What did Google's AI Co-Scientist "Discover"? The Human Scientists' POV, from the Podovirus podcast

We're following up on our recent episode on Google's AI Co-Scientist with a special crossover episode from the Podovirus podcast. Hosts Dr Jessica Sacher and Dr Joe Campbell speak with José Penadés and Tiago Costa, scientists at Imperial College London who made a surprising discovery that Google's AI Co-Scientist later put forward as a hypothesis entirely on its own. The episode explores the fascinating world of bacteriophages (viruses that infect bacteria) and phage-inducible chromosomal islands (PICIs) - DNA sequences that hijack virus reproduction to spread themselves as a bacterial defense mechanism. The key mystery was how capsid-forming PICIs, which only encode virus heads without tails, managed to spread across different bacteria. The surprising answer, which eluded human scientists for years but which Google's AI Co-Scientist discovered through literature analysis, is that these capsids evolved to connect with various virus tails in the environment. This episode demonstrates how AI can now contribute to frontier scientific research beyond just grunt work - providing unbiased perspectives and key insights that accelerate discovery. It's a vivid example of science fiction becoming reality in our lifetimes. SPONSORS: Oracle Cloud Infrastructure: Oracle Cloud Infrastructure (OCI) is the next-generation cloud platform that delivers better, cheaper, and faster solutions for your infrastructure, database, application development, and AI needs. Experience up to 50% savings on compute, 70% on storage, and 80% on networking with OCI's high-performance environment—try it for free with zero commitment at https://oracle.com/cognitive NetSuite by Oracle: NetSuite by Oracle is the AI-powered business management suite trusted by over 41,000 businesses, offering a unified platform for accounting, financial management, inventory, and HR. Gain total visibility and control to make quick decisions and automate everyday tasks—download the free ebook, Navigating Global Trade: Three Insights for Leaders, at https://netsuite.com/cognitive Shopify: Shopify powers millions of businesses worldwide, handling 10% of U. S. e-commerce. With hundreds of templates, AI tools for product descriptions, and seamless marketing campaign creation, it's like having a design studio and marketing team in one. Start your $1/month trial today at https://shopify.com/cognitive PRODUCED BY: https://aipodcast.ing CHAPTERS: (00:00) About the Episode (04:37) Welcome to Podovirus Podcast (04:58) Introducing the Special Guests and Topic (06:30) Exploring Mobile Genetic Elements (13:20) The Role of AI in Phage Research (16:48) Mechanisms of Gene Transfer (Part 1) (20:10) Sponsors: Oracle Cloud Infrastructure | NetSuite by Oracle (22:43) Mechanisms of Gene Transfer (Part 2) (23:36) Insights and Discoveries (28:45) Future Directions and Applications (Part 1) (32:35) Sponsors: Shopify (34:32) Future Directions and Applications (Part 2) (41:22) Unbiased Systems and Conjugation (42:35) Google's Excitement and Experimental Evidence (45:39) Benchmarking AI Systems (49:29) Manuscript Revisions and Future Plans (51:53) AI as a Collaborator in Scientific Research (54:52) Challenges and Hypotheses in Phage Biology (57:58) Future of AI in Scientific Research (01:05:52) Concluding Thoughts and Future Collaborations (01:12:07) Outro

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  Hello and welcome back to the Cognitive Revolution. Today, we're following up on our recent episode on Google's AI co-scientist with a special crossover episode from the Podovirus Podcast, in which hosts Dr. Jessica Sacher and Dr. Joe Campbell speak with Jose Panades and Thiago Costa, the scientists at Imperial College London, who recently made a surprising discovery, which Google's AI co-scientist later put forward as a hypothesis entirely on its own. For context, here's a quick crash course on the biology that you'll hear discussed in this episode. Bacteriophages are viruses that infect bacteria. In general terms, these phages reproduce by inserting their genes into a host bacteria

• 0:42Nathan Labenz?

  cell and hijacking the cell's protein-making mechanisms to produce copies of the virus itself, until the cell ultimately bursts and releases many copies of the virus into its environment. Structurally, phages consist of a tail, which is specifically adapted to attach to specific types of bacteria cells, and a head, also known as a capsid, which stores and protects the genetic material until it's injected into a target cell. Phage-inducible chromosomal islands, also known as pickies, are really a fascinating product of evolution. They are DNA sequences that have evolved to lie dormant in bacterial genomes until the cell is infected by a certain type of bacteriophage, at which point they become

• 1:26Nathan Labenz?

  active and actually hijack the virus's reproduction process, replacing the virus's normal DNA with copies of itself. The affected cell still ends up bursting, but instead of releasing copies of the virus that infected it, it releases viruses that spread the picky DNA to its sister cells, in some cases thus serving as a collective bacterial defense against the attacking virus. Now, the question that Jose and Thiago and their teams, and also independently the Google AI co-scientist, had set out to answer, was how a certain class of pickies, known as capsid-forming pickies, or CF pickies for short, which encode only the capsid or head portion of the virus with no tail to latch on to other target cells, had somehow

• 2:08Nathan Labenz?

  managed to spread widely across many different types of bacteria.

• 2:14Nathan Labenz?

  The surprising answer, which had eluded the human scientists for years, but which Google's AI co-scientist was able to surmise just from its analysis of the relevant literature, at an estimated inference cost, I should say, of maybe somewhere between $100 and $1,000, is that these capsids have evolved the ability to connect up with different kinds of virus tails in the environment. And that's how they were able to infect and ultimately become incorporated into many different kinds of bacterial cells. Beyond serving as a vivid reminder that evolution is an eternal arms race, and that we should absolutely avoid evolutionary competition with AIs at just about all costs, this episode shows that as of the Gemini 2.0 generation, large language models with proper scaffolding and a decent inference

• 2:59Nathan Labenz?

  budget can now contribute to frontier scientific research. And not just by expediting the grunt work, but in some cases by providing an unbiased perspective, or even the key insight. Obviously, such hypothesis generation can and will accelerate scientific discovery, even if its hit rate is ultimately fairly modest. And you can imagine how quickly this becomes even more powerful as Google plugs Gemini 2.5 into the co-scientist architecture, which they've surely already done by now. And then again, as AIs begin to direct experiments and collect their own data via Cloud Lab APIs. This is all science fiction, but it's happening for real in our lifetimes right now. And once again, I can only conclude that the singularity

• 3:43Nathan Labenz?

  really is quite near. As always, if you're finding value in the show, we'd appreciate it if you take a moment, share it with friends, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Your feedback is always welcome, too. Feel free to reach out anytime via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. Finally, before diving in, I should note that Eric Torenberg, in his new role at A16Z, has just announced that he's actively hiring podcast hosts across multiple domains, including biology and biotech. If you'd like to have conversations like this for a living, definitely check his Twitter for more information on the opportunity. With that, I hope you enjoy this foray into microbiology and this early glimpse of AI-powered

• 4:27Nathan Labenz?

  scientific discovery with Dr. Jessica Sacher, Dr. Joe Campbell, and Professors Jose Panadis and Thiago Costa from the Podovirus Podcast.

