A cambrian explosion of new science funding models was driven by covid urgency, open science, frustration w/status quo. Most exciting: these models are built for translation & startup creation!
🧵of new science funding models:
1. Mega-LabCos
For-profits comprising several teams led by top scientists from academia. Groups are independent to enable blue sky creativity, but work toward a shared moonshot like taming non-model organisms (@ArcadiaScience) & cellular reprogramming for aging (Altos Labs).
2. Focused research organizations (FROs)
Non-profit institutes organized around a common goal like brain circuit mapping (E11 bio), non-model organisms (Cultivarium) and aging (Rejuvenome). Spearheaded by the great @AdamMarblestone!
4. University-affiliated institutes with philanthropic funding
@arcinstitute (just launched!) is funding and hosting labs affiliated w/ Stanford, Berkeley, and UCSF. Outside funding enables PIs to break the grant writing cycle and focus on long-term breakthrough ideas.
5. Fast grants
Fast grants (fastgrants.org) enable scientists to get money *fast* for COVID-related projects.
Science is joining the web3 revolution! Several DAOs assembled to work on both the *way* we do science (@lab_dao, @molecule_to) as well as discussing and funding science on underserved topics (@vita_dao, @PsyDAO_ ).
Honorable mention: Open access publishing & preprints.
Intrepid fighters (🙏 @mbeisen) spent years making preprints & OOS "acceptable". But covid broke the floodgates and now it's standard practice. Open science lets the public engage so ppl are newly fired up to fund science!
It's hard to predict which of these new approaches will stick, but it's obvious that slow government grants for incremental advances in university ivory towers aren't going to be the dominant model for long.
This is one evolutionary story I'll be watching very closely 👀
🔍ID tools = look for genes, proteins, metabolites using known set of probes
🔡Sequencing tools = read out these molecules de novo
[example: microarrays (ID) vs RNAseq (Seq)]
ID may win at first, but Seq will always win in the long run. Why? ...
ID tools are typically faster to invent and deploy, b/c we can "identify" by binding to a known probe and we know many ways of measuring binding. (eg antibodies)
Sequencing, however, will require new measurement tech for each molecule type.
So ID tools will be first to market.
But in the long run, people will want to identify what they don't know to look for - the unknown unknowns.
Sequencing enables discovery of *new* biology. (eg non-coding RNAs)
I get asked a lot why I chose to go from doing science to investing in science.
Here's my answer: a tale of two technologies. 🧵
Going into my PhD, I wanted to join the startup ecosystem in syn bio. If science was a 2D puzzle, commercializing added a 3rd dimension, an extra challenge -> extra fun.
At the time, two opposite, but highly influential stories were unfolding:
1) Biofuels📉 2) Solar📈
1) Biofuels📉
Biofuels tech was improving quickly and the core technology basically worked! And yet, companies couldn't take off.
The science "worked", yet there were fundamental issues with unit economics and scale-up, terms that were completely new to me at the time.