas.
[1:49] SPEAKER_01: We have a technical staff side of the house,
[1:51] SPEAKER_01: more like you'd see in a frontier AI lab or in biotech industry,
[1:55] SPEAKER_01: where we have professional teams of R&D scientists
[1:58] SPEAKER_01: working cross-functionally on higher level organizational-wide goals that we call
[2:03] SPEAKER_01: institute initiatives,
[2:05] SPEAKER_01: one focused on Alzheimer's disease experimentally,
[2:08] SPEAKER_01: and one that we call a virtual cell initiative to simulate human biology with AI
[2:12] SPEAKER_01: foundation models.
[2:14] SPEAKER_01: And our third pillar over time is to have things not just end up as academic papers,
[2:21] SPEAKER_01: but really get things out into the real world as products or as medicines that can
[2:25] SPEAKER_01: actually help patients on the translational side.
[2:28] SPEAKER_01: And so we thought really,
[2:29] SPEAKER_01: you know,
[2:31] SPEAKER_01: that some really important scientific programs could be unlocked by enabling new
[2:36] SPEAKER_01: organizational models.
[2:38] SPEAKER_01: And we are experimenting at the institutional scale with how we can better organize
[2:45] SPEAKER_01: and incentivize and support scientists to reach these long-term capability breakthroughs.
[2:52] SPEAKER_00: So the two Patrick's, how did you...
[2:56] SPEAKER_00: One, Patrick, I guess, is a multi-billionaire from Stripe.
[3:00] SPEAKER_00: And then there's you, who I suspect maybe not quite as wealthy as the other Patrick.
[3:04] SPEAKER_00: How did you guys come together to do this extraordinary thing?
[3:08] SPEAKER_01: Yeah, no, science is certainly expensive.
[3:10] SPEAKER_01: I met Patrick originally through Silvana, actually.
[3:15] SPEAKER_01: They actually met.
[3:16] SPEAKER_01: So funny trivia, all three ARC founders did high school science there.
[3:23] SPEAKER_01: Patrick and Silvana originally met in the European version of the European young
[3:29] SPEAKER_01: scientist competition in high school.
[3:32] SPEAKER_01: And Silvana and I met during our PhDs at,
[3:36] SPEAKER_01: in her case at MIT,
[3:37] SPEAKER_01: I was at Harvard,
[3:38] SPEAKER_01: but we met at the Broad Institute,
[3:39] SPEAKER_01: sort of also a collaborative Harvard institute.
[3:43] SPEAKER_01: and Harvard Hospital Institute based in Kendall Square.
[3:47] SPEAKER_01: And so we sort of,
[3:49] SPEAKER_01: in various pairwise combinations,
[3:52] SPEAKER_01: known each other for decades and worked together for decades and have all
[3:58] SPEAKER_01: collectively been really excited about science and technology and its potential to
[4:03] SPEAKER_01: accelerate societal progress.
[4:05] SPEAKER_01: Yet we also felt in our own ways that despite a lot of the tremendous progress
[4:11] SPEAKER_01: the structures in which we do this work,
[4:14] SPEAKER_01: fund it,
[4:15] SPEAKER_01: incentivize it,
[4:16] SPEAKER_01: and roll it out into the real world,
[4:18] SPEAKER_01: seems like it's really possible that will undershoot that potential.
[4:23] SPEAKER_01: And if you take 15 years ago,
[4:26] SPEAKER_01: we didn't have the modern transformer that launched the current AI revolution,
[4:31] SPEAKER_01: CRISPR technology,
[4:32] SPEAKER_01: single cell,
[4:34] SPEAKER_01: mRNA technology,
[4:35] SPEAKER_01: or broadly addressable LNPs.
[4:38] SPEAKER_01: That's a tremendous amount of technologies have developed in the next 15 years.
[4:41] SPEAKER_01: We think there's a real unique opportunity for new institutes in the 2020s.
[4:46] SPEAKER_01: to take advantage of all of these breakthroughs and the new ones that are coming to
[4:50] SPEAKER_01: continue to accelerate biological progress,
[4:53] SPEAKER_01: but do so in a way that's fast and flexible and really focused.
[4:58] SPEAKER_00: Yeah, I did want to talk to you a bit.
[5:01] SPEAKER_00: First of all,
[5:01] SPEAKER_00: before I get to the next related topic,
[5:04] SPEAKER_00: I get a kick out of you saying,
[5:06] SPEAKER_00: you know,
[5:06] SPEAKER_00: you worked or knowing each other for decades because I think you're only in your
[5:10] SPEAKER_00: early 30s,
[5:11] SPEAKER_00: right?
[5:11] SPEAKER_00: Yeah.
[5:13] SPEAKER_01: So I was lucky to get an early start.
[5:17] SPEAKER_01: I first started doing research at the local university when I was 14, actually.
[5:23] SPEAKER_01: And I was homeschooled, actually, until college.
[5:26] SPEAKER_01: And so one of the funny things that you get to do when you're homeschooled is,
[5:30] SPEAKER_01: well,
[5:31] SPEAKER_01: you can do whatever you want.
[5:32] SPEAKER_01: And in my case, that was work in the lab.
[5:34] SPEAKER_01: And so I actually worked basically full time as an intern volunteer.
[5:40] SPEAKER_01: cut my teeth in single cell patch clamp,
[5:43] SPEAKER_01: molecular biology,
[5:44] SPEAKER_01: protein biochemistry,
[5:46] SPEAKER_01: two photon confocal imaging,
[5:48] SPEAKER_01: and spiraled from there.
[5:51] SPEAKER_01: I loved the lab.
[5:52] SPEAKER_01: I loved doing bench work.
[5:53] SPEAKER_01: It was much more exciting to me than programming computers, which was what I was doing at the time.
[6:00] SPEAKER_01: And I think these two loves have brought me and us to where we are today.
[6:07] SPEAKER_00: Before you got to Berkeley and ARC,
[6:10] SPEAKER_00: did you,
[6:11] SPEAKER_00: I know you were at Broad Institute,
[6:13] SPEAKER_00: but did you also pick up family training in computer science and AI,
[6:19] SPEAKER_00: or was that something that was kind of just part of the flow?
[6:24] SPEAKER_01: So I grew up coding.
[6:27] SPEAKER_01: I used to work through Perl problem sets before dinner growing up.
[6:33] SPEAKER_01: And so it's just something that you kind of learn natively, just like learning French or Mandarin.
[6:41] SPEAKER_00: That's what I figured.
[6:43] SPEAKER_00: Okay, now this model of Arc Institute
[6:47] SPEAKER_00: came along in a kind of similar timeframe as the ARENA Bioworks in Boston,
[6:54] SPEAKER_00: where some of the faculty left to go to ARENA,
[6:58] SPEAKER_00: like my friend Stuart Schreiber and many others.
[7:01] SPEAKER_00: And then, of course, annotating, Mark, was the Chan Zuckerberg Institute and its biohub and its support.
[7:08] SPEAKER_00: So can you contrast
[7:11] SPEAKER_00: For one,
[7:11] SPEAKER_00: these three different models,
[7:13] SPEAKER_00: because they're both very different than,
[7:14] SPEAKER_00: of course,
[7:15] SPEAKER_00: the traditional NIH pathway,
[7:18] SPEAKER_00: how ARC is similar or different to the others.