• 4:38Speaker 01

  Hello, welcome everyone to Podovirus Podcast. So today we have a special episode, and I might say that every time, but we're doing lots of different things all the time these days. And I heard about this exciting AI story that's also a phage picky story. So it was the obviously perfect choice of topic. And I'm so glad to have convinced Jose Pennedes and Thiago Costa, who are professors at Imperial College London. They have been working together, I'm sure we'll hear a lot more, but they have been working in the space of mobile genetic elements, phages, specifically phage-inducible chromosomal

• 5:18Speaker 01

  islands, or pickies. And they were working on this. And apparently, Google let them use its not-yet-released co-scientist tool, which is an AI tool. And they were able to use it to kind of, it sounds like, re-derive a body of work that they had not yet published, but had been working on for a couple of years, and giving the AI tool this research question and a little bit of background. But none of their unpublished data, they saw that the scientist was able to kind of come up with the same hypothesis that they had recently proven but not yet published. So this is a story that we want to dig into, and you might have seen a bunch of coverage of this lately. It's been on like Forbes, The Economist, BBC. So they made it to the mainstream, which doesn't happen all the

• 6:03Speaker 01

  time with phages. So I'm sure, yeah, you'll be able to look into that coverage too. But we wanted to, kind of talking to our scientists, our phage scientists especially, but everyone who might be curious about what are these AI tools useful for, and how did they actually start using them. So I'm very excited to have you both here. And of course, my co-host, Joe Campbell, is here too. So we'll get deep in and hopefully find out more, what are the pros and cons of using this kind of tool, and what are the limits right now? To start, tell us just a little bit about, where were you studying when you came across this tool? How did that even come into your lap?

• 6:41Speaker 02

  Okay, so for, I don't know, for more than 20, 25 years, we have been working with these pickies, that they are phage satellites. So they are using theoretically other phages for induction, for mobility.

• 6:57Speaker 02

  And it was so, yeah, for at the beginning, we have quite a few issues because people just thought that these P Ks are defective fadges. Okay, for, probably for more than ten years, we were fighting just to put the P Ks as a kind of new family of of mobile genetic elements of fate satellite. And probably, I don't know, ten years ago or twelve years ago, we discovered a peculiar P K family. There are, there were this kind of capsid forming P Ks or what we call C F P Ks. And they were a peculiar family because they have all the genes required for to produce the capsid, the small capsids, and to package the D N A into the capsids.

• 7:42Speaker 02

  But they still require tails for creative active particles. This is quite unusual because the majority of all the satellites, the other satellites just eject probably everything from the fate, okay? But this is a very peculiar thing. So we realized that these capsid forming P Ks were present exactly the same D N A. Eight, the same element in many different species. Sometimes we put as an example, one capsid forming that was in seven species from five different genera. So it was a bit surprising how these elements, we thought that this is a new mechanism of gene transfer, allowing these elements to go between species and between genera. And this is the things that we have been doing in the lab for a few years to try and to understand the mechanism.

• 8:29Speaker 01

  Exciting. And then were you also working with Tiago's lab or how does that come into play?

• 8:34Speaker 02

  So Tiago is a structural biologist, probably he will, he can introduce himself later. It's quite funny because he works in another important mechanism of gene transfer. He's an expert on conjugation. We want, we really wanted to know how these capsid forming P Ks, these small capsids that have the ability to package the P K D N A inside, how they look like in terms of a structural, you know, because theoretically, the genes involved in the production of the small capsids, the capsid forming P Ks, the capsids are related to fate proteins. But somehow they have both the ability to produce a small capsids and just to package the P K D N A inside. And so we start working with Tiago.

• 9:14Speaker 02

  Because he is the expert in getting all these kind of structures and things. And in fact, he solved the mystery, but I think it's better to introduce himself.

• 9:23Speaker 03

  Well.

• 9:23Nathan Labenz?

  Go ahead.

• 9:24Speaker 03

  Yeah, well, we saw a fraction of the mysteries. We still don't know the full story, but I think we are in the right track also. And this is also supported by the AI coscientists that we seem to agree with our experimental data and our hypotheses. Yeah, so I come from a different mechanism of gene transfer from conjugation. But I mean, my lab and Jose's lab share this, you know, this love and excitement for our bacteria and either directly transfer the DNA among themselves by conjugation or how they use the phages to mobilize the DNA. So Jose, well, asked me to, or we have teamed up to, to understand from different

• 10:09Speaker 03

  angles, the mechanism of how these capsiforming pickies are able to widespread in different bacterial species. So my role or the role of my lab was to understand at the molecular level, how those all those CF pickies are formed structurally, how these capsies are able to, you know, to take one protein, a phage protein, and change it to make this structurally very different entities. So just to do a little bit more in detail, we understood that there are some some insertions of protein sequences into the capsiforming

• 10:51Speaker 03

  proteins that dictate, we think that dictate the different symmetry of the capsiform units and make them smaller so that they can only accommodate the DNA of the CF pickies and that the the DNA of the phage species sort of makes it smaller.

• 11:08Nathan Labenz?

  Ah.

• 11:09Speaker 03

  So that they can only are able to harbor the DNA of the picky.

• 11:13Speaker 02

  Probably it's better to, we didn't say, you know, usually the satellite, these kind of pickies, they have one third of the size of the phage genome. So it's usually this peak is around ten fifteen kilo basis and the phage, the helper ones are around forty five. So a classical mechanism of phage interference is for the satellites to produce a small capsids. Where just the peak or the satellite DNA can be packaged.

• 11:37Speaker 01

  Got it.

• 11:38Speaker 02

  So yeah, this capsid forming, they all just produce a small capsids. They also have a specific terminuses to package the capsid, the peak DNA, not the phage DNA. So this is kind of very specific packaging system to, you know, or to package the capsids from Indian into small capsids that are formed by proteins in gold like the satellite. That was other unique thing compared to the satellite.

• 12:05Speaker 01

  And I think I saw in one of your papers that these are not considered parasites because they're not having a detrimental effect on the phage. Is that right?

• 12:16Speaker 02

  So for many years, we, I think it probably was in terms of selling the papers. It was nice to talk the war between the parasites, you know, and but in this case, the prototypical member of this family, we didn't see an interference with the phage reproduction. So the phage, you know, we don't know for other, for other capsid forming, if that's the case or not. But even we published, I think it was in two years ago, even some phages that might suffer for inducing the pickers. Because, for example, we published that because some of the pickers have anti-phage systems. So even you have some cost inducing the speakers, if you can move them against other phages, you can also benefit.

• 13:00Speaker 02

  So we don't see now them as a parasitic relationship, maybe so, but we might see more. We see them more as a, yeah, can be more synergistic or, you know, depending on the scenario. It's, they are more friends than we thought probably fifteen or twenty years ago. Okay.

• 13:19Speaker 01

  Okay. Okay. So, so what were you, what made you want to use this coscientist AI tool? Where were you when you figured out that was an option and why did you set it to work on this problem?

• 13:34Speaker 02

  Tiago started using it for his work? Tiago what had?

• 13:39Speaker 03

  Yeah, yeah, so, yes, so, so actually, there is a Fleming initiative at, at the college that put us in contact with the Google. And because they knew that Google was developing this new AI LLM model tailored for scientists. And they wanted to engage with the scientists so that they could understand how good the LLM model was. And so actually, initially, this was not related to Pigi. So there was a, the work started with my lab trying to understand a particular question in terms of conjugation that has been unknown for, for seventy years.

