Fabian Theis has been one of the key players in the various iterations of the Virtual Cell concept and initiatives. It is not hyperbole at all to say that his lab has been pushing the frontier of algorithms to understand multi-scale, multi-modal (”multiomics”), time-varying biological data.
Indeed, he has shaped the large scale vision and data collection through his leadership at Helmholtz, various startups and probably at Biohub itself.
Much of the recent attention in AI x Bio has been on protein and molecular design. AlphaFold, ESM and BioHub, Chai, Leash, Isomporphic, Boltz, Genesis, Octant, Baker Lab, and many many more. After a lot of initial excitement, Virtual Cell projects have received less attention, due in no small part to questions about the effectiveness of large-scale models in solving even basic Virtual Cell problems.
The Biohub announcement a few weeks ago got a lot of press for the (really) big numbers: $1.8B of commitments[1] from Biohub, NIH, DOE, Google and Meta. The discussion cites new imaging and measurement capabilities, generating large data sets and making the data available for training AI.
Source: Latent Space — Read the full article on the original site ↗