[7:21] SPEAKER_00: And obviously, the goal here is accelerating things that are going to really make a difference.
[7:26] SPEAKER_01: Yeah,
[7:26] SPEAKER_01: the first thing I would say zooming out,
[7:28] SPEAKER_01: right,
[7:28] SPEAKER_01: is there have been lots of efforts to experiment with how we do science,
[7:35] SPEAKER_01: the practice of science itself,
[7:37] SPEAKER_01: right?
[7:37] SPEAKER_01: And in fact, a lot of the, I've recently been reading this book, The Demon Under the Microscope.
[7:44] SPEAKER_01: about the history of infectious disease.
[7:45] SPEAKER_01: It talks about how in the 1910s through the 1930s,
[7:49] SPEAKER_01: these German industrial dye manufacturing companies like Bayer and BASF actually
[7:56] SPEAKER_01: launched what became essentially an early model for industrial scale science,
[8:02] SPEAKER_01: where they were trying to develop Prontosil,
[8:04] SPEAKER_01: Salversan,
[8:06] SPEAKER_01: and some of these early anti-infectives that targeted Streptococcus.
[8:11] SPEAKER_01: And these were some of the major breakthroughs that led to huge medical advances
[8:16] SPEAKER_01: tackling infectious disease compared to the more academic,
[8:21] SPEAKER_01: university-bound model.
[8:22] SPEAKER_01: So these trends of industrial versus academic labs and different structures to
[8:28] SPEAKER_01: optimize breakthroughs and applications has been a through-current throughout
[8:33] SPEAKER_01: international science for the last century.
[8:39] SPEAKER_01: way that we do research today,
[8:41] SPEAKER_01: and that's some of our kind of core tenants at Arc is basically it hasn't always
[8:45] SPEAKER_01: been this way,
[8:46] SPEAKER_01: right?
[8:46] SPEAKER_01: It doesn't need to necessarily be this way.
[8:48] SPEAKER_01: And so I think, you know, organizational experiments should really matter.
[8:53] SPEAKER_01: And so, you know, there's CZI,
[8:55] SPEAKER_01: Altos, Arena, Calico, a variety of other kind of organizational experiments.
[9:02] SPEAKER_01: And similarly, we had MRC and Bell Labs and Xerox PARC, NIBR, GNF, Google Research and so on.
[9:12] SPEAKER_01: And so I think there are lots of different ways that you can organize folks.
[9:15] SPEAKER_01: I think at a high level, you can think about ways that you can play with
[9:19] SPEAKER_01: for-profit versus non-profit structures whether you want to be a completely
[9:24] SPEAKER_01: independent organization or if you want to be partnered with universities if you
[9:29] SPEAKER_01: want to be doing application driven science or really blue sky curiosity driven
[9:34] SPEAKER_01: work and i think also thinking through internally the types of expertise that you
[9:40] SPEAKER_01: bring together right you can think of it like a cancer institute maybe is a very
[9:46] SPEAKER_01: vertically integrated model um you have folks working on
[9:49] SPEAKER_01: all kinds of different areas surrounding oncology or immunotherapy, right?
[9:54] SPEAKER_01: And you might call that the Tower of Babel model.
[9:57] SPEAKER_01: The other way that, you know, folks have built institutes, you might call the lily pad model, right?
[10:02] SPEAKER_01: Where you have coverage of as many areas of biomedical research as possible,
[10:07] SPEAKER_01: you know,
[10:07] SPEAKER_01: places like the Whitehead or Salk,
[10:10] SPEAKER_01: right?
[10:11] SPEAKER_01: You know,
[10:11] SPEAKER_01: will be very broad.
[10:12] SPEAKER_01: You'll have plant epigenetics,
[10:14] SPEAKER_01: folks looking at RNA structural biology,
[10:18] SPEAKER_01: people studying yeast cell cycle,
[10:21] SPEAKER_01: folks doing in vivo melanoma models.
[10:23] SPEAKER_01: It's very broad.
[10:24] SPEAKER_01: And I think what we try to do at ARC is think about a model that you might liken
[10:31] SPEAKER_01: more to overlapping Viking shields,
[10:33] SPEAKER_01: where there's sort of five core areas that we're deeply investing in,
[10:38] SPEAKER_01: in genetics and genomics.
[10:42] SPEAKER_01: computation, neuroscience, immunology, and chemical biology.
[10:46] SPEAKER_01: Now,
[10:47] SPEAKER_01: we really think of these as five areas that are maybe the minimal critical mass
[10:52] SPEAKER_01: that you would need to make a dent on something as complicated as complex human diseases.
[10:57] SPEAKER_01: It's certainly not the only thing that you need,
[11:00] SPEAKER_01: but we needed a critical mass of investigators working at least in these areas.
[11:05] SPEAKER_00: Well, yeah, and they really converge on where the hottest advances are being made these days.
[11:11] SPEAKER_00: Now,
[11:12] SPEAKER_00: can you work at Art Institute without being one of these three universities,
[11:17] SPEAKER_00: or is it really that you maintain your faculty and you're kind of part of this
[11:22] SPEAKER_00: other entity?
[11:24] SPEAKER_01: Yeah, so we have a few elements to even just the academic side of the house.
[11:28] SPEAKER_01: We have our core investigators,
[11:29] SPEAKER_01: I'm one of them,
[11:30] SPEAKER_01: where we have duly appointed faculty who retain their ladder rank or tenured
[11:36] SPEAKER_01: appointment in their home department,
[11:37] SPEAKER_01: but their labs are physically sited at the ARC headquarters,
[11:41] SPEAKER_01: where we built out a lab in Stanford Research Park in Palo Alto.
[11:45] SPEAKER_01: And so folks move their labs there.
[11:47] SPEAKER_01: They continue to train graduate students based on whatever graduate programs
[11:51] SPEAKER_01: they're formally affiliated with through their university affiliation,
[11:55] SPEAKER_01: right?
[11:55] SPEAKER_01: So we have nearly 40 PhD students across our labs that are training on site every day.
[12:01] SPEAKER_01: Yeah.
[12:01] SPEAKER_01: So,
[12:02] SPEAKER_01: you know,
[12:03] SPEAKER_01: so in addition to our core investigators,
[12:05] SPEAKER_01: we also have what we call our innovation investigators,
[12:07] SPEAKER_01: which is more of a grant program to faculty at our partner universities.
[12:12] SPEAKER_01: They receive unrestricted funding from us to,
[12:15] SPEAKER_01: you know,
[12:15] SPEAKER_01: seed a new project or accelerate an existing area in their group.
[12:20] SPEAKER_01: And, you know, their labs stay at their home campus and they just, you know, you know.
[12:26] SPEAKER_01: get that funding to augment their work.
[12:29] SPEAKER_01: The third way is our technical staff model,
[12:31] SPEAKER_01: where folks basically just come work at Arc,
[12:34] SPEAKER_01: and many of them also are establishing their own research groups focusing on
[12:40] SPEAKER_01: technology R&D areas.
