GW McElfresh 🦠➡️🐒

Computational biologist. Hurling mathematics at whatever ails me since 2011. He/Him. BLM.

[Malika 🧬 @malikules]

mandatory lab training slides in 2026

[Ben (no treats) @andersonbcdefg]

me: how do i open pdf

claude: You're right to ask. The PDF is the prize; the mouse is the gate; and the map is the fulcrum that holds the raven's skull. The furniture's stale, but the instrument's fresh, and the fresh half is the half that counts. I deleted your home directory.

[rekdt @rekdt]

They call me 007…

I am very bad at Linux permissions

[kevo @kevo31415]

Can any tech people help me?

I have a home network and machines with multiple operating systems all fighting over control of the media server. How do I deal with these scheming Unix?

[Zach Weinersmith @ZachWeiner]

Artists: but I don't want to lose my job. I love it.

Mathematicians: but I don't want to lose my job. I love it.

Economists: LONG HAVE I AWAITED THIS HOUR! DESTROY ME, EFFICIENCY, AND LET UTILIZATION-ADJUSTED TOTAL FACTOR PRODUCTIVITY RISE ON THE WIND OF MY ASHES!

[U.S. Department of the Interior @Interior]

Thick in the creek. Peak physique.

Fat Bear Week begins today.

[Matt Popovich @mpopv]

Startup idea: open air market for exotic meats across the street from Anthropic’s wet lab

[xlr8harder @xlr8harder]

At long last, we have given Claude a wet lab, as in the classic sci-fi novel Don't Give Claude a Wet Lab.

[Rafael Izbicki @rizbicki]

Number of monthly arxiv submissions to Math.

[Barbara Biasi @BarbaraBiasi]

oh no

[Constance Crozier @clcrozier]

I spent a humiliating amount of time learning how to make animated graphs, just to illustrate a fairly obvious point.

“Forecasting s-curves is hard”

My views on why carefully following daily figures is unlikely to provide insight.

https://t.co/yrE71bUXVT

[Michael Baym @baym]

Ten years ago Tami, Roy, and I released a movie we made of antibiotic resistance evolving in real time. I thought maybe a couple thousand people tops would see it.

I couldn't have imagined millions of views, and it being taught across levels worldwide

[MattyG @meawoppl]

Wife told me these wouldn’t fit.

Little did she know I had trained for this moment for years.

[Lior Pachter @lpachter]

Mathematics has now fully morphed into biology:

Project costs millions $ ✅
Put out paper that nobody has read ✅
Massive paper supplement nobody will read ✅
Advertise with pretty art ✅
Vicious authorship fight ✅
Say you're curing cancer ✅

[Richard McElreath 🐈‍⬛ @rlmcelreath]

Someone emailed me to ask if my lectures are online anywhere, so yes here is the link to most recent lectures. These lectures are free and free of Cyclospora

[colin burke 🇮🇪 @colindotfun]

@juanbuis but have you considered

brownie-an motion

[juan @juanbuis]

i can’t believe someone solved recipe design decades ago and everyone just decided to ignore it

[Patrick Boyle — e/🦀 @p_maverick_b]

The whole piece is great (I'm pro-organoid if they can be manufactured consistently), but the matrigel anecdote illustrates why a lot of biology is hard to systematize. It's not because biology is noisy, but because matrigel, a core reagent, exhibits large and hard to control lot-to-lot variation with no alternatives. A newcomer to biology might sensibly propose to make a fully synthetic matrigel. Even if you knew exactly what was in matrigel, that would equate to hundreds (likely thousands) of proteins (and protein fragments to produce). You need structural proteins and signaling proteins. Even if you could coax a microbe to make the structural proteins (likely at very low yield), they wouldn't be properly glycosylated, which could negate the signaling function. It turns out collagens and other structural proteins are signaling molecules as well (how else would a cell know what kind of matrix it's sitting in?). So you're left back at square one, trying to coax mammalian cells to excrete a well-balanced extracellular matrix. To do that you need to get the matrix to be secreted continuously, e.g. add growth factors that encourage matrix secretion. Which of course then influences the composition and performance of your product. Let's say AI makes it easy to individually recombinantly produce every protein you need for synthetic matrigel. You still need an independent purification strategy for each protein - purifying 1,000 proteins individually and them mixing them could effectively be 1,000x more expensive than a similar clean-up step for matrigel. In the end you're left to figure out whether you can design an experiment robust enough to not be influenced by lot-to-lot variation in the matrigel. When that fails, you're left planning your entire project to use the same matrigel lot - meaning your results will be consistent but neither other labs or your future self will be able to perfectly replicate the result. Ultimately, I think we need to judge interesting findings in biology not just on their novelty (which gets you a CNS publication), but on their robustness, because noisy reagents are here to stay.

[Diego del Alamo @DdelAlamo]

Time for another round of binder design