Andre Brown

Group leader at MRC London Institute of Medical Sciences and Reader at Imperial College London

[Andre Brown @aexbrown]

@curiouswavefn I think there's also a dynamic where the most important discoveries and theories become canonical quickly and people don't feel the need to cite them. Methods keep getting cited because they say specifically what you did.

[Convergent Research @Convergent_FROs]

We’re very excited to announce the launch of two new Focused Research Organizations in the UK powered by @ARIA_research: @Meridial_Neuro and @EchoLabsFRO !

https://t.co/eSaXv5msJV

[Pranay Shah @Pranay_Shahh]

We're hiring for someone to help write the ARIA playbook 🚀

We're looking for a 'Special Projects Manager (Science & Tech)' to create the projects, initiatives, and ecosystems to ensure we're pursuing the boldest scientific ideas that reach the real world.

Learn more below👇

[Andre Brown @aexbrown]

@sethbannon AgBio: @syntato_garden, @ResurrectBio, @PhytoformLabs
Bio: Cyclana (@lea_md_wenger)
Also Amodo (@tsmilton_amodo--presumably already well known to you!)

[Karen Sarkisyan @k_sarkisyan]

Two positions available in my lab in London to work on engineering plant traits:

🪴Postdoc and PhD student positions 🪴

Deadline very soon: 10 March 2026. Links in the comments.

Please share with suitable candidates!

[Karen Sarkisyan @k_sarkisyan]

PhD student position, co-supervised with @aexbrown:
https://t.co/BUbnYGWaGZ

Postdoc position:
https://t.co/CdoRE18RS7

[Andre Brown @aexbrown]

This is such a cool idea. Thinking up quests this weekend...

[aishwarya🍎 @aishdoingthings]

Delighted to share our new fund: https://t.co/nGl2ZhbWOq

> Soviet papers from the 50s!
> Antimalarial drugs from Mao’s China!
> Buried research on reality and consciousness!

Led by @WendiYan5 and seeded by @jwmares and @stuartbuck1

[Janelle Tam @janellehmtam]

@eric_is_weird and I are raising a fund to revitalize one of the most successful R&D models of the 20th century.
If you want to be a part of the next stage of this experiment, I'd love to chat!

[Tom Milton @tsmilton_amodo]

We’re hiring for “Field Leads” - people who will own verticals at Amodo. You will shape multi-million dollar R&D programmes, work closely with our incredible engineers, and build the 21st century’s most important hardware

[Andre Brown @aexbrown]

https://t.co/mwfzsJRiBz

[Andre Brown @aexbrown]

Come on UK. Let's do Lovelace Labs and more!

[Caleb Watney @calebwatney]

NSF is launching one of the most ambitious experiments in federal science funding in 75 years.

The program is called Tech Labs, and the goal is to invest ~$1 billion to seed new institutions of science and technology for the 21st century.

Instead of funding projects, the NSF will fund teams. I’m in the @WSJ today with a piece on why this matters (gift link): https://t.co/xteQ3NgWVC

Here’s the basic case:

1) Most federal science funding takes the form of small, incremental, project-based grants to individual scientists at universities.

2) The typical NSF grant is ~$250k/year to a professor with a couple of grad students and modest equipment over a few years. This is a perfectly reasonable way to fund some science, but it's not the only way.

3) A healthy portfolio needs more than one instrument. Project-based grants are like bonds: low-risk, steady, safe. But no one trying to maximize long-run returns would put 70% of their portfolio in bonds.

4) Yet that's basically what our civilian science funding portfolio looks like. Around 3/4ths of NSF and NIH grant funding is project-based.

5) Tech Labs is NSF's attempt to diversify that portfolio. The Tech Labs program is aiming for:

- $10-50 million/year awards per team

- 5+ year commitments

- Measuring impact through advancement up the Tech Readiness Level scale rather than papers published

- Up to ~$1 billion for the program

- Supporting research orgs outside traditional university structures

6) Scientific production looks very different than it did when the NSF launched 75 years ago. The lone genius at the chalkboard can only do so much. Frontier science + tech today is increasingly team-based, interdisciplinary, and infrastructure-intensive.