[12:42] SPEAKER_01: And so we have five of those technology centers working in molecular engineering,
[12:48] SPEAKER_01: multi-omics,
[12:49] SPEAKER_01: complex cellular models,
[12:51] SPEAKER_01: in vivo models,
[12:52] SPEAKER_01: and in machine learning.
[12:54] SPEAKER_00: Yeah, that's a great structure.
[12:56] SPEAKER_00: In fact,
[12:57] SPEAKER_00: you know,
[12:57] SPEAKER_00: just a few months ago,
[12:58] SPEAKER_00: Patrick Hollison,
[12:59] SPEAKER_00: the other Patrick,
[13:01] SPEAKER_00: came to Stanford HAI where I'm on the board and presented things.
[13:06] SPEAKER_00: And, you know, you've summarized it really well.
[13:08] SPEAKER_00: And it's very different than the other models and other entities, companies included that you mentioned.
[13:15] SPEAKER_00: It's really very impressive.
[13:16] SPEAKER_00: Now, speaking of impressive,
[13:19] SPEAKER_00: On June 26,
[13:19] SPEAKER_00: this past few months ago,
[13:23] SPEAKER_00: which incidentally is coincident with the draft genome in the year 2000,
[13:27] SPEAKER_00: the human sequence,
[13:30] SPEAKER_00: you and your colleagues,
[13:31] SPEAKER_00: perhaps the most impressive jump in terms of an ARC Institute contribution,
[13:39] SPEAKER_00: published two papers back-to-back in Nature about bridge RNA.
[13:47] SPEAKER_00: I get you to describe this breakthrough in genome editing.
[13:52] SPEAKER_00: Some would call it genome editing 3.0 or CRISPR 3.0, whatever.
[13:58] SPEAKER_00: What we have today in the clinic with the approval of CRISPR 1.0 for sickle cell
[14:06] SPEAKER_00: and thalassemia is actually quite crude.
[14:09] SPEAKER_00: I think most people will know it's just a double-stranded DNA cleavage with all
[14:15] SPEAKER_00: sorts of issues about repair,
[14:17] SPEAKER_00: and it's not very precise.
[14:19] SPEAKER_00: And so CRISPR 2.0 is supposed to be represented by
[14:23] SPEAKER_00: David Hsu's contributions and his efforts abroad like crime and base editing.
[14:30] SPEAKER_00: And then comes yours.
[14:32] SPEAKER_00: So maybe you can tell us about it and how it has to be viewed as quite an important advance.
[14:39] SPEAKER_01: The first thing I would say before CRISPR is that we had RNA interference.
[14:46] SPEAKER_01: And so even before this modern genome editing revolution with programmable CRISPRs,
[14:53] SPEAKER_01: we had this technology that had a lot of the core selling points as well.
[14:59] SPEAKER_01: Any target will now become drug-willed to us.
[15:02] SPEAKER_01: We simply need to reprogram a guide RNA, and we can get genetic access to things that are intracellular.
[15:10] SPEAKER_01: And I think both the discovery of RNA interference by Craig Mello and Andy Fire or
[15:16] SPEAKER_01: the invention or discovery of programmable CRISPR technologies,
[15:22] SPEAKER_01: both depend on the same fundamental biological mechanism.
[15:25] SPEAKER_01: These non-coding guide RNAs that are essentially a short RNA search stream that you
[15:31] SPEAKER_01: can easily reprogram to retarget a desired enzyme function.
[15:35] SPEAKER_01: And natively, both RNAi and CRISPR are molecular scissors.
[15:40] SPEAKER_01: They're RNA or DNA nucleases that can be reprogrammed to different regions of the
[15:45] SPEAKER_01: genome or the transcriptome to make a cut.
[15:48] SPEAKER_01: And as bioengineers,
[15:50] SPEAKER_01: we have come up with all kinds of creative ways to leverage the ability to make
[15:54] SPEAKER_01: site-specific cuts to do all kinds of incredible things,
[15:59] SPEAKER_01: including genome editing or beyond,
[16:01] SPEAKER_01: like transcriptional up or down regulation,
[16:04] SPEAKER_01: molecular imaging, and so on and so forth, right?
[16:08] SPEAKER_01: And so the first thing that we started thinking about in our lab was,
[16:12] SPEAKER_01: why would Mother Nature have stopped only at RNAi and CRISPR?
[16:17] SPEAKER_01: There probably are lots of other non-coding RNAs out there that might be able to be programmable.
[16:23] SPEAKER_01: And if they did exist,
[16:26] SPEAKER_01: they'd probably also do more complicated and interesting things than just guide a
[16:30] SPEAKER_01: molecular scissors.
[16:32] SPEAKER_01: So that was sort of the first core kind of intuition that we had.
[16:37] SPEAKER_01: The second intuition that we had on the technology side,
[16:40] SPEAKER_01: I was just wearing my biology hat,
[16:41] SPEAKER_01: put on my technology hat,
[16:43] SPEAKER_01: is the thing that we call genome editing today hardly involves the genome,
[16:49] SPEAKER_01: right?
[16:50] SPEAKER_01: It's really,
[16:50] SPEAKER_01: you know,
[16:51] SPEAKER_01: you're making a cut to change an individual base or an individual gene,
[16:56] SPEAKER_01: right,
[16:56] SPEAKER_01: or locus.
[16:57] SPEAKER_01: So really you're doing small scale, single locus editing, right?
[17:01] SPEAKER_01: So you might call it gene level or locus level cuts, right?
[17:05] SPEAKER_01: And what you really want to be able to do is do things at the genome scale, right?
[17:11] SPEAKER_01: At 100 KB, a megabase at the chromosome scale, right?
[17:15] SPEAKER_01: And I think that's where I think the field will inevitably go if you follow the
[17:20] SPEAKER_01: technology curves of longer and longer range gene sequencing,
[17:24] SPEAKER_01: longer and longer range gene synthesis,
[17:26] SPEAKER_01: and then longer and longer range gene editing.
[17:29] SPEAKER_01: And so what would that look like?
[17:33] SPEAKER_01: And we started thinking,
[17:34] SPEAKER_01: could there be essentially recombination technologies that allow you to do cut and
[17:41] SPEAKER_01: paste in a single step?
[17:43] SPEAKER_01: Right now,
[17:44] SPEAKER_01: the reason for that is the way that we do gene editing today involves a cut and
[17:49] SPEAKER_01: then a multi-step process of cellar DNA repair that resolves the cut to make the
[17:54] SPEAKER_01: insertion or the air print,
[17:56] SPEAKER_01: the lesion,
[17:57] SPEAKER_01: or the modification that that ends up happening.
[17:59] SPEAKER_01: And so it's very complicated.
[18:02] SPEAKER_01: Um,
[18:02] SPEAKER_01: and whether that's nucleases or base or prime editing,
[18:05] SPEAKER_01: you're all generally limited to the small scale,
[18:08] SPEAKER_01: single locus changes.
[18:09] SPEAKER_01: However, there are natural mechanisms that have solved this cut and paste problem, right?
[18:15] SPEAKER_01: There are these viruses or bacterial versions of viruses known as phage that have
[18:22] SPEAKER_01: generally been trying to insert their multi-kilobase genomes into bacterial hosts
[18:28] SPEAKER_01: and specialize throughout billions of years.
