Latent Space: The AI Engineer Podcast
00:00:00 · Fabian Theis
[00:00:00] S0: 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. From mapping, we went to prediction. But now, we can go to actually steering the next experiment. But quite frankly, very often, the papers end it with some model. Right? Maybe some sort of concept figure, maybe add a little bit validation. But 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. A new thing we... 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. Obviously, the the ultimate vision being that we want to RL everything. We want to do RL also in in this loop. It's a bit out there, but, you know, I think we're we're slowly getting that.
00:00:41 · RJ Hanaki, Fabian Theis
[00:00:41] S1: Welcome to the Latent Space AI for Science Podcast. I'm RJ Hanaki, CTO of MirrorOmics. I'm joined by my cohost, Brandon Anderson, who develops RNA therapeutics and atomic AI. I can't emphasize enough how much Fabian Theis and his labs and companies have contributed to the development of systems biology and various iterations of the virtual cell concept. Papers and models come out of his lab faster than I can read them, and the lab is consistently moving the ball forward on our understanding of how to observe and model the behaviors and interactions of cells. Just as importantly, his work often focuses on creating the tools that researchers and scientists actually use in their day to day work. Most computational biologists have probably used ScanPy and Ndata at some point. And this work has led to the creation of SCverse, which is an amazing collection of open tools that has attracted many, many contributors. Of course, Fabian is one of those academics who has way more honors than you can possibly list. But suffice it to say that he leads a bunch of the AI and computational biology efforts at Helmholtz Munich and related institutions and participates also in many other industry efforts. Fabian, it's really great to meet you. Thanks a lot for joining us. [00:02:00] S0: Thanks for having me. I'm really looking forward to the discussion, man.
00:02:00 · Fabian Theis, RJ Hanaki
[00:02:00] S0: Thanks for having me. I'm really looking forward to the discussion, man. [00:02:02] S1: Yeah. I've been tracking your work for a long time. And so it's a great honor to finally get you on the show and talk to you. Let's kick things off with some of the hype happening right now. There's a ton of interest right now in structural biology, anthropic and open AI biopharma initiatives focus heavily on designing molecules right now. On the other hand, Virtual Cell and Systems Biology approaches really haven't gotten as much attention recently. Maybe Xaira and Noetik are good counter examples there. But Biohub just announced a $1,800,000,000 collaboration with the DOE, NIH, Meta, and Google to collect an open source multiscale biological data that can be used to develop a new generation of virtual cells. So my question, do you think that we're about to see renewed interest from the pharma industry in virtual cells based on the activity that we're seeing in academia right now.
00:02:56 · Fabian Theis
[00:02:56] S0: I should disclose before I sort of go to the answer that obviously my opinion is quite academic in the sense that whenever I talk to my friends and colleagues that really sort of day by day sit in in the startups, they have like like what I sort of have as an overview and sort of a bit of intuition what the different research labs do, they have the same thing for the startups. And they know this is sort of the the hot big thing that, you know, this and these investments are happening. I don't really have that on on on that scale. But having said that, for example, you you you you mentioned a Biohubs initiative, which, by the way, is also heavily coupled to Human Cell Atlas, which is sort of this international, really worldwide consortium that sort of pushes this next level of data set generation. I do think that there's a whole bunch of reasons why we're going to see that sort of scale up from all these models working well in terms of being generative, but also sort of lab in the loop on the molecule level now being exported and sort of really working one level up on the cell level. And I think there's a bunch of reasons for that. And the first one, think, and maybe the most important one which you really hit here is the data generation. I think we had that concept of a virtual cell for a while. Like my lab, we started this perturbation prediction modeling and and using machine learning in 2019 with a paper we called scGen at the time, which was like... It was a nonlinear model, but essentially sort of in the latent space did some linear transformation, and it was actually quite okay. And even nowadays, many of these sort of pseudo linear type of things are still doing a good job, but the reason for that being that I think the data sets and the questions that we asked on top were just sort of still on the simplistic side, that's why also sort of it didn't yet take up. So whenever people talk about virtual cells, maybe ask you a question, I think what you actually ask is how does... Can you predict how a cell would behave under all kind of perturbations? And that's sort of, I think, a quite deep question.
00:04:43 · Fabian Theis
If you really think about it, that's maybe even a bit of a holy grail in computational biology or systems biology, sort of this this input output system that is a cell with memory and, like, with all these specifications and with all the different sort of levels of molecules and so on, not just RNA, I I think being able to map this out, what does even understanding mean? And I've been... This is sort of a bit of a meta problem, but I I think you understand at least your modeler system very well when you sort of predict its output behavior given different inputs. So I think it's a very good question, and this is why often people nowadays equate a Virtual Cell with perturbation prediction. I think it's a bit too simplistic, but I think this perturbation prediction type of models and that sort of being, maybe what you mean by Virtual Cell is taken off a lot, and the startup landscape, I think is a bit exploding there. We talk, I think, a lot about US Europe, but also in the Chinese ecosystem, there's a lot of these additional efforts now happening, and I hope that these are not just sort of now a kindling off of a flame because everyone sort of adds AI to their things, but be because pharma is really desperately needing to be cell type context specific in their screens, which at the moment they can't do yet on that screen.
00:05:51 · RJ Hanaki
[00:05:51] S1: Maybe you could expound a little bit on what does this... So perturbations right now that... Like, we talked to Xaira recently. They're doing these perturbation screens. What does that unlock? And then what are... What do you think the next generation of data is going to unlock in addition to that?