• 14:22Speaker 03

  So we were interested to understand how conjugation is actually initiated. Okay, and we wanted to understand the molecular determinants behind the T equals zero of, of conjugation. So we have challenged the algorithm and the algorithm came up with, with several hypotheses, five different hypotheses.

• 14:49Speaker 03

  And we realized that it will take us many months or to understand whether the hypotheses, the highest ranked hypotheses would be correct. Okay. So, so Jose was on that meeting and immediately saw an opportunity there. Okay, so why we don't challenge the algorithm with, with actually our experimental data that we already have in our hands and we were about to submit or I don't know if we were about to submit or already submitted to.

• 15:25Speaker 02

  To one of the Tokyo journals, okay. So, so, so, so, so basically, we turned upside down the strategy of Google instead of having AI-driven hypotheses that would be validated in a few months down, down the line. We, we knew already, in the, in the case of the P Ks, we already knew the mechanism behind the spreading of the C F Ds in different bacterial species. And we have challenged the algorithm with that exact same question. And it was astonishing. I mean, the one of the top or the top hypotheses was basically a recapitulation of what we had observed in the lab.

• 16:08Speaker 01

  Wow. So were you surprised? Yeah, go ahead, Jose.

• 16:11Speaker 02

  He didn't say, but the hypothesis that the scientists suggested for the conjugation, I'm not an expert there, so I'm just, he was really impressed. So that's why I said, okay, this looks really good. I need to take some advantage here because if the hypothesis provided by the coscientists for conjugation were, probably I would have been interested. But he was really impressed, he says, okay, this is looking very good. So we did this kind of, we changed the approach and it was quite impressive. Probably the, the audience probably would like to know now that, so the way this capsid forming can be mobilized between different species is very simple. Okay.

• 16:55Speaker 02

  They can produce this capsids with the P K D N A inside. Okay. And that was a mistake we did for many years. We always thought that whatever is released after a phage infection or whatever, it's an infective particle. And we realized in this paper is that these P Ks can be induced or even without induction, as soon as the cells lies, what this release is just a capsid P K with the package D N A.

• 17:26Speaker 02

  And outside the cell, they have the ability to take tails from different phases. Free tails. Huh. You mean free tails, okay? And then depending of the tail, that will determine, because we know for many years that the tails in the phase biology determines the tropism, okay? So that's it. And so there were many things on the paper, okay? Until we realized that. Yeah. It's induction and induction, the cells lies, okay? The capsid, the packaged DNA will be released, no tails, and then outside will be free tails. We knew for many years as well that there were many tails that producing excess. Okay, and even all during the purification, they already showed that there were more tails that infected particles or whatever.

• 18:11Speaker 02

  And then it's for the same capsid depending of the tail that will bind there, that will determine the tropism, okay? So and then the, one of the hypotheses that Google says is like, you need to check the possibility that this capsid can interact with different tails from different phases, okay? The system didn't talk about how that happened or that was induction, so it was just the final picture, okay? So many things that was in the middle. It's not so many new concepts that we put in the paper are not on the Google document. But the final thing that was right. Okay? And it was something also quite impressive is that

• 18:54Speaker 02

  we knew for the genetics and now to avoid solving the structure, hopefully will be soon. That there were two key players in this. The ability of a capsid forming of binding to one tail or another is two proteins called the adapter and the connector. So if you use what the adapters and the connectors was what the ability of a capsid forming to bind to some tails or others, okay? All scientists also suggested we should look on the adapter and the connector.

• 19:22Speaker 01

  Wow.

• 19:23Speaker 02

  Okay, so it was so many things that were not, you know, in the output from the coscientist. But the key one, the tails, that was there. Okay?

• 19:34Speaker 01

  Wow.

• 19:35Speaker 02

  And it was another one. So we put this, you know, that was quite interesting. Is they suggested, the system also suggested to, to check conjugation. That was quite funny, you know, as a new mechanism, because conjugation is more promiscuous than transduction theoretically because of the tail, the narrow for a specific tail. Okay? We have been, we have started working on that as well to see if that's true or not. But that was quite interesting hypothesis as well. Okay, that this system provides.

• 20:06Speaker 04

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 20:11Speaker 01

  Tiago must have been excited that it chose your favorite area.

• 20:16Speaker 03

  Yeah, well, it, it was back to the beginning, right? That was the first, we have tried was to understand a bit better the.

• 20:26Speaker 02

  As a biologist, Tiago loves the adapter and the connector.

• 20:30Speaker 01

  Ah.

• 20:31Speaker 02

  Yeah. At dinner or lunch, we, you just need to mention the adapter. Because it's pretty cool that some adapters and connectors bind to one specific tail. You know, look to be very narrow and others have the ability to bind several trails. Okay, from different species. And he's all the time, he's very excited to see how mechanistically probably one or two residues. You know, he's, that's the word to need to mention to Tiago, connector and adapter, that's it.

• 21:01Speaker 01

  I love it.

• 21:02Speaker 05

  To get a little better understanding of the process. So I assume because you hadn't done this when you started the experiments. Were the hypothesis that were generated by the computer? Things that you had thought about and therefore you designed experiments to test them? Or did you have ideas? I mean, how were the experiments designed by you that ended up leading to the conclusions that agreed with the AI-generated ideas?

• 21:40Speaker 02

  I agree with us because the timing is very important here. Yeah.

• 21:44Speaker 05

  Yeah.

• 21:45Speaker 02

  So.

• 21:47Speaker 02

  We have been thinking a lot, okay? How this machine?

• 21:53Speaker 02

  Got the right answer, okay?

• 21:54Speaker 05

  Right.

• 21:55Speaker 02

  And it's very frustrating, it's very frustrating. Because we have the answer there and we didn't see it, okay? As a face biologist, we know that the tail. Determines the tropism, is that right? Yeah, we know that. Many years, okay? For ye- years. So. In one DNA, using a tail, is in many different species? It's because maybe it's used different tails. You know? But we were biased. That's the problem we had. And I'm killing myself for that because for many years. I always thought, and all face biology people will think. That after infection, what you have are infected particles.

• 22:39Speaker 02

  Yeah. Capsid and the tail. And we knew it's very things, very strange, we have pickings that can be induced. But we didn't get transferred, we couldn't understand this thing because we were wrong in the way we saw this thing. We were so biased, okay? We always thought that after induction or whatever it's released from a bacterium. Are infected particles. Both for faces. And both for satellites. Okay? And I think that's the big thing, so. Google didn't understand what's happening here. But make the most connection. You are in many different species and to go to this species, you need a tail? You are binding to different tails, that's it.

• 23:22Speaker 05

  So, look, if I could just. Go back to the order. Am I hearing you say? That you have? Data? And you weren't exactly sure how to interpret it and then you realized? That this was telling you how to interpret it.

• 23:41Speaker 02

  No, we had the data for years.

• 23:44Speaker 05

  Yeah.