[18:31] SPEAKER_01: So our core thought was, well, if there are these new non-coding RNAs
[18:36] SPEAKER_01: what kind of functions would we be excited about?
[18:38] SPEAKER_01: Can we look in these mobile genetic elements, these so-called jumping genes for new mechanisms?
[18:45] SPEAKER_01: They're incredibly widespread.
[18:46] SPEAKER_01: Transposons are thought to be some of the most diverse enzyme mechanisms found in nature.
[18:53] SPEAKER_01: And so we started computationally by asking ourselves a very simple question.
[19:00] SPEAKER_01: If a mobile element inserts itself into foreign DNA,
[19:03] SPEAKER_01: and it's able to somehow be programmable,
[19:06] SPEAKER_01: presumably the inside,
[19:08] SPEAKER_01: something encoded in the inside of the element is predictive of some sequence on
[19:12] SPEAKER_01: the outside of the element,
[19:15] SPEAKER_01: right?
[19:15] SPEAKER_01: And so that was sort of the core insight we took,
[19:17] SPEAKER_01: and we thought,
[19:18] SPEAKER_01: let's look across the boundaries of many different mobile genetic elements,
[19:23] SPEAKER_01: right?
[19:23] SPEAKER_01: And we zoomed in on a particular subfamily of these MGEs,
[19:27] SPEAKER_01: known as insertion sequence elements,
[19:29] SPEAKER_01: or IS elements,
[19:31] SPEAKER_01: which are the most autonomous minimal transposons.
[19:34] SPEAKER_01: Normally,
[19:34] SPEAKER_01: transposons have all kinds of genes that they use to hitchhike around the genomic
[19:39] SPEAKER_01: galaxy and endow the bacterial host with some fitness advantage.
[19:44] SPEAKER_01: some ability to metabolize some copper and some host or some metal, right?
[19:50] SPEAKER_01: And these IS elements have only the enzymes that they need to jump around, right?
[19:56] SPEAKER_01: And if you identify the boundaries of these using kind of modern computational methods,
[20:02] SPEAKER_01: this is actually a really non-trivial problem,
[20:04] SPEAKER_01: but if you solve that problem to figure out with nucleotide resolution where the
[20:08] SPEAKER_01: element boundaries end,
[20:09] SPEAKER_01: and then you look for the open reading frame,
[20:12] SPEAKER_01: of the transposase enzyme inside of this element, you'll find that it's not just that coding sequence.
[20:19] SPEAKER_01: There are also these non-coding flanks inside of the element boundaries.
[20:23] SPEAKER_01: And when we looked across the non-coding,
[20:26] SPEAKER_01: the entire IS family tree,
[20:29] SPEAKER_01: there are hundreds of these different types of elements.
[20:32] SPEAKER_01: We found that this particular family, IS110, had the longest non-coding ends of all IX elements.
[20:39] SPEAKER_01: And we started doing experiments in the lab to try to figure out how these work.
[20:44] SPEAKER_01: And what we found was that these elements are cut and paste elements.
[20:48] SPEAKER_01: So they excise themselves into a circular form and paste themselves back into a target site linearly.
[20:55] SPEAKER_01: But the circularization of this element brings together two distal ends together,
[21:02] SPEAKER_01: which brings together a minus 35 and a minus 10 box that create and reconstitute a
[21:08] SPEAKER_01: canonical bacterial transcriptional promoter.
[21:11] SPEAKER_01: This essentially is like plugging a plug into an intellectual socket in the wall,
[21:16] SPEAKER_01: and it jacks up transcription.
[21:18] SPEAKER_01: Now,
[21:19] SPEAKER_01: you would think this transcription would turn on the transposase enzyme so it can
[21:23] SPEAKER_01: jump around more,
[21:24] SPEAKER_01: but it transcribes a non-coding RNA out of this non-coding ent.
[21:30] SPEAKER_01: We're like, holy crap.
[21:31] SPEAKER_01: You know, are these RNAs actually involved in regulating the transposon?
[21:37] SPEAKER_01: Now, the boring answer would be, oh, it regulates the expression.
[21:41] SPEAKER_01: It's like an antisense regular or something.
[21:43] SPEAKER_01: The exciting answer would be, oh, it's a new type of guide RNA and you found an RNA guided integrase.
[21:50] SPEAKER_02: Right.
[21:50] SPEAKER_01: Now,
[21:51] SPEAKER_01: so we started zooming in bound dramatically on this,
[21:54] SPEAKER_01: and we undertook a co-variation analysis where we were able to show that this
[21:58] SPEAKER_01: cryptic non-coding RNA has a totally novel guide RNA structure,
[22:02] SPEAKER_01: totally distinct from RNAi or CRISPR guide RNAs.
[22:05] SPEAKER_01: And it had a target site that co-varied with the target site of the element.
[22:12] SPEAKER_01: And so we're like, oh, wow, this could be a programmable transposase.
[22:16] SPEAKER_01: The second thing that we found was even more surprising.
[22:19] SPEAKER_01: There was a second region of complementarity in that same RNA that recognized the
[22:24] SPEAKER_01: donor sequence,
[22:26] SPEAKER_01: which is the circularized element itself.
[22:28] SPEAKER_01: And so this was the first example of a bispecific guide RNA and also the first
[22:34] SPEAKER_01: example of RNA-guided self-recognition by a mobile genetic element.
[22:38] SPEAKER_00: And...
[22:39] SPEAKER_00: It's pretty extraordinary because basically you did a systematic assessment of
[22:44] SPEAKER_00: jumping genes or transposons,
[22:47] SPEAKER_00: and you found that they contain things that previously were not at all recognized.
[22:55] SPEAKER_00: And then you have a way to program these to edit,
[22:59] SPEAKER_00: change the genome without having to do any cuts or nicks,
[23:03] SPEAKER_00: right?
[23:05] SPEAKER_01: Yeah,
[23:05] SPEAKER_01: so what we showed in a test tube is when we took this so-called bridge RNA,
[23:10] SPEAKER_01: which we named because it bridges the target and donor together,
[23:15] SPEAKER_01: along with the recombinase enzyme,
[23:17] SPEAKER_01: so the two-component system,
[23:18] SPEAKER_01: those are the only two things that you need,
[23:21] SPEAKER_01: they're able to cut and paste DNA and recombine them in a test tube,
[23:25] SPEAKER_01: right,
[23:26] SPEAKER_01: without any DNA repair,
[23:28] SPEAKER_01: meaning that it's independent of cellular DNA repair,
[23:31] SPEAKER_01: and it does strand-nicking
[23:33] SPEAKER_01: exchange, junction resolution, and religation all in a single mechanism.
[23:39] SPEAKER_01: So that's when we got super excited about its potential applications as a bioengineering tool.
[23:46] SPEAKER_00: Yeah, it's pretty extraordinary.
[23:48] SPEAKER_00: And have you already gone into in vivo assessment?
[23:54] SPEAKER_01: In our initial set of papers,
[23:57] SPEAKER_01: what we showed is that these are programmable and functional recombinases in a test
[24:02] SPEAKER_01: tube and in bacterial cells.
[24:05] SPEAKER_01: And by reprogramming the target and donor the right way, you can...
