| So this was Synthego’s OG thesis, but it didn’t validate in the market. In the last 5 years, the industry has moved to using the LabCyte Echo in high-well-count plates for this kinda work. Zymergen (RIP) Amyris and Ginkgo have this scaled up to something that resembles model train layouts, where plates are shuffled between discrete workcells by little trains. One of the challenges is the sheer volume of data — Illumina sequencers generate multi-TB files for analysis (synthetic biology context) — with most folks not having “fast datacenter networks” so overwhelmingly I see folks buying Snowballs, AWS direct connect, or running on-prem. Industry is broadly interested in this kinda thing, with efforts like [1] [2] (me), and many many others integrating into the Design-Build-Test pipeline. Commercial MD (not necessarily only protein folding) has had a huge boost due to NN’s as well, with companies like [3] [4] cropping up in order to sell their analysis as a service. Academia has also not been sitting idle, with labs like [5] [6] doing cool stuff Pure, classic microfluidic setups are a huge PITA, but technologies like the Echo or [7] have the potential to change some of the unit economics. [1] https://atomscience.org/ [2] https://radix.bio/ [3] https://deepcure.ai/ [4] https://syntensor.com/ [5] https://www.damplab.org/ [6] https://www.chem.gla.ac.uk/cronin/ [7] https://www.voltalabs.com/ |