• 23:44Speaker 02

  Okay, so for example, we have the picking, we use the capsid for me from Klebsila. Okay. And we use the picking, so this is a profit there that is a helper profit. Until the paper will be published for us, a helper profit means a face that will provide whatever is required to produce an effective particle. Capsid and tails or just tails, okay? So we have a strain in Klebsila one we use, we we show very nice capsid forming replication, very nice packaging, no transfer. So in our mind was because the recipients we use didn't have the receptor. But we were in all the time that there were infected particles.

• 24:28Speaker 02

  Okay, and the same happened to us in Icola. And then one day suddenly talking to my student, we realized that in Icola there were several profages. Not, but when we induced them, we knew that the island was heavily induced, there are a lot of replication. And a lot of packaging, because we can purify the capsids from the lyssate, but the transfer was very low.

• 24:54Speaker 02

  And then he was making mutants to delete, it was, I think, six profages, one by one to see what's the inducer, whatever. And then he realized that it was one day that he removed one.

• 25:05Speaker 02

  Eliminate the transfer. You know, even the low transfer, but the island was still induced. And at some point, we thought that maybe we'll we'll be fadist to induce and fadist to provide the tails. Okay, and then we start connecting the dots. And it has been now for fifty years or sixty years that you can get lambda or eighty lyssate, mutinine capsid, mutinine tails. You induce them, you take the lyssate, you mix, and then you have infected particles. But if you did this experiment, they never thought taking capsid from one fate and tails from another, never.

• 25:45Speaker 02

  Because we all thought that everything, you know, that that's the big bias, okay? So then that day, everything started making sense.

• 25:54Speaker 03

  So if you think that I was excited when we understood that adapter and connector are the key factors here, you should have seen Jose on that page when he cracked this hard net. It was very excited because he knew that this was something big, something novel.

• 26:14Speaker 01

  And you did this before the AI came into play. You cracked this, but you were spending a couple of years trying to get there.

• 26:21Speaker 03

  More than a couple.

• 26:23Speaker 01

  Okay.

• 26:25Speaker 01

  More than a couple.

• 26:27Speaker 03

  Yes.

• 26:28Speaker 01

  Many years, a scary number. And so then the fact that the AI came up with that because it did not have this bias that once you release a particle that of course it has its tail, it has whatever it needs to be infectious. It was unbiased in that thinking. And of course, why not? Could you have this later meeting of caps and tail outside the cell? And that's sort of what happened.

• 26:50Speaker 03

  So, yes, sorry, Jose, I think we need to also to clarify a bit the chronology here. Because there was other angle that was in play here, which obviously we saw opportunity to patent that these new elements, because these elements are a broad range, okay, which is one of the limitations on phase therapy. You have always narrow range. These phages using the phase therapy, they either only hit about your species or sometimes strains within that that species. And with this chemical particles, we met you, you tailor made and

• 27:31Speaker 03

  open up the, you know, you have a toolkit where you can customize the target bacteria that you want to hit by for therapy or to make diagnostic tools. So we kept this very secretive because unless we could only speak about this in public and once the patent was filed.

• 27:54Speaker 03

  So, yeah, so there was new public domain. So that's why we are very reassured that AI system would never had access to this manuscript or these ideas because they were kept in a safe spot in our computers.

• 28:10Speaker 02

  Also, we challenge other systems as well with the same input. Okay, and no, none of the other systems, you know, even related to the scientists, you know. And even, so I think the important thing, we didn't mention this thing in the preprint. When we got the first round with the five hypothesis. We asked because, as I said, it's not really a real mechanism in terms of the capes will be released outside the cells and they will, you know. Was just, okay, check if this can bind to different cells without telling you how this interaction will happen or when will happen or whatever. Okay. Yeah. Like make a second round. And I said, yeah, but this, you know, challenging the system saying, yeah, but this is doesn't

• 28:55Speaker 02

  make any sense because how these particles are produced well will be whatever. And the reply was crap. You know what I mean? So the system don't really understand what this proposition. Yeah. It's just connecting the dots with make sense, you know, just taking this simple thing that it details that are mind the tropism. Maybe it's because it's using different cells. That's it. And it was there, we all knew that. Okay. So this is the type of things that for the people, when they read the paper, they will think every single person will understand very quickly because it's obvious. And you know, but somebody needs to tell you, you know what I mean? So this is, so it was quite frustrating because it was quite, you know, few years trying to understand.

• 29:40Speaker 02

  We have a strains and a lot of mutants and we couldn't understand why we didn't get transferred is because just this tailless, we said this is a new entity. We call the tailless capsid forming, you know, the package DNA. These are the entities that are released. Okay.

• 29:58Speaker 04

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 30:04Speaker 01

  Can phages, regular lytic phages have this sort of mix and match of tail and capsid? Or have you started to look?

• 30:12Speaker 02

  That.

• 30:14Speaker 01

  That's so cool.

• 30:16Speaker 02

  If I did a bit, I would say yes.

• 30:20Speaker 01

  Yeah. Do they all have this?

• 30:23Speaker 02

  You know?

• 30:23Speaker 01

  Yeah.

• 30:25Speaker 01

  They have these adapter connectors, like do many phages have those? Is that conserved?

• 30:31Speaker 02

  Yeah.

• 30:31Speaker 01

  Interesting.

• 30:32Speaker 02

  You have asked.

• 30:33Speaker 01

  Okay.

• 30:33Speaker 02

  Sorry, I think the biggest difference is that when I have phage effects, the capsids and the compatible tail are already there. So we think that the number of capsids that will be released probably is lower. Okay. But structurally, the capsid forming, the proteins involved are exactly the same that you have in the typical H97, you know, and this is the things that Tiago. But also, we think that even other myobiasis or whatever, they work in kind of similar thing. The funny thing that we are working now, and you know, probably the people that it's good that we will say them publicly, because then if somebody copy us, can always get the credit.

• 31:13Speaker 02

  So you know now that there are many anti-tail, for example, anti-phage systems that block the tail formation. So when that happens, you will have capsids.

• 31:24Speaker 01

  Ah, it's really coming out.

• 31:29Speaker 02

  So we really think the same will happen with, okay? And the phages will have the ability to inject DNA in different species by its warp in tails. If these phages in the newest species survive or not, this is another question. Okay, we also show that the capsid forming, when they go to another species, they have mechanism to hijack other phages that are in the newest species to also be mobilized and to be transferred. But mechanistically, we think that will be the case. Okay?

• 31:58Speaker 01

  Yeah. So tropism is you're like narrowing, kind of getting more high resolution on what it's actually defined by, like not necessarily just the tail, but specifically this connector point maybe.

• 32:12Speaker 01

  A little bit.

• 32:12Speaker 03

  Yeah, I mean, the true, true, it will be always determined by the, the depth of the tail, right? Whether it binds the surface or the receptor in the target cell. But it's true that now the neck region where the portal, the adapter and the connectors are, will have a same determining that trophy is not by the direct binding to the surface or the receptor by, but by the generating a permissuous or a very specific structure that binds only one or several phage stands.

• 32:54Speaker 03

  So that, and that's what we are looking after while we sort of start to understand now, what is imprinted in those structures of these proteins that determine that specificity or that permissivity in to bind only one type of tails or a set of tails. It's, that this is for the next story, this is the next story.

• 33:20Speaker 02

  Yeah.

• 33:21Speaker 01

  Yeah.