[24:10] SPEAKER_01: use these enzymes not just for insertion but also for flipping and cutting out dna
[24:16] SPEAKER_01: right and so we actually have in a single mechanism the ability to do bridge
[24:21] SPEAKER_01: editing if you will for universal dna recombination insertion excision or inversion
[24:27] SPEAKER_01: similar to what folks have been doing for decades with cree recombinates but with
[24:31] SPEAKER_01: fully programmable recognition sequences right
[24:36] SPEAKER_01: The work that we're doing now in the lab,
[24:37] SPEAKER_01: as you can imagine,
[24:39] SPEAKER_01: is to adapt these into robust tools for mammalian genome editing,
[24:44] SPEAKER_01: including,
[24:45] SPEAKER_01: of course,
[24:45] SPEAKER_01: human genomes.
[24:47] SPEAKER_01: We're excited about this.
[24:48] SPEAKER_01: We're making good progress.
[24:51] SPEAKER_01: CRISPR has had thousands of labs over the last 10,
[24:55] SPEAKER_01: 15 years working on it to make these therapeutic level potency and selectivity.
[25:02] SPEAKER_01: We're going to work and follow that same blueprint
[25:05] SPEAKER_01: for getting bridge systems to kind of get to that level of performance.
[25:09] SPEAKER_01: But we're on the path and we're very optimistic for the future.
[25:12] SPEAKER_00: Yeah, I think it's quite extraordinary.
[25:15] SPEAKER_00: And it's a whole different look to what we've been seeing,
[25:18] SPEAKER_00: you know,
[25:18] SPEAKER_00: in the CRISPR era for,
[25:20] SPEAKER_00: you know,
[25:20] SPEAKER_00: over the past decade and how that's been advancing and getting more specific and
[25:26] SPEAKER_00: less need for repair.
[25:27] SPEAKER_00: But, you know, and being able to be more versatile.
[25:30] SPEAKER_00: But this takes yet another dimension.
[25:32] SPEAKER_00: Now, this brings me to the field that you when I think of this term digital biology, I think of you.
[25:39] SPEAKER_00: okay and now our um our mutual acquaintance jensen wong who everybody knows now um
[25:48] SPEAKER_00: back some months ago he wrote and said at conference where do i think the next
[25:53] SPEAKER_00: amazing revolution is going to come and this is going to be flat out one of the
[25:58] SPEAKER_00: biggest ones ever there is no question that digital biology is going to be it for
[26:03] SPEAKER_00: the first time in human history biology has the opportunity to be engineering
[26:08] SPEAKER_00: not science so can you critique jensen's uh is he is he right uh and tell us what
[26:16] SPEAKER_00: you how you conceive the field of digital biology if you look at gene therapy today
[26:23] SPEAKER_01: the core concepts are actually remarkably simple they're elegant of course you're
[26:29] SPEAKER_01: missing a broken gene you need to put it back and that can be curative right very
[26:35] SPEAKER_01: simple powerful concept
[26:37] SPEAKER_01: However,
[26:38] SPEAKER_01: for complex diseases where you don't have just a single gene that goes wrong,
[26:43] SPEAKER_01: in many cases,
[26:44] SPEAKER_01: we actually have no idea what to do.
[26:46] SPEAKER_01: And in fact,
[26:47] SPEAKER_01: when you're trying to put in DNA that's over more than a gene scale,
[26:53] SPEAKER_01: we kind of very quickly run out of ideas.
[26:57] SPEAKER_01: Is it a car and a cytokine, a car and a cytokine and another thing?
[27:02] SPEAKER_01: And then we're kind of out of ideas.
[27:05] SPEAKER_01: And so we started thinking in the lab, you know, how can we actually design genomes, right?
[27:13] SPEAKER_01: Where it's not just let's reduce the genome into individual Lego blocks,
[27:18] SPEAKER_01: iGEM style with promoters and different genes that we just sort of shuffle the Lego
[27:23] SPEAKER_01: blocks around,
[27:24] SPEAKER_01: but actually use AI to design genome sequences,
[27:29] SPEAKER_02: right?
[27:29] SPEAKER_02: Yeah.
[27:29] SPEAKER_01: So to do that,
[27:31] SPEAKER_01: we thought we would have to,
[27:32] SPEAKER_01: first of all,
[27:33] SPEAKER_01: train a model that can learn and decode the foreign language of biology and use
[27:40] SPEAKER_01: that in order to design sequences.
[27:42] SPEAKER_01: And so we sort of have been training DNA foundation models and virtual cell models
[27:48] SPEAKER_01: at ARC,
[27:49] SPEAKER_01: sort of a major effort of ours,
[27:51] SPEAKER_01: where the first thing that we tried was to take
[27:53] SPEAKER_01: a variance of transformer architecture that's used to train ChatGPT from OpenAI,
[28:01] SPEAKER_01: but instead apply this to study the next DNA token.
[28:07] SPEAKER_01: Now,
[28:08] SPEAKER_01: the interesting thing about next token prediction in English is that you can
[28:12] SPEAKER_01: actually learn a surprising amount of information by just predicting the next word.
[28:17] SPEAKER_01: You can learn world knowledge is the capital of
[28:21] SPEAKER_01: Azerbaijan, is it Baku or is it London?
[28:25] SPEAKER_01: Or if you're walking around in the kitchen,
[28:27] SPEAKER_01: then the next text is,
[28:30] SPEAKER_01: I then left the kitchen or the bathroom.
[28:33] SPEAKER_01: Now you're learning about spatial reasoning.
[28:37] SPEAKER_01: And so you can also learn translation, obviously.
[28:40] SPEAKER_01: And so similarly, I think
[28:42] SPEAKER_01: Predicting the next token or the next base in DNA can lead you to learn about
[28:48] SPEAKER_01: molecular biochemistry,
[28:49] SPEAKER_01: like is the next amino acid residue hydrophobic or hydrophilic?
[28:54] SPEAKER_01: And it can teach you about the mechanics of some catalytic binding pocket or something.
[29:00] SPEAKER_01: You can learn about a disease mutation, right?
[29:04] SPEAKER_01: Is the next base the sickling base or the wild type base?
[29:07] SPEAKER_01: and so on and so forth.
[29:09] SPEAKER_01: And what we found was that at massive scale,
[29:12] SPEAKER_01: DNA foundation models learn about molecular function,
[29:16] SPEAKER_01: not just at the DNA level,
[29:18] SPEAKER_01: but also at the RNA and the proteins.
[29:21] SPEAKER_01: And indeed,
[29:21] SPEAKER_01: we could use these to design molecular systems like CRISPR-Cas systems,
[29:27] SPEAKER_01: where you have a protein and a guide RNA.
[29:30] SPEAKER_01: It could also design new DNA transposons
[29:34] SPEAKER_01: And we could design sequences that look plausibly like real genomes,
[29:40] SPEAKER_01: where we generate a megabase,
[29:41] SPEAKER_01: a million bases of continuous genome sequence.
[29:45] SPEAKER_01: And it really looks and feels like it could be a blurry picture of something that
[29:51] SPEAKER_01: you would actually sequence in the wild.
[29:54] SPEAKER_01: This has been a wonderful collaboration with Brian He, a PI at Stanford and an ARC investigator.
