Latent Space: The AI Engineer Podcast
00:40:56 · Fabian Theis, RJ Hanaki
[00:40:56] S0: I mean, I I I think Noetik is is is a good example in the sense that they also have, I think, a rather nice scale of data generation. And a big part of that is also access to data. So, you know, we talked before about these organoid perturbation type of things, and we've been setting up actually in collaboration with a whole bunch of pharma, a consortium which we call the Cancer Organoid Perturbation Atlas, where then pharma actually buys in because they wanna get early access to some of these sort of perturbation type of datasets, Right? And I think some of these deals sort go in these these directions. So I think if you couple it with data generation, not just with the modeling part, I think this is where sweet spot hits where then also actually pharma gets buying. I think more and more of the companies I see, they sort of build their moat not just around a Virtual Cell model, but in combination with sort of unique sort of specific data. This is then, of course, also more value creation. [00:41:41] S1: I don't know if this is exactly correct, but I read that you said somewhere that you have your students follow what what you I think called a why model, in which you have to have a methods and a wet lab adviser. That sounds super interesting, super relevant to the conversation we're having right now about data being so important, and all the attention on lab work and that being moat for a lot of companies. Can you talk a little bit about that?
00:42:07 · Fabian Theis
[00:42:07] S0: Yeah. Thanks for raising this. So I've been setting up, and since then, it's been sort of become quite a big thing, what's called the Munich School for Data Science. Right? This is beyond just just bias. We had a whole bunch of other disciplines, and there we had this supervisor model that is always two PIs needed to come together to make a proposal, then sort of proposals to the students and a graduate student type of model, which has this this y. So there's like a method, more domain one, right, and then you supervise student. And sometimes we had two students under the y model, so y plus y equals h. You get it? But but I I think this idea is... It's somewhat even nowadays a bit trivial. Right? But I I thought it was good to bake this in so hard so that students really ideally go to two lab meetings, one lab meeting where they need to really sort of learn and understand some deeper biological problem, understand the language maybe of a medical setup or something like that, and then they go to computational. Typically, they're all computation data science type of students, right, But then they go to the computation one, they sort of get pushed the method aspect of it, and thereby sucking up a a whole bunch of of problems that are sort of not exactly precisely yet formulated in in that bio lab that sort of can't directly put that in a function, but then because they are sort of method y, right, and they're quantitative, they can actually put this into a metric or whatever that then turns into a model and in a paper. And I I think this is a way how... I mean, we've been doing this now for maybe eight years or so, this this type of school. I really... A whole bunch of papers came out where I I I learned about systems that I had no idea about this before. Right? You know, people approached me, you know, could we do something like this? And then, you know, I learned something about the lung or the brain that, you know, I had no idea about. But then I said, oh, hey. Cool data. I think our model types could fit. Let's build a project around it. And then those students became the experts in my lab for that sort of detailed biological thing together with a partner, and then we we drive this forward. And this actually nowadays extend also across the data types.
00:43:51 · Fabian Theis, RJ Hanaki
Early on, a lot of things have been around just RNA, but you know, since then, you know, we had a very nice paper in science last year on single cell proteomics, we've been extending things to spatial observations, you know, there's sort of all these additional modalities and things that you can can can measure, and I think then interesting questions come up in that particular domain where you know, assays work very well. [00:44:11] S1: Yeah. I mean, is my natural inclination is to like gathering cross domain stuff. It, I think it's all often ripe for good, good generation of really good and interesting ideas. Have you just as a follow-up, these you've been doing this eight years now, I guess. So what kinds of, you know, sort of longer term outcome benefits have you seen from this?
00:44:33 · Fabian Theis
[00:44:33] S0: I think one thing that this is also, I think, how we got students to even join in this maybe initially. It sounds like more work. Right? You kinda need to learn two things, and you go to two lab meetings and so on. But I think once, you know, students are done in in in in in Germany, Europe, think in general, but I in Europe in general, people sort of typically do a master's first before they go into a PhD. Right? So not just bachelors in US. Right? So you do, like, one or two or more of master. And and then they sort of know science a little bit, but they don't exactly know what sort of interests them. So in this combination type of thing, you know, they can sort of sample a bit because in this grad school, they just get different proposals, so they can sample a bit what they're interested in terms of domain, but then they can also play around a bit with the method side. So I think that was kind of attractive. Yeah. I I think with this sort of trying out different things, I had the impression that this sort of turned the students into very good data analysts, but then also modeling people. And then a downstream, you know, the sort of areas where they they went into was also equally diverse, know. So some people went back in now in the in the biomedical domain into pharma, but, you know, some obviously is data research, some went more into some sort of startup direction, some went into very pure AI type of work. I think NVIDIA just a few months ago were hired in one month, two of my people going very much into sort of the computation aspect, and also a bit of a biopart that NVIDIA is now building up, and there are similar other of the big companies. So I think it it gives them an an an interesting profile. I think when... Nowadays, when you get interviewed, you know, it's it's a good idea to say, you know, I I didn't only do, this fancy model that improved maybe 2% in a NeurIPS paper type of thing, but you also sort of had the real sort of more complex, deeper application that, like, you know, I had to help find as as a use case.
00:46:07 · Brandon Anderson
[00:46:07] S2: There's been a movement amongst AgenTeq AI. Like, several guests we've had or we've had a blog post about how the wet lab scientists have started basically becoming computational thanks to AgenTeq AI. I don't know if this is... As I'm kinda curious, like, as this evolves, as basically now AgenTake AI enables any scientist to be now become their own computational, what do you think the evolution of this sort of, like, co advising strategy is? And also, what do you think the evolution of, like, integration between wet lab, biologists, and computational teams are? I mean, I also can think of, like... I've heard several, you know, famous CEOs say things like, our strength is integration between computational and, well, lab teams. So I'm I'm curious to see how you think that evolves and how you also, like, set up culture to exploit, you know, these new technologies.
00:46:56 · Fabian Theis
[00:46:56] S0: Let let me maybe sort of keep the the culture part a bit out because I think it's a hard one to answer, but a very good one. So let me first sort of talk about the, I think, very cool, cool point that I I think you asked about where where these things happen sort of goes with deep integration. I think one of the most exciting frontiers we currently face, and I think maybe have as an opportunity, is we can really now start guiding the experimentation. So where before, you know, we built these human cell atlases. We had sort of this mapping aspect. Then we talked about the word model prediction problem. Right? So from mapping, we went to prediction. But now we can go to actually steering the next experiment. Right? And this has always been the scientific method, know, sort of putting a hypothesis in the loop and then doing something with it. But quite frankly, very often the papers end it with some model, right, maybe some sort of concept figure, maybe at a little bit validation. Then typically you start the next project, right. And of course you learn a bit, but there's not like not like a continual learning type of thing. But now, you know, I think we can actually really loop back more and more. This is happening very much so in molecule design or material design and so on, you know, sort of the lab and the loop is really working out. And even though some big pharma has been putting this out as sort of a general general big theme, there's not many examples here where really people do multiple loops of this integration of an experiment, a computation model, then going back, whether this is now two person to one. Right? But like this integration is hard to set up. And it's hard to set up once because of modeling, because actually sort of once you have a model, actually deciding what's an optimal next question is not trivial. And we actually brought out, I think it should be on on bio archive today or tomorrow, a new thing which we call Lab Compass, which is an inverse generative model that actually allows you once you have a forward world model or whatever, to tell you what you need to do in order to reach a certain state. So super exciting for us, right. And I think a big part of the work was not just the model, but actually the work with experimental people.