[frisco]

As someone who used to think a lot of the stuff in here, and definitely no longer does, I can say that this is not someone who is a practitioner.

Adam, if you’re reading this, I would definitely encourage you to take a step back and go deep on how you might prove that the problems are where you think they are. The stuff you’re describing has all been done many times at this point and just either hasn’t turned out to be valuable to solve, or now has big effective companies doing it, or is extremely difficult for non-obvious reasons. Your characterization of total doom in modern experimentation is inaccurate.

[udp]

It would be more helpful to point out some specific examples of what has already been tried and what went wrong than just saying “don’t go there”

[frisco]

Sure, that’s fair.

An API driven vivarium as a service with instrumented cages was tried to tens of $M by Vium. That didn’t take off, though there are lots of vendors of sophisticated cage instrumentation now, including based on computer vision. DeepLabCut is amazing and open source.

In terms of general progress in experimental tools, there has been tons. The modern super resolution confocals; affordable femtosecond light sources like the Coherent Monaco enables all kinds of awesome stuff; or newer methods like MERFISH and PatchSeq (or hell, just the total commodification of sequencing generally).

Microfludics are now widely used and super valuable as “ASICs”, though I think the lack of a general purpose “CPU” lab of a chip has misled people not in the field.

In terms of molecular tools, it’s just night and day from 10 years ago. iPSCs, CRISPR, expansion microscopy, tons of new labels and stains etc.

Ginkgo and Zymergen have enormous scale, invest heavily in software and robotics, and are working “in vivo.” Recursion also invests heavily in automation, and while I don’t think they run animals in house, it’s not clear what Transistor is proposing that would outperform them.

Lots of companies run lots of studies in tons of different species all the time. Less so in academia, but I don’t think saying “well everyone else is working in vitro and we will work in vivo” is the kind of arbitrage opportunity Transistor seems to think it is. Where there are bottlenecks that I think could be improved, they are either unsexy (an easy Stripe-product-quality 3rd party IACUC would be super useful) or hinge on showing up with an enormous bucket of money so you can do things like set up your own breeding colonies.

And of course scientists really do care about being right and finding lasting results that are big effects. It is so much harder than it looks to do that well, but the people working in it are super smart and, at least outside of academia, generally have good incentives.

Edit: I clicked through to their “Business” page, which reads in part:

> Transistor will instead build a system designed with speed and scale in mind from the beginning: an automated wet lab with an API interface. Current CROs require bureaucratic back and forth which can extend into the months and are extraordinarily expensive for results that one crosses their fingers and hopes are correct.

May I point them to a company I founded 9 years ago, which raised a $56M series B last week: https://strateos.com/

Does this mean an opportunity exists? Maybe. But I think Transistor has some education to do on where the true problems that would be valuable to solve lie.

[gkk]

As someone with software background and interested in bio, it's a real pleasure to see a commentary from an experienced practitioner.

While we're discussing venues of progress, it's clear that software (and deep learning advances specifically) is poised to have a large impact on how bio research is conducted, and what categories of questions we'll be able to answer. The current consensus on how you leverage software in practice is to put both bio and software teams under one roof (Insitro and Recursion are canonical examples). I wonder if you think software-only company makes sense in this space? The analogy I like to use: people used to roll out their own accounting software within large enterprises until spreadsheets came along. Is there room for an equivalent in some segment of bio?

[agumonkey]

Incredible amount of stuff I never hear about, is there a journal or conference to track to follow advances in this field ?

[Gatsky]

Yeah, only someone with the most cursory undersanding of experimental life science could write something like this.

[dnautics]

as someone who has worked in biology and "building fault tolerant systems" (erlang), I think this might be a real problem (I don't know if it is; the last lab stuff I did was just before automation), but it's simultaneously obvious that the writer has no clue about how to correctly merge the two concepts.