[30:01] SPEAKER_01: And we're really excited about what we've seen in this work because it promises
[30:10] SPEAKER_01: better performance with even more scale.
[30:14] SPEAKER_01: And so simply by scaling up these models,
[30:18] SPEAKER_01: by adding in more compute,
[30:19] SPEAKER_01: more training data or more powerful models,
[30:23] SPEAKER_01: they're going to get sharper and sharper.
[30:24] SPEAKER_01: Yeah.
[30:25] SPEAKER_00: Well, this whole use of large language models for the language of life.
[30:32] SPEAKER_00: whether it's the genome proteins and and on and on actually rna and they even cells
[30:40] SPEAKER_00: has really taken root and of course this is really one of the foundations of that
[30:45] SPEAKER_00: field of digital biology which brings together uh generative ai ai tools and uh
[30:51] SPEAKER_00: trying to
[30:53] SPEAKER_00: push forward our understanding in biology and also obviously what's been emphasized
[30:57] SPEAKER_00: drug discovery perhaps has been emphasized even too much because we still have a
[31:01] SPEAKER_00: lot to learn about biology but that gets me to these models like today alpha
[31:08] SPEAKER_00: proteus was announced by deep mind as as we all know alpha fold one two now three
[31:16] SPEAKER_00: they were kind of precursors being able to predict
[31:19] SPEAKER_00: amino acid 3D structure, proteins from amino acid 3D structure.
[31:25] SPEAKER_00: And that kind of took the field by a little bit like chat GPT for life science.
[31:31] SPEAKER_00: But now it's like a new model all the time.
[31:34] SPEAKER_00: So you've been working on various models and ARC Institute.
[31:39] SPEAKER_00: How do you see this unfolding?
[31:40] SPEAKER_00: Are we just going to
[31:41] SPEAKER_00: have every aspect of the language of life being approached in all the different interactions.
[31:49] SPEAKER_00: And this is going to help us get to a much more deep level of understanding.
[31:56] SPEAKER_01: I'll say two things.
[31:57] SPEAKER_01: The first is a lot of models that you just described are what I would call
[32:01] SPEAKER_01: task-specific models,
[32:03] SPEAKER_01: a model for de novo design of a binder,
[32:06] SPEAKER_01: a model for protein structure prediction.
[32:09] SPEAKER_01: And there are other models for protein fitness or for RNA structure prediction,
[32:15] SPEAKER_01: et cetera,
[32:15] SPEAKER_01: et cetera,
[32:16] SPEAKER_01: right?
[32:17] SPEAKER_01: And I think what we're going to move towards are more unifying models,
[32:22] SPEAKER_01: where there's different classes of models at different levels of scale.
[32:28] SPEAKER_01: So we will have these at atomic level models for looking at generative chemistry or ligand docking.
[32:36] SPEAKER_01: We have models that can unify genomes.
[32:41] SPEAKER_01: and their molecules and we have models that can modify sort of unify cells and
[32:46] SPEAKER_01: tissues right and so for example you know if you took uh an h e stain of some liver
[32:54] SPEAKER_01: right there are folks building models where you can then predict what the single
[32:58] SPEAKER_01: cell spatial transcriptome will look like of that model and that's obviously
[33:03] SPEAKER_01: operating at a very different level of
[33:06] SPEAKER_01: action than a de novo protein binder.
[33:09] SPEAKER_01: But in the long run, all of these are going to get, I think, unified.
[33:13] SPEAKER_01: I think the reason why this is possible is that biology,
[33:17] SPEAKER_01: unlike physics,
[33:19] SPEAKER_01: actually has this unifying theory of evolution that runs across all of its length
[33:24] SPEAKER_01: scales from atomic,
[33:25] SPEAKER_01: molecular,
[33:26] SPEAKER_01: cellular,
[33:26] SPEAKER_01: organismal,
[33:27] SPEAKER_01: to entire ecosystems,
[33:29] SPEAKER_02: right?
[33:29] SPEAKER_01: And the promise of these models is no short than to make biology a predictive discipline.
[33:37] SPEAKER_01: Six,
[33:38] SPEAKER_01: the experimentalists win the big prizes for the theorists when they measure
[33:43] SPEAKER_01: gravitational waves or whatever.
[33:45] SPEAKER_01: But in biology, we're very practical people.
[33:48] SPEAKER_01: You do something three times and do a t-test, right?
[33:52] SPEAKER_01: And,
[33:53] SPEAKER_01: you know,
[33:53] SPEAKER_01: I think my prediction is we can actually gauge the success of these LLMs or
[33:59] SPEAKER_01: whatever in biology by how much we respect theory in this field.
[34:04] SPEAKER_00: Yeah, well, that's a really interesting perspective, important perspective, because
[34:10] SPEAKER_00: the proliferation of models,
[34:13] SPEAKER_00: which we're going to get into,
[34:14] SPEAKER_00: not just doing the things that you described,
[34:17] SPEAKER_00: but also being able to be pseudoscientists,
[34:21] SPEAKER_00: the so-called AI scientists.
[34:24] SPEAKER_00: Maybe you could comment about that concept, because that's been the idea that
[34:30] SPEAKER_00: Everything from the question that could be asked to the hypothesis and the
[34:35] SPEAKER_00: experiments design and the analysis of data and then feedback.
[34:39] SPEAKER_00: So, you know, what is the role of the scientists?
[34:43] SPEAKER_00: That seems to have been overplayed and maybe you can put that in context.
[34:48] SPEAKER_01: So yeah,
[34:48] SPEAKER_01: right now there's a lot of excitement that we can use AI agents,
[34:51] SPEAKER_01: not just to do software enterprise workflows,
[34:55] SPEAKER_01: but to be a research assistant.
[34:57] SPEAKER_01: And then over time,
[34:58] SPEAKER_01: it's self and autonomous research scientists that can read the literature,
[35:02] SPEAKER_01: come up with an idea,
[35:04] SPEAKER_01: maybe run a bunch of robots in the lab or do a bunch of computational analyses.
[35:10] SPEAKER_01: And then potentially even
[35:13] SPEAKER_01: analyze data, conclude what is going on, and actually write an entire paper.
[35:19] SPEAKER_01: Now, I think the vision of this is compelling in the long term.
[35:25] SPEAKER_01: I think the question is really about time scale.
[35:27] SPEAKER_01: If you break down the scientific method into its constituent parts,
[35:31] SPEAKER_01: like hypothesis generation,
[35:33] SPEAKER_01: doing an experiment,
[35:34] SPEAKER_01: analyzing the experiment,
[35:35] SPEAKER_01: and iterating,
[35:37] SPEAKER_01: we're clearly going to use AI of some kind at every single step of this cycle.
[35:43] SPEAKER_01: I think different steps will require different levels of maturity.
[35:49] SPEAKER_01: The way that I would liken this is just wet lab automation.
[35:52] SPEAKER_01: Folks have dreamed about having pipetting robots that just do their Western blots
[35:57] SPEAKER_01: and do their cell culture for them for generations.
[36:01] SPEAKER_01: But of course,
[36:01] SPEAKER_01: today,
[36:02] SPEAKER_01: they don't actually really feel fundamentally different from the same ones that we
[36:07] SPEAKER_01: had in the 90s,
[36:08] SPEAKER_01: let's say.
