Do you think you could advance these goals by working for one of the many companies building alternative proteins? Or are there special benefits to independent work? The latter totally seems possible, but the former would eliminate the need for EA to evaluate and fund the project while still enabling it.
My thoughts on this are similar to what Fai wrote. By working for one company, any work I do would only benefit that specific company. My own goal, however, is to solve a bottleneck that applies to many companies in that space, then make it available to all of them, thus having a much higher ROI, impact-wise.
I thought about using AI/ML to help alternative protein research and thought about whether it is better for talents to work for one of the alt-pro companies, or independently. I think I much prefer to see independent work on this because the research results will be much more likely to be shared among the whole industry. If I am a funder I think I will only consider funding this if this work will be open source.
You probably might have came across it already, but the Good Food Institute (GFI) has compiled a research funding database for alternative proteins (here).
Overall I think having collaborators that have the expertise and facilities to perform biological experiments and bioprocessing may be quite crucial. My experience is that at this moment, publicly available data are scarce and insufficient to support purely computational work in this field, unlike say cancer research where loads of multi-omics data are available for data mining and modeling.
I'm not sure if you live in Israel - just seeing signs suggesting it on your website - but have you contacted The Modern Agriculture Foundation (https://www.modern-agriculture.org/)? I know they're in touch with many companies locally and may have ideas about funding locally as well.
Hey Noa,
Thanks for writing this up!
As someone who's worked on similar stuff, I just wanted to pitch in with two thoughts (hopefully helpful!):
On extrusion modeling, I (& others) looked into something similar about 4-5 years ago and I personally I came away quite pessimistic for 2 reasons.i) The search space is practically a lot narrower than it seems (very limited by ingredients accessible and "suppliable" at scale).ii) Furthermore, an extrusion expert I spoke to thought that more of the variability probably came from ingredients rather than actual extrusion parameters so a model alone seemed unlikely to create "big" improvements to the texture / product quality.
Somewhat confirming this: I spoke to a senior researcher at a well known plant-based company + and an ex-one from a mainstream CPG company and they both confirmed that they'd tried something like this and came away from it without any real product improvements.
On the second idea, I have less to add but it may be worth checking out AMII's recent collab with New Harvest. More general hot take though: My (the low confidence) general impression has been that solving "big" fermentation / cultivated problems with an AI-ish approach has been somewhat bottlenecked by the ability to get good data and iterate (in other words, the "sensors" aren't great and limited). In-silico modeling is seemingly pretty far away too for most cell lines/platforms that I've heard folks talk about in recent years but unsure where that will go.
Other interesting things from an ML/AI perspective may be looking at areas like ingredient functionalization & processing for ingredient companies (ADM, Roquette etc.). The idea here would be to tweak processes to get better functionality or lower prices. There was some interest in this work 4-5 years ago but I haven't followed it much since.
Anyway, hope some of that is useful and that you find an interesting problem & funding!
Hi,
Thank you for this detailed reply! These are some very relevant facts. I know of some of the problems you mentioned, but the fact that others have tried and fail is new to me - I haven't been able to find anyone who attempted the same thing up until now.
I would love to talk to you about it f2f and get some more detail, if that's ok. Would you be willing to have a short call?
TL;DR:
A physical machine where you put ingredients in, and plant-based meat comes out.
The vast majority of plant-based meat solutions use this. It is also sometimes used for cultivated (lab-grown) meat.
Trial and error, mostly unpredictable.
Train a model to guess the result based on given ingredients.
In problems like this, the model often isn’t perfect but does help narrow down the search space significantly (for example, by recognizing that most sets of ingredients “obviously” won’t work)
TL;DR:
All bottlenecks here are known problems for alternative protein companies, according to the experts I spoke to, but please tell me if I’m wrong. For all problems here, there is at least one scientific paper that seems promising enough to explore (in my opinion).
If you’re an org that could use a solution for one or more of these problems, please get in touch!
By messaging me here, or emailing [me at weissnoa dot com].
Thanks to Yonatan Cale for helping me with this post.
Hey there, your ideas seem promising, hope you get the support you need. In the meantime, here's a few potentially relevant grant making organizations that may be able to help you, or hopefully, help direct you to other potential funding opportunities more mission aligned.
These are just ones that I found that could be relevant to your situation, but feel free to check out this this sheet maintained by Animal Advocacy Africa which may be useful too.
Best of luck!
This is extremely helpful, thank you!
No problem, glad it helped, best of luck!