7) The team behind AlphaFold just won the Nobel Prize in Chemistry. It came from DeepMind, an AI lab with sustained institutional funding and full-time research teams. It would be near-impossible to fund this kind of work on a 3-year academic grant.

8) Same pattern at the @arcinstitute (8-year appointments, cross-cutting technical support teams) and @HHMIJanelia (massive infrastructure investments to map the complete fly brain). Ambitious science increasingly needs core institutional support, not a series of project grants stapled together.

9) Similarly, Focused Research Organizations (@Convergent_FROs) have showcased a new model supporting teams with concrete missions and predefined milestones to unlock new funding.

10) There’s a whole ecosystem of philanthropically-supported centers doing amazing research, like the Institute for Protein Design, the Allen Institute, the Flatiron Institute, the Whitehead Institute, the Wyss Institute, the Broad — the list goes on.

11) But philanthropy can’t reshape American science alone. The federal government spends close to $200 billion each year on research and development, an order of magnitude more than even the largest foundations.

12) If we want to change how science gets done at scale, federal funding has to evolve. And the NSF and NIH don’t have dedicated funding mechanisms to support or seed these sorts of organizations.

13) Earlier this year, I started working on a related framework called “X-Labs” that built on all this exciting institutional experimentation that’s been happening within the private and philanthropic sectors. It’s time for the federal government to step into the arena: https://t.co/0iVLobqQeA

14) Traditional university grants are still important for training the next generation of scientists and for certain kinds of curiosity-driven work. But after 75 years of putting nearly everything into one model, we should try something different.

15) And key program details are still being developed! You can reply to the Request for Information with suggestions or feedback on how to design this program here: https://t.co/R6MNo0ZfN1

16) Science is supposed to be about experimentation. Science funding should be too.

[Ruxandra Teslo 🧬 @RuxandraTeslo]

If you know anyone interested in advancing clinical trial abundance/ biomedical data transparency from an exciting bio policy role please DM.

[Laura Ryan @Lyan82]

New from @instituteGC: a proposal for Lovelace Disruptive Invention Labs

Is a better science possible, & how can we build it? 🏗️

Britain should pioneer a complementary model for research at the intersection of science & engineering – inverting core assumptions of modern public R&D

[Bennett Innovation Lab @BILCambridge]

@whittlelab @Rob__Miller @InstituteGC @Ri_Science It feels as though the UK is beginning to embrace a new model of growth: taking risks, backing talent and building institutions built for speed.

Discover more → https://t.co/E0Rm5Hp7TB

#ActionThisDay

[Ilan Gur @ilangur]

After three extraordinary years shaping ARIA’s foundation, I’m excited to share that Kathleen Fisher will be the agency’s next CEO. /1

[Jake Wintermute 🧬/acc @SynBio1]

If there are 30,000 centimillionaires worldwide and the global prevalence of rare diseases is about 5%, that means there are about 1500 rare diseases with a viable single-customer business model.

Why aren't all of these active R&D projects?

[Louis Andre @louisnandre]

Today, we're announcing @episteme, a new type of R&D company that recruits exceptional scientists to pursue high-impact ideas.

Science isn’t bottlenecked by the availability of talent, but by places where they can do their best work.

Scientific progress has driven human flourishing: extending lifespans, lifting billions from poverty, and expanding our understanding of the universe.

But history is littered with transformational ideas that were overlooked in their time. That problem is still acute today: too much promising talent remains uncultivated, and remarkable ideas die in the lab or are filtered out by misaligned incentives.

Today, scientists face suboptimal paths for translating their research into impact: academia is famously risk-averse and incentivizes publications and winning grants vs. translational research. Industry is too often focused on short‑term incentives. And startups lack the substantial capital, expertise, and complex infrastructure needed to deliver long-term scientific progress.

On top of that, recent funding cuts in the US mean the overall supply of ideas is decreasing. Put together, the global scientific production system is operating at a fraction of its capacity.

How Episteme operates is different: we identify great scientists who can meaningfully benefit humanity, but who aren’t supported efficiently within traditional institutions today. Researcher by researcher, we work with them to determine the bespoke resources, operational support, and environmental conditions to execute on their research. We bring them together in-house, and provide those resources to ensure that their breakthroughs are deployed for real-world impact.