[36:11] SPEAKER_01: Obviously,
[36:11] SPEAKER_01: they're getting better,
[36:12] SPEAKER_01: but it seems to me one of the trends I'm very bullish about is the explosion of
[36:18] SPEAKER_01: humanoid robots and robot foundation models that have a world model and a sense of
[36:23] SPEAKER_01: physics and proportionate space loaded onto them.
[36:26] SPEAKER_01: Within five years,
[36:28] SPEAKER_01: we're going to have home robots that can fold your clothes,
[36:32] SPEAKER_01: that can organize your kitchen,
[36:35] SPEAKER_01: and do all of this while you're sleeping so you wake up to a clean home every day.
[36:39] SPEAKER_00: It's not going to be just Roomba anymore.
[36:42] SPEAKER_00: There's going to be a lot more.
[36:44] SPEAKER_00: But it isn't just the hardware.
[36:46] SPEAKER_00: It's also the agents playing in software, right?
[36:50] SPEAKER_01: It's the integrated loop of the hardware and the software where the ability to make
[36:56] SPEAKER_01: the same machine generally intelligent will make it adaptable to a broad array of tasks.
[37:02] SPEAKER_01: Now, what I'm excited about is those generally intelligent humanoid robots coming into the lab.
[37:08] SPEAKER_01: Where instead of creating a centrifuge or a new type of pipetter that's optimized
[37:14] SPEAKER_01: for your Backman or Hamilton device,
[37:18] SPEAKER_01: instead you just have robot arms that you snap onto the edge of the bench,
[37:21] SPEAKER_01: and then they just work alongside you.
[37:24] SPEAKER_01: And I do think that's coming,
[37:25] SPEAKER_01: although it'll take a lot of hardware and software and computer vision engineering
[37:30] SPEAKER_01: to make that possible.
[37:32] SPEAKER_00: Yeah,
[37:32] SPEAKER_00: and I think also going back to originating the question,
[37:36] SPEAKER_00: there still is quite a debate about the creativity and the lack of any simulation
[37:43] SPEAKER_00: of AGI,
[37:44] SPEAKER_00: whatever that means anymore.
[37:46] SPEAKER_00: And so the human in the loop part of this is obviously, I think, still of critical nature.
[37:53] SPEAKER_00: Now, the other thing I learned about you is you have a great sense of humor.
[37:58] SPEAKER_00: So which is really important, by the way.
[38:01] SPEAKER_00: And recently,
[38:02] SPEAKER_00: which is great that you're active on X or Twitter,
[38:06] SPEAKER_00: because that's one way we get to see what you're thinking on a day to day basis.
[38:11] SPEAKER_00: But I think you put out a poll, which was really quite provocative.
[38:17] SPEAKER_00: And it was about here's what it said.
[38:20] SPEAKER_00: Do more people in the world truly, let's emphasize truly, understand transformers or health insurance?
[38:30] SPEAKER_00: And interestingly, you got, you know, 49% for transformers and 51% for health insurance.
[38:36] SPEAKER_00: Can you tell us what you're thinking when you put that poll together?
[38:39] SPEAKER_00: Because obviously a lot of people don't understand either of these.
[38:44] SPEAKER_01: I think the core question is there are different ways of looking at the world,
[38:50] SPEAKER_01: some of which are very bottoms up and some of which are very top down.
[38:54] SPEAKER_01: And one of the very surprising things about transformers is they're taking
[38:59] SPEAKER_01: something that is in principle an incredibly simple task,
[39:03] SPEAKER_01: which is if you have a string of text,
[39:05] SPEAKER_01: what is the next letter?
[39:07] SPEAKER_01: And somehow at massive, massive scale,
[39:11] SPEAKER_01: you can unlock something that looks an awful lot like reasoning, and you get these emergent behaviors.
[39:17] SPEAKER_01: Now,
[39:17] SPEAKER_01: the bottoms-up theory of just the linear algebra that's going on in these models
[39:22] SPEAKER_01: couldn't possibly really help us predict that we have these emergent capabilities
[39:28] SPEAKER_01: And I think similarly in healthcare,
[39:30] SPEAKER_01: there's a literal kind of set of parts that are operating in some complex way that
[39:36] SPEAKER_01: at massive scale becomes this incredibly confusing and dynamic system for how we
[39:42] SPEAKER_01: can actually incentivize how we make medicines,
[39:45] SPEAKER_01: right?
[39:45] SPEAKER_01: How we actually take care of people and how we actually pay for any of this from an
[39:49] SPEAKER_01: economic point of view,
[39:50] SPEAKER_01: right?
[39:51] SPEAKER_01: And so I think it was, in some sense, if...
[39:56] SPEAKER_01: transformers can actually be explainable by just,
[39:59] SPEAKER_01: you know,
[40:00] SPEAKER_01: linear algebra equations,
[40:01] SPEAKER_01: maybe there will be a way to decompose the seemingly incredibly confusing world of
[40:07] SPEAKER_01: healthcare in order to actually,
[40:09] SPEAKER_01: you know,
[40:09] SPEAKER_01: build a better way forward.
[40:12] SPEAKER_00: Yeah, well, that's great.
[40:15] SPEAKER_00: Now, the other thing I wanted to ask you about, you know, we open source
[40:21] SPEAKER_00: and the arms race of GPUs.
[40:25] SPEAKER_00: And this whole kind of idea is how you touched on the need for kind of coalescing a
[40:31] SPEAKER_00: lot of these tools to exploit the synergy.
[40:38] SPEAKER_00: But we have an issue because many academic labs like here at Scripps Research and
[40:44] SPEAKER_00: so many others,
[40:45] SPEAKER_00: including as I learned even at Stanford,
[40:47] SPEAKER_00: have limited access to GPUs.
[40:50] SPEAKER_00: So computing power of large language models is a problem.
[40:55] SPEAKER_00: And then the models that exist today, they can be adopted like LAMA or others.
[41:01] SPEAKER_00: You know, they're somewhat limited.
[41:04] SPEAKER_00: And then we also have a movement towards trying to make things more open source,
[41:08] SPEAKER_00: like for example,
[41:09] SPEAKER_00: recently Open CRISPR with ProFluent Bio that basically trying to use AI for CRISPR guides.
[41:17] SPEAKER_00: And so how do you deal with this arms race,
[41:21] SPEAKER_00: computing power,
[41:23] SPEAKER_00: open source,
[41:24] SPEAKER_00: proprietary models that are not easily accessible without a lot of resources?
[41:30] SPEAKER_01: So the first thing I would say is,
[41:34] SPEAKER_01: you know,
[41:34] SPEAKER_01: we are in the academic science sphere,
[41:39] SPEAKER_01: really unprepared for the level of resources that are required for doing this type
[41:43] SPEAKER_01: of cutting edge computational work.
[41:45] SPEAKER_01: There are top Stanford computer science professors or computational researchers who
[41:51] SPEAKER_01: have a single GPU in their office.
[41:55] SPEAKER_01: And that's actually what their whole lab runs off of.
[41:57] SPEAKER_01: The entire UC Berkeley campus, the grid runs on something like 12 megawatts of power.