• 33:21Speaker 02

  If this capsid forming, so we proposed in the paper that we published in 2023, that became the genes came from the HK97 phage, let's say, you know? But they have evolved, so this is not cross talk between the two proteins, even they are very similar in terms of sequence. Okay, so you have the capsid, the portal, the terminases, you know, the protease are very specific. Okay, so everything is very specific until they arrive to a point that is the connector and adapter, that each connector and adapter needs to bind to the capsid, the sheer peaky or the phage peaky. And then they need to bind to the same tail. So it will be a quite funny thing.

• 34:04Speaker 02

  You evolve something to be completely separate in terms of capsid forming things and you know, the proteins involved, that you cannot cross talk, but you cannot, you know? But then the tail is the same and probably will be a competition between them as well. So it's quite interesting to know what, what's happening there, you know? Because the idea we have is that probably we can create synthetic peakies with the ability to bind to multiple tails. And if that's possible, we will limit, you know, we will solve one of the limitations for phage that appear, you know.

• 34:36Speaker 01

  Yeah.

• 34:37Speaker 02

  We can create, we can deliver DNA to multiple strains or multiple species, or you know, this is the pattern that Tiago was mentioning before.

• 34:46Speaker 01

  Yeah. Yeah, I think this is so cool to just, it feels like one of the first fresh sort of phage host range discoveries that I can remember for a while, like it's an actual, another level of looking at it as a whole and, and pairing it with the AI aspect, it feels like we have this potential tool, set of tools that we're all gonna, it's gonna be more, not less, like we're all gonna probably start using these tools. And now we can have someone, almost like someone in a beginner's mind, like who doesn't have all the assumptions of our hundred years of history. And then now we can get their input into just physically what would be probable or

• 35:31Speaker 01

  possible. And now we can kind of bounce off that whole other set of knowledge, different, differently built than ours.

• 35:40Speaker 02

  I agree. For me, something that is kind of revenge, you know, no nice for me. So I, I, I normally I don't say these things because people said that this is not the right things to say, but I think like you said, some of the things we found in the app was because I don't read, I don't read too much.

• 36:00Speaker 01

  Yeah.

• 36:01Speaker 02

  Okay, so I'm not biased.

• 36:03Speaker 01

  Okay. I love that. Yep.

• 36:04Speaker 02

  Yeah, yeah, to some extent.

• 36:06Speaker 01

  You can't admit it. It's hard to admit.

• 36:08Speaker 02

  I admit, you know, and so many times, you know, I have a very good idea and go, I say, wow, this is looks so good. And then I go to maybe say, yeah, somebody in 1985.

• 36:17Speaker 01

  Yeah.

• 36:18Speaker 02

  Thought the same. And then you have a huge admiration for these people, you know, that.

• 36:22Speaker 01

  Yeah.

• 36:23Speaker 02

  But I wasn't biased, you know, that happened, for example, with the lateral and this kind of things, you know, because I was.

• 36:28Speaker 01

  Interesting.

• 36:29Speaker 02

  A completely ignorant, you know, I always said to my students, I trust your results because the students sometimes said, also, but you know, I don't mind what people said. You have the controls, it works well, let's interpret your results, okay? And now I was so biased. I knew too much about the satellites. And I had this thing there, you know, with the tails, you know, it was obvious, you know what I mean? So I think that's the good advantage for this kind of systems, that this not bias of the system. It doesn't matter if this is a fate, you know, it's a conjugation, you know, we always thought that the satellite should move by a fate. Because that's the name. We never thought, you know, but

• 37:11Speaker 02

  why not by conjugation? You know, it's just to put a very small sequence. So it was a good revenge, you know, in that way, you know, that the system, you know, gave me in that way because I knew too much and I was so biased. We were so biased when we talked about this thing, you know, and then, yeah, we realized. And another funny thing is that I have no idea about tails, okay? When, when, yeah, zero. It's something that I never, ever thought about that the tails will be something interesting at all. That was a tail, its form, you know, the funny things with the packaging, the capsule, you know, this is the big papers from the capsule, this can ninety seven, you know, the determinant, you know? And now suddenly it looks like the tail.

• 37:55Speaker 02

  It's a funny thing as well, you know, it's because as you said, you can have now a very simple mechanism to move things between species, you know?

• 38:05Speaker 01

  Wow. Yeah, so was Google kind of excited that you guys showed the world a nice test case? Like, it's pretty neat how well it worked. Is that, is it really that clean cut? They must have been very excited.

• 38:21Speaker 03

  Yeah, well, that's a question for them, obviously, but I think they were very pleased

• 38:28Speaker 03

  to have experimental evidences that that hypotheses that the system was generating were sound and solid and verifiable in the, in the lab. I mean, there, there was other studies that, that they, they were initiated by hypotheses from the co-scientists that are also reported in the, in their system pre-print. But they are, they still have to go through the peer review and then, you know, publication at the end. In our case, we were a little bit. Way more advanced because we had already a full story. We knew the answer and the system was able to recalculate.

• 39:09Speaker 02

  We we tried to obtain some stock options from them, but you know, so it's like as a joke.

• 39:14Speaker 01

  What was that?

• 39:16Speaker 02

  Some stock options from Google.

• 39:18Speaker 01

  Oh, yeah, I was gonna say they should be funded for lab.

• 39:21Speaker 02

  They didn't. They didn't agree with us. Okay, because I think that they they were lucky and we were lucky as well, you know, because I think this is the perfect system to test because as I said, everything was there. Okay, it's the capsid, you can produce the tails, whatever people has done experiments in the past, mixing things, you know, tail mutants with capsid mutants, and then you have an effective particular. So all was there, just putting the dots with

• 39:45Speaker 01

  Yeah.

• 39:45Speaker 02

  a bit bias. So I think was lucky for them and lucky for us, you know?

• 39:49Speaker 03

  Yeah, well, that definitely a very nice partnership between the

• 39:53Speaker 02

  Yeah.

• 39:53Speaker 03

  Well, what's that? Oh, well, there was no funding involved, so they didn't invest a penny.

• 39:59Speaker 02

  Because

• 40:00Speaker 03

  So we need to effectively

• 40:02Speaker 02

  We are getting money from Google. We don't. We are trying, but so far.

• 40:09Speaker 03

  So so so there was no, yeah, so so it was mutually benefit for both sides.

• 40:16Speaker 02

  I don't know if you check their print because we didn't put, sorry, Joe, we didn't put in the print, but even the system for each of the hypothesis provides key papers that the system use to arrive to that this is, you think it would be nice, for example, to to include that information as well for the people to because they were mentioning names, as you know?

• 40:38Speaker 01

  Yeah.

• 40:38Speaker 02

  And for each name, they they highlight one paper that they thought was relevant for the hypothesis, and that was pretty cool as well, you know, so it's like for each this is the run. They were like, I think, is a few papers.

• 40:52Speaker 01

  Yeah.

• 40:52Speaker 02

  It was for the tail things, for the hypothesis we were mentioning, I think to there were fifteen papers that they say, okay, this is the the papers they found relevant to arrive to that hypothesis.

• 41:05Speaker 01

  Okay. They were real papers.

• 41:07Speaker 02

  Yeah, real papers, a lot of sense, you know?

• 41:10Speaker 01

  Yeah.