We’ve already assembled an amazing team of operators, ranging from the Gates Foundation, DeepMind, ARPAs, DoE – just to name a few – and researchers who are pursuing important problems across physics, biology, computing, and energy. Our team has spoken to hundreds of researchers across disciplines and geographies to understand the limitations they’re facing and what can be done better, and designed Episteme for them.

We’re backed by individuals like @sama, Masayoshi Son, and other long-term partners who share our mission of enabling ambitious science for tangible human impact.

About me: I started working as a researcher 9 years ago, on problems ranging from AI-driven drug discovery to developing brain-machine interfaces. It was that experience that led me to realize that so many scientists with great potential to change the world don’t have access to opportunities equal to their capacities.

@sama and I believe that much better science should happen for humanity, and that a new engine is needed to support that. We decided to cofound Episteme together, and I am incredibly grateful for Sam’s unwavering support as a thought partner and founding investor.

Our conviction is that by supporting the right people with the right incentives, we're set to generate breakthrough discoveries to benefit humanity. We cannot rely on the course of history to shape scientific progress; we need to proactively shape the system by supporting the most talented people with the right resources and incentives.

[Andre Brown @aexbrown]

If David Jordan writes, I read.

[Asimov Press @AsimovPress]

Many great discoveries in biology came about because of LIMIT THINKING. Rather than focus on small tweaks to improve a system, it's sometimes better to make them abstract to find that system's theoretical limits.

Carnot did this with engines. Shannon did this with information. Hopfield did this with kinetic proofreading.

Limit Thinking forces one to focus solely on the features of a system essential for its performance, so that one can make predictions or evaluations, regardless of the specifics of how each individual system is built. It grounds problems in mathematics — as one cannot calculate limits without being precise about what is being measured and in what units. Once such calculations have been determined, they often drive rapid progress, signaling not only when we have reached diminishing returns, but also just how far we can aspire.

In 1712, for example, Thomas Newcomen constructed his newly designed “atmospheric engine” for Coneygree Coalworks. Each stroke of the 20-ton machine would raise about 37 liters of water from the flooded mines below.

Although Newcomen's engines were cost-effective compared to horses or humans, they were still expensive to operate.

In 1763, while working at the University of Glasgow, James Watt was tasked with repairing the university’s scale model of the Newcomen engine. As he worked, Watt envisioned ways to improve the efficiency of the design. In 1776, he unveiled an engine with seemingly extraordinary modifications: it consumed 75-80 percent less fuel than Newcomen’s. Tasks that would have burned 100 kilograms of coal could now be done with a mere 20.

Although a triumph of engineering, Watt’s engine was nowhere near its optimal performance. The difference between Newcomen’s and Watt’s engines, in fact, is between 0.5 percent and 2.5 percent efficiency. But these inventors could not have known this because the concept of a theoretical limit — asking how efficient an engine design could be, in principle — had not yet been imagined.

But then, in 1824, a 28-year-old Nicolas Léonard Sadi Carnot aimed to determine the fundamental limits of how heat could be converted into mechanical work.

He wrote a 118-page booklet, of which he printed 600 copies at his own expense, that was largely ignored until 1834.

But Carnot’s work showed that what mattered for the efficiency of a heat engine was the temperature differential between the hot and cold reservoirs, rather than any particular design feature. He says of the motive power of heat: “Its quantity is fixed solely by the temperatures of the bodies between which it is effected.”

In retrospect, this simple fact explained why the Watt engine was superior to the Newcomen engine; the separate condenser allowed for a larger difference between the reservoirs. The same style of thinking also applies to biology, as we explore in our new essay by David Jordan:

https://t.co/zm5lNQLgdg

[Tom Milton @tsmilton_amodo]

The main thing working with @ARIA_research does is raise our ambition. Today's @thetimes piece mentions @MintNeuro , one of ARIA’s grantees we’ve been working with 🧵

[Andre Brown @aexbrown]

@OdedRechavi

[Mike O'Donnell @_MikeOD_]

For the worm-types, the destination of the glucose-octopamine complex is CAN - whose function is essential but not well understood. CAN appears to have the capacity to release octopamine but not to make it itself, implying it acts like a neuron under certain circumstances. (5/6)