[42:04] SPEAKER_01: And how are they going to build an on-prem GPU cluster is a central question that
[42:09] SPEAKER_01: can scale across the entire needs of
[42:13] SPEAKER_01: And these are two of the top computer science universities in the world.
[42:17] SPEAKER_01: And so I think one of our core beliefs at Arc is as science,
[42:21] SPEAKER_01: both experimentally and computationally,
[42:23] SPEAKER_01: has gotten incredibly complex,
[42:26] SPEAKER_01: not just in terms of conceptually,
[42:28] SPEAKER_01: but also just the actual infrastructure and machines and know-how that you need to
[42:32] SPEAKER_01: do things,
[42:33] SPEAKER_01: we actually need to essentially support this.
[42:36] SPEAKER_01: So we have a private GPU cloud that we use to train our models.
[42:42] SPEAKER_01: we have access to,
[42:43] SPEAKER_01: you know,
[42:44] SPEAKER_01: significantly larger clusters for large,
[42:47] SPEAKER_01: large bursts,
[42:48] SPEAKER_01: kind of train outs as necessary.
[42:51] SPEAKER_01: And I think infrastructurally for,
[42:53] SPEAKER_01: you know,
[42:54] SPEAKER_01: writing genomics experiments,
[42:55] SPEAKER_01: or doing scalable brain organoid screens,
[42:58] SPEAKER_01: right?
[42:59] SPEAKER_01: We're also building out the infrared to support that experimentally.
[43:03] SPEAKER_00: Yeah, no, I think this is one of the advantages of the new model like ARC Institute, because
[43:09] SPEAKER_00: Not many centers, you know, have that type of plasticity with access to computing power when needed.
[43:16] SPEAKER_00: So that's where a brilliant mind,
[43:20] SPEAKER_00: you and the ARC Institute together makes for a formidable recipe for future advances.
[43:27] SPEAKER_00: And of course, building on the ones you've already accomplished.
[43:31] SPEAKER_00: Well,
[43:31] SPEAKER_00: I mean,
[43:32] SPEAKER_00: I mean,
[43:33] SPEAKER_01: I would say my main skill,
[43:37] SPEAKER_01: if I have one,
[43:38] SPEAKER_01: is to,
[43:39] SPEAKER_01: you know,
[43:40] SPEAKER_01: you know,
[43:41] SPEAKER_01: you know,
[43:41] SPEAKER_01: recruit really,
[43:42] SPEAKER_01: really smart people.
[43:43] SPEAKER_01: Right.
[43:43] SPEAKER_01: And so everything that you're seeing and hearing about is,
[43:46] SPEAKER_01: you know,
[43:46] SPEAKER_01: the work of,
[43:47] SPEAKER_01: you know,
[43:47] SPEAKER_01: unbelievable colleagues who are curious,
[43:50] SPEAKER_01: passionate and,
[43:51] SPEAKER_01: you know,
[43:51] SPEAKER_01: incredible scientists.
[43:53] SPEAKER_00: Right.
[43:53] SPEAKER_00: But it also takes the the person who can judge those who are in that category set as a role model.
[44:02] SPEAKER_00: And, you know, you're certainly doing that.
[44:05] SPEAKER_00: I guess just in closing,
[44:08] SPEAKER_00: I mean,
[44:09] SPEAKER_00: it's just such a delight to get to meet you here and kind of get your thoughts on
[44:15] SPEAKER_00: what is...
[44:16] SPEAKER_00: hottest thing in life science without question,
[44:18] SPEAKER_00: which brings together the fields of AI and what's going on,
[44:24] SPEAKER_00: not just obviously in genome editing,
[44:26] SPEAKER_00: but this digital biology era that we're still in the early phases of.
[44:31] SPEAKER_00: I mean, I think you could say that it's just going to continue to accelerate the exponential curve.
[44:38] SPEAKER_00: We're still kind of on the bottom of that, I would imagine, where we're headed.
[44:43] SPEAKER_00: Any other things that you want to bring up that I haven't touched on that
[44:47] SPEAKER_00: who will round out this conversation.
[44:49] SPEAKER_01: I mean, I think it's very early days here at ARX.
[44:53] SPEAKER_01: When we founded ARX, we asked ourselves, how do we measure success?
[44:58] SPEAKER_01: We don't have customers or revenue in the way that a typical startup does.
[45:04] SPEAKER_01: And we felt sort of three things.
[45:07] SPEAKER_01: The first was research institutes live and die by their talent.
[45:12] SPEAKER_01: Can we actually hire incredible people when we make offers to people we want to come?
[45:16] SPEAKER_01: Do they come?
[45:17] SPEAKER_01: The second was when those folks do come to ARC,
[45:21] SPEAKER_01: do they feel like they're able to work on important research programs that they
[45:25] SPEAKER_01: couldn't do sort of at their prior university or company?
[45:30] SPEAKER_01: And then longer term,
[45:33] SPEAKER_01: the third thing was,
[45:34] SPEAKER_01: and there's just no shortcut around this,
[45:37] SPEAKER_01: you need to do important work.
[45:40] SPEAKER_01: And I think we've been really excited that there are early signs that we're able to
[45:46] SPEAKER_01: do all three of these things.
[45:48] SPEAKER_01: And
[45:50] SPEAKER_01: We're still,
[45:51] SPEAKER_01: again,
[45:51] SPEAKER_01: just following the same scaling laws that we're seeing in natural language and vision,
[45:56] SPEAKER_01: but for the domain of biology.
[45:57] SPEAKER_01: And so we're excited about what's ahead.
[45:59] SPEAKER_01: And I think if there are folks who are interested in learning more about ARC,
[46:04] SPEAKER_01: just shoot me an email or DM.
[46:07] SPEAKER_00: Yeah, well, you know, just say congratulations on what you've already achieved.
[46:12] SPEAKER_00: I know you're going to keep rocking it because you already have in a short time.
[46:17] SPEAKER_00: And for anybody who doesn't know about ARC Institute and your work and your team,
[46:22] SPEAKER_00: I hope this is going to be...
[46:25] SPEAKER_00: a real putting them on notice,
[46:26] SPEAKER_00: actually,
[46:27] SPEAKER_00: what can be accomplished outside of the usual NIH funded model,
[46:31] SPEAKER_00: which is kind of a risk free zone where you basically have to have your results
[46:37] SPEAKER_00: nailed down before you send in your proposal frequently.
[46:41] SPEAKER_00: And it doesn't do great things for young people.
[46:45] SPEAKER_00: Really,
[46:45] SPEAKER_00: I think you actually qualify in that demographic where it's hard for them to break
[46:51] SPEAKER_00: in for getting NIH grants and also for this type of work that you're doing.
[46:57] SPEAKER_00: So we'll look for the next bridge beyond bridge RNAs.
[47:02] SPEAKER_00: of your just fantastic effort.
[47:05] SPEAKER_00: So Patrick, thanks so much for joining us today.
[47:08] SPEAKER_00: And we'll be checking back with you and following all the great work that you'll be
[47:12] SPEAKER_00: doing in the times ahead.
[47:14] SPEAKER_01: Thanks so much, Eric.
[47:15] SPEAKER_01: It was such a pleasure to be here today.
[47:17] SPEAKER_01: Appreciate the opportunity.

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