• 41:10Speaker 05

  Can you tell if the other AI tools that you use that weren't as effective, did they see those papers and not realize the importance of them, or did they just somehow miss them or?

• 41:28Speaker 05

  I guess in trying to understand what's different and why they're giving different answers, I guess I could broadly think of two categories. One is that somehow they didn't find the right papers, or they found them and they didn't realize weren't able to understand the importance. So can you go back and figure out if those other search engines even or whatever learning tools or AI tools even looked at those papers?

• 42:00Speaker 03

  So, so the, so part of the preprint, we had to benchmark the system, the KAI system with other systems, okay? So, GEM and I, ChatGPT. Yeah, and the output from those systems are different, okay? So the format is not the same. However, the hypothesis and, and the answers are there, but some they output, well, the coscientist outputs the references, he outputs notable scientists that have worked on that, on that field. So this is a very comprehensive report that, that you obtain. However, other systems, they do not give such a, such a comprehensive output

• 42:41Speaker 03

  because they were built differently. Coscient is very, I mean, we are not AI people and it's for still coscient, at least for me, it's still a black box, but the system is built differently, okay? And the, it's funny enough. We ask, well, we, we have published the preprint of the experimental paper in BioArchives. And we, and then we challenged the system, the different LLMs. And some, they haven't seen the preprint. They, they could not even find already published our published data where the

• 43:19Speaker 02

  It was, it was written.

• 43:22Speaker 03

  I think Google.

• 43:23Speaker 02

  No, I think what the system found the preprint but didn't provide the right answer. Or, okay, so it is the same, you need to provide the right answer, even though we found that. But they found the preprint, I think was one case. And in other systems, they also propose things that make sense. So, for example, some of the system proposed that maybe dyscapia forming can take tails that have the ability to inject DNA in different species, the same tail. Okay, but for example, in that case, that will work for both for dyscapia forming or for any satellite. You know, so it's not something that is specific because at the end the tails are very similar.

• 44:04Speaker 02

  So some of the hypotheses were kind of okay, but none of the other systems provide the right thing. You know, it's whopping different tails and, you know. And even some of the systems, I can't remember which one was the last one we put in the paper. They made a very nice overview of the tickies cycle, you know, so it was definitely had very good access to the bibliography, but I don't know to what extent, as I said before, that's a problem. Because this is the problem we have, you have a very good knowledge, then how you interpret things that, you know, it's something new. And that's the question that some people asked to us, you know, to what extent these are just new hypotheses or just

• 44:48Speaker 02

  linking, you know, connecting the dots. And I think so far it's more connecting the dots in an unbiased way, way more than thinking something that is completely novel. Okay.

• 44:59Speaker 05

  Yeah, if I could ask just a couple more questions. First is, are you in any way planning to rewrite

• 45:09Speaker 05

  your manuscript

• 45:12Speaker 05

  that you hadn't shown it, based on what it did? So are you, I guess, my guess would be it would most likely affect the discussion. But are you planning to rewrite it? And well, I'll ask what you add to that before I ask the follow on.

• 45:30Speaker 02

  What? So for the experimental manuscript, nothing changed? Okay, so basically what the output from Google, we already was addressed in the experimental manuscript. Even in the experimental manuscript, we provide new concepts that are not in the coscientists never mentioned. What we are doing now is like, because we thought it would be nice for the, to share our experience. We are creating a new manuscript that is our experience with the coscientists.

• 46:01Speaker 05

  Right.

• 46:01Speaker 02

  Learning what's the input we provide, okay? And then evaluating what's the outputs that we got, okay? Yeah. And explaining, you know, for example, the tails, you know, this is a good thing. But this is things where, you know, explaining this kind of frustration or why we think the coscientists was able to arrive to that point, you know, even this non-bias, even did they provide the picture? And we also make kind of evaluation from the rest of the hypothesis. We pre-printed that manuscript as well. And we sent, we have sent the manuscript to the journal as well, to the same journal that the experimental one. Because they might be interested in having both. Yeah. The experimental one and the, and this. Love it.

• 46:45Speaker 02

  Coscientists related what?

• 46:48Speaker 05

  I guess the other thing I was thinking about, and maybe this is a hard one, is if you had done this, had the, in the output of this L L M system before you started the experiments.

• 47:08Speaker 05

  I guess the hard question, what would you have done differently? If anything, would you have done different experiments or? No, okay.

• 47:17Speaker 02

  Not, nothing, nothing changes. We, nothing changes. It's just good faith. Yes. So, so the way that I see the system is, it's like a collaborator, okay? It's something that you can interact with. You obtain hypotheses in the end, but they are not the final truth. You, they all, you have to go to the lab.

• 47:41Speaker 03

  Run the experiments, interpret the data and draw conclusions. The hypotheses that the system generates are not universal truths. You and the scientific method does not change at all.

• 47:54Speaker 05

  Right.

• 47:54Speaker 03

  What it does, it puts you perhaps in the right path, right from the beginning. So if you.

• 48:01Speaker 05

  Right, I understand that the hypothesis doesn't affect that you need to do an experiment to address it. But I guess what I'm asking is, I mean, back when I used to do experiments, you know, often if you have a hypothesis, you design an experiment to test it. And were there things?

• 48:27Speaker 05

  So when you said it was quicker, is that because you would have designed the right experiment?

• 48:32Speaker 03

  You maybe, yeah, it would be because you have not, maybe you would not fail ninety percent of the experiments that you have failed. But you just failed fifty percent and this would save you half of a year or one year of experimental work and one year.

• 48:54Speaker 05

  So it would have changed things in terms of it would have changed which experiments you didn't, didn't do, right?

• 49:01Speaker 03

  Exactly.

• 49:01Speaker 02

  It would change an example, you know? Coming back to the idea that we knew that we have some strains that we can induce, the island it's been, you know, it's been induced, it's highly packaged. You know, and we tried to get transfer and we didn't get any transfer because there were just caps, no tails there, you know? So can you imagine how many recipients strains did we try? Because we thought it was affecting the recipient strain. Okay, capsule with capsule with these, these, you know, can you imagine how many? And we didn't get an answer for that. And then we moved to equalize the same thing, you know? And then calculating these things, relating, you know, because we never thought that these were tailless.

• 49:41Speaker 01

  Okay.

• 49:41Speaker 02

  Oh, we never thought that, yes, if they have everything to package the DNA, it just need a cell. Maybe they can bind to different, you know, so it's like, at the end, we arrived to the right experiments, you know, but we fail a lot of the experiments because we didn't understand what was happening, you know?

• 49:59Speaker 05

  Right.

• 50:01Speaker 02

  In, yeah. If they give you some kind of path and the others, there are other hypotheses that you realize that might be irrelevant for the biology of the satellites, but they were not really important for the question, you know, because can apply to many other satellites, so there was no exclusive of this family. But the type of conjuvition is the same. They provide an hypothesis that is very easy to test. Something that we highlight in this second print is that at least in our example, all the five hypotheses are very easy to test.

• 50:33Speaker 01

  Yeah.

• 50:34Speaker 02

  You know, I, it was, and also as Tiago said, we had the feeling that we were talking to an expert in the field. Even, you know, says, what do you think about this? I think you should take this thing.

• 50:46Speaker 05

  Right.

• 50:47Speaker 02

  Okay, that makes sense, you know, and definitely, you know, at the end, we were very happy because we arrived to this, to the conclusion, you know, the same one. But for the other examples, for example, the, what the one that Tiago was saying, it's a mystery for seventy years. And nobody thought about it. Okay. Can you imagine how many experiments have people did in the past, trying to understand the time zero for conjuvition? This is a big question. I can't believe that these guys working on conjuvition don't really know that. Okay? So the system provides you an hypothesis that looks okay. It's saving a lot of time.

• 51:27Speaker 05

  Right. Yeah, I wonder if in your paper about this method, you could.

• 51:36Speaker 05

  Sort of give concrete examples of of why you would get move ninety percent of the experiments not working to maybe only fifty percent of the experiments not working.

• 51:47Speaker 03

  Power.

• 51:48Speaker 05

  And sort of say, we would have done, if we had known this before, we wouldn't have gone down this rapid hole and we wouldn't have gone down this rapid hole and. Because I mean, I guess maybe I'm trying to think about if someone's out there tried to say, well, why am I going to do this? I mean, I think a lot of people would be attracted to know which experiments that you did, which ended up being dead ends. You think you might have just not have done if you had run this AI prior to starting the experiment or does that make any sense?

• 52:26Speaker 03

  Yeah, it makes sense. Yeah, it makes sense, but this, yeah, I mean, you tend to for to forget the failed experiments and keep track of the good ones, right?

• 52:35Speaker 05

  Yeah, yeah.

• 52:37Speaker 03

  I don't know you. And that's why we arguably you don't you don't publish a negative results or maybe you should, but anyway, that's another question. But yeah, it's, I think it's now it's it's very difficult to to unders to say which experiments.

• 52:56Speaker 05

  Yeah.

• 52:56Speaker 03

  Have been done or should have not been done or could have been done differently.

• 53:01Speaker 05

  Yeah.

• 53:01Speaker 03

  You know. Life. I.

• 53:03Speaker 05

  Yeah. It's hard, but I mean, I guess it's a question of.

• 53:06Speaker 03

  But that.

• 53:06Speaker 05

  I like you.

• 53:07Speaker 03

  But if you're these are.

• 53:09Speaker 05

  With the AI.

• 53:10Speaker 03

  Sure. This won't be an issue because if it will be an AI driven hypothesis, you have never run those experiments before. Okay, so in this case, you because we knew the answer, we had experimental, we had the portfolio of experiments of fail and successful. We could do that that that judgment, but in the future, if it's an AI driven hypothesis. All the experiments you will, I think that's will what what will make.

• 53:42Speaker 03

  A good or a quick or quicker discovery is how good the human will be to critically interpret the hypotheses and design and experimental setup. Although the AI system already gives you some experimental experiments that you should, that you could do to test that hypothesis. And that will, the human will, will have definitely a very important role still in this process.

• 54:12Speaker 02

  So I, I think I can answer your question. Okay. So we knew this capsid forming, I don't know for how many years. I was almost ready to publish this capsid forming pickings in two thousand and ten. Okay. And Richard Novick told me, Jose, you can't. Richard Novick was, is the person that discovered the pickings in a staff that he calls Shapis. This is the first member of the, let's say he's the father of the picky. He discovered the first element. Okay. And as I said, for many years, he discovered, I think it was nineteen ninety eight. And for more than ten years, twelve years, people thought were defective fates. So when we discovered this capsid forming, that was probably ten, two thousand and ten.

• 54:57Speaker 02

  And he said to me, Jose, you can't publish this thing because then people will think that these are defective fates. They have everything, they have the capsid, the whatever. So you, you can't. Okay. So we wait. It has been for many years that we knew they exist. They kind of manual, you know, findings. We knew that they were in many different species. We couldn't understand why a satellite want to use half of the genome to, to carry genes that they can kick jack from the fates. So it was kind of things. We did a lot of experiments that we not transferred or whatever. And the day talking to the student that we realized that maybe some satellites use a fate for induction and another fate for the tail. Everything makes sense. Okay.

• 55:42Speaker 02

  And I think it's when Tiago was mentioning before I say Tiago, I think we have something pretty big. And since then, everything was very quick.

• 55:52Speaker 02

  Very quick, because these experiments are very simple. If you have a capsid mutant, you can have details, you can mix. So these things even work in natural populations. So a normal fate, profate, when it's induced, there is a lot of tails there. Maybe you just mix with the capsid form, you have infecting particles, everything. We have an experiment in the paper, we have a donor for the capsid form, a donor for the profate, a functional one, and a recipient, and things move, you know, so. But that was the day that we realized that maybe this is something for induction and something to provide details. And maybe we realized that, you know, everything makes sense. So you have something from Google that said, maybe this can use different tools.

• 56:36Speaker 02

  You're thinking, you know, because the experiments are very easy to do. Always, you know, case, I don't know, in other areas. So I think it's like because of the bias, and I said, you know, it was awful, because we didn't see the big picture, you know. But having something that is new, it's like the same thing conjugation. We never thought, I never thought about conjugation, okay? Tiago never thought about conjugation, he's an expert on conjugation. There is a guy in Pastoral Institute, Eduardo Rocha. He's an expert on satellites and conjugation. He has made a lot of programs and studies about O R Ts that are required for. And we sent him an email saying, Eduardo, have you ever thought about this? And he said, it was not, never.

• 57:19Speaker 02

  Because satellites use pages, plasmids use conjugation. You know, this, you know, so we are so biased that things that are there, you know, we didn't realize. So I think that's the main, I think the power of this system at least for us, you know.

• 57:38Speaker 02

  It's not biased, whatever has been published, they will, you know, highlight that, have you, you know? And then it's up to, as Tiago said, it's up to you to decide if that makes sense or not. And, you know, for the hypothesis they provide, were very easy to test, all of them.

• 57:53Speaker 01

  I want to touch on that last point. I think the testability is really interesting. And I wonder if behind the scenes, the AI co-scientist that Google made, maybe they gave it a lot of training related to knowing what science is like when it, so it could understand like what would be a testable hypothesis versus not, because I don't think that's usually, that's probably not, you know, what you're getting when you talk to regular ChatGPT or other LLMs or even regular scientists. Like, I think that's like a advanced skill to think, you know, of all the things you could do, what are the things that are the most bang for your buck and the most testable, least time in the lab? Like, you have to have a lot of background to know that. But even filtering on that, like, lens

• 58:38Speaker 01

  and giving you the hypotheses and giving you testable ones, like, it's pretty interesting that you said they're also testable. And I wonder if that's built into the system. And if so, that seems like a really nice feature. Is that your view of it?

• 58:53Speaker 03

  I think just as I, we don't, I don't know. I think this is a question for the Google team. The way that, I mean, we do know the basic principles. We know that the system generates different hypotheses. They are ranked, and then they compete, and they challenge each other, and they are ranked with a halo ranking, like the chess players. We know that is a very complex system, because I've, I've heard experts on AI talking about the co-scientist, and they seem to be very surprised on the architecture of the system. And not, and in particular, these novel hypotheses that haven't, that, that, because they were just, was mentioned

• 59:36Speaker 03

  about the ORGs and that this CFPs could also go be mobilized by a conjugation, this, what they call it indirect assumptions or hypotheses, and that they, they are not imprinted direct, or they cannot readily interpret by the data that is available. Okay. So whether there is an element of reasoning in the system that would come up with this less evident hypotheses.

• 1:00:09Speaker 03

  Yeah, maybe, but this is again, it's something for the Google people.

• 1:00:14Speaker 02

  This system sometimes provides a specific experiment, you know, for the, for example, for the connector and the adapter, they said you should check like I O M how these things look. For some obstacles as well, the system suggests to use liposomes. So it was quite funny thing, even some of the hypotheses I found, even they were incorrect. They were quite funny because we included them in the paper as a negative. So for example, one of the hypotheses was that just the capsids forming the with proteins there that might have the ability to bind to some specific receptors without the tail. Okay, and we, you know, so it's like a different mechanism of entry. You say, well, this is a, and we include these controls because we have experiments with no tails to show that the tail was absolutely required. Okay.

• 1:00:57Speaker 01

  Okay, so you ruled it out.

• 1:00:59Speaker 02

  Yeah, it was some kind of suggesting experiments, you know, in a very easy way. Okay. But I, again, I had the feeling here that the face wall is a very easy, it's a very good model for the system because it's just, at least the world we do in the satellite is mutant complementation. This is some cryo M, yeah, we don't really know in other areas, you know?

• 1:01:23Speaker 01

  Yeah. Wow. Well, yeah, I think we should probably let you go on with your evening and close out. But this has been so, so cool. I guess my last thing I wanted to end on was just when can others use this? Or are you still going to be using it as a still kind of in a testers only phase? Or what's that like going forward?

• 1:01:46Speaker 03

  So the system is, has been just in under development, right? The system is not publicly available still. This is again, that's the grounds of, of Google. But the discussions that we have had with them is that there is a process for the maturation of the system till it gets publicly a release. Okay. I think the strategy would be to, to incorporate different areas of science to the system and understand how robust the algorithm is. And eventually if, if it pans out as, as well as. At least with fate biology, it will eventually be released to the to the public. I think now it's still, it's written in the Google's co-scientist tree print is that there is a test

• 1:02:31Speaker 03

  trust tested program and the labs interested in testing the system can can contact Google. Yeah, we will definitely will keep working with them because we have a very close collaboration and partnership with them.

• 1:02:44Speaker 01

  Yeah.

• 1:02:44Speaker 03

  And I believe it will be not mutual benef- beneficial for Google and for us, as it has been so far.

• 1:02:53Speaker 02

  We are using now with some postdocs, okay, that they are in the process, they are very talented postdocs. So to challenge the system with the questions they really want to address.

• 1:03:05Speaker 01

  Yeah.

• 1:03:05Speaker 02

  At different stages, so there are projects that are quite new, so to see how the system answer the questions or how the systems better establish, so yeah, we are having some kind of, but this is a different approach. It's now asking for new hypothesis, you know, and we will come time.

• 1:03:22Speaker 01

  Forward version.

• 1:03:24Speaker 02

  Yes, the forward version.

• 1:03:26Speaker 01

  I like your reverse version. I think, yeah, both make sense, but it's like forward and reverse genetics, like the reverse AI co-scientist, like it makes so much sense. Everybody else who's sitting on data that hasn't put it out.

• 1:03:40Speaker 02

  Another thing that it's quite funny, you know, I don't know. So we work also with plasma and the impact of transduction and plasma mobility, okay? So we have a paper now, again, nobody knows about the paper. So why you ever have a pack or cars sequences on plasma? Because if as a plasma, you really want to move, just put a pack or a cars and then that's it, you will fly, okay?

• 1:04:07Speaker 02

  So we have an answer for that. It's like in reality, plasma don't really want to move too much, okay? Because then we'll, if there is a plasma that move too much, it will lose variability or whatever, okay? But this idea of the plasma being less selfish than we thought, still not, it's a very small proportion in the literature. So far, for many years, we thought that the mobile are selfish, okay? So when you ask the system why there is not cars or pack sequences on plasma? That body of literature is more important than the cooperativity or whatever. So most of the hypothesis that are pretty good are wrong because that's just the selfish.

• 1:04:49Speaker 02

  You know, they do the things, it diminishes with cleavage the plasma that the plasma will not be functional. Or you know, what's more on the thinking that was a selfish element. Okay, it was something that blocked the biology of the plasma that's applied. So it's a quite funny thing, you know, the the thinking is really good.

• 1:05:09Speaker 01

  Yeah.

• 1:05:10Speaker 02

  But yeah, because most of the literature is based on the selfish thing. You can see that from the hypothesis. Okay, so the system also needs to learn probably what papers are more important. You know, the recent one versus the, I don't know. So this is, we are working there, you know. In some, we try against the reverse for the plasma thing. The hypothesis were pretty cool, but they were wrong. And we are working now.

• 1:05:37Speaker 05

  Have you, did you try the other L L M. Systems with that one?

• 1:05:41Speaker 02

  No, for, no, for this one. You can do that.

• 1:05:44Speaker 05

  Is that what we announced? I mean, I guess it could be scary, but could be interesting if, depending on the question you ask, different L L M. Works better.

• 1:05:52Speaker 02

  That's a good point, and we are now having the chat and we are telling the system, yeah, that's very cool. But you are ignoring that this is a really selfish. You can have some kind of another type of relationship with the cells. So let me know what you think, if you include that possibility as well. And we are waiting, you know, so this is, it's quite interesting.

• 1:06:13Speaker 05

  Yeah, I guess you want to be selfish, but within reason, because if you're dependent on bacteria for survival. If you get too selfish, you might kill your own host, like, I don't.

• 1:06:24Speaker 02

  Yeah, but.

• 1:06:25Speaker 05

  What I was, granted, I was working in a lab that studied this transposon T N ten. And it had this thing called multi copy inhibition. Because you would naively think that it just wants to keep jumping and making more and more. But eventually that's bad for the cell. So it has a mechanism that prevents it from transpose, makes it transpose less. When they get too many in the cell. And it was a really, so that's like, I was thinking about that when you were talking about it. Because again, that's another selfish D N A. That you think is like, the best thing would be for it to jump whenever it can. But if you start jumping too much, you trash the ECOI that it's in and then.

• 1:07:10Speaker 02

  Correct.

• 1:07:12Speaker 01

  Yeah.

• 1:07:12Speaker 05

  All right.

• 1:07:13Speaker 01

  Yeah, I was gonna find Joe's thesis soon and.

• 1:07:16Speaker 05

  Well, that's not what, not at least someone else's work.

• 1:07:20Speaker 01

  Well, thank you. Thank you all so much and good luck with everything and I can't wait to see your papers come out and we'll be posting them and yeah, can't wait to get this out, so. Keep.

• 1:07:32Speaker 02

  Thanks. Thanks a lot.

• 1:07:34Speaker 03

  Yeah, thank you. Thank you for the invitation. Okay, bye bye now.

• 1:07:37Speaker 01

  So welcome. Bye.

• 1:07:39Nathan Labenz?

  It is both energizing and enlightening to hear why people listen and learn what they value about the show. So please, don't hesitate to reach out via email at [email protected] or you can DM me on the social media platform of your choice.