Amir Mitchell

Just concluded a 2-week coding spree with a student using VS+codex. The x5-10 gain in productivity blew my mind 🤯. BUT, I'm also sure that for inexperienced coders AI-coding makes garbage-in-gargbe-out pitfall inescapable.

Beautiful “full arc” story on abx response with impressive level of mechanistic detail (could be a class example for undergrad microbiology lesson)

Congrats on publishing this beautiful work! I'm still mind-blown that: (1) there's a 7th RND pump in E. coli and (2) that its somewhat unclear what it actually pumps out IRL

It's a beautiful approach! But it's very disappointing that there is actually NO phenotypic characterization of the rearranged strains in the manuscript ... hoping it's an evolving manuscript that they'll keep updating

Totally agree. Fail early, fail often (and aim high) is something more scientists need to live by.

Huge credit to @carmenli.bsky.social for her persistence in chasing a moonlight project into its beautiful completion. Credit also goes Ethan Chang, the rotation student contributing to this work 9/9

Btw, note that “inactivation” can mean more than enzymatic degradation and includes any process that reduces effective drug activity over time (chemical modification, sequestration, etc) 8/9

We then tested if this assosiation holds through our entire dataset. We used a functional assay for drug inactivation on all drugs and found the association holds up. A long-lag inhibition phenotype is a strong indicator of drug inactivation 7/9

That pushed us to ask whether cellular defenses might impact curve profiles. We cloned different resistance cassettes and measured how they altered the potency-matched growth curves. This strongly hinted that active drug inactivation underlies a long-lag inhibition profile 6/9

Overlaying the known mechanisms of action over the barycentric landscape ruled out this effect stem exclusively from how drug target bacteria (since drugs with the same mechanism can land in very different regions of the landscape) 5/9

Clustering drug by their impact on lag/rate/yield clearly revealed that they vary hugely in how they inhibited growth. In extreme cases, a drug solely affected only a single parameter 4/9

To compare drugs fairly, we didn’t use an arbitrary concentration. Instead, we interpolated each drug to a potency-matched condition (the concentration expected to produce the same overall level of inhibition) 3/9

So we assembled a new carefully curated dataset with growth curves across almost forty drugs, measured across multiple sub-inhibitory concentrations. For each curve, we quantified intuitive its key features: lag, growth rate, and yield 2/9

Predicting drug inactivation by changes in bacterial growth dynamics - npj Antimicrobials and Resistance npj Antimicrobials and Resistance - Predicting drug inactivation by changes in bacterial growth dynamics

Our recent paper in npj Antimicrobials and Resistance is a great example of scientific serendipity: after staring at thousands of bacterial growth curves over many studies, we started wondering whether the curve shapes themselves carry mechanistic information 1/9 🦠🧪
www.nature.com/articles/s44...

This was a true collaboration between physicists (Andrew Mugler and @motasemelgamel.bsky.social), an immunologist (Michael Brehm from @umasschan.bsky.social ), and systems biologists (with @serkansayin.bsky.social and Brittany Rosener from my lab also at @umasschan.bsky.social ) (7/7)

Beyond providing (to our knowledge) the first dynamical model for tumor colonization, our study matters given the fierce debate on the tumor microbiome. These statistical “fingerprints” may help distinguish genuine colonizers from technical artifacts/contamination (possibly even by microscopy) (6/7)

The surprise: lineage sizes formed a scale-free power law that matches Zipf’s law (rank–frequency slope ~−1). This signature was robust across dozens of tumors and multiple collection days post bacteria intratumor injection (5/7)

When we injected bacteria directly into the tumor (circumventing the bottleneck), we detected thousands of colonizing lineages, yet their sizes were still highly uneven (ruling out early tumor arrivers dominate) (4/7)

Since we used genetically barcoded bacteria, we could also monitor growth of individual colonizers. We found that growth was extremely uneven with a handful of lineages becoming dominant (“winner-takes-most”) (3/7)

Main takeaways: Post systemic infection, there's a tight colonization bottleneck (per-cell colonization probability ~0.005%). Yet, once colonization happens, growth is remarkably fast (~50 min generation time) and bacterial load in tumors approaches saturation within a day (2/7)

We just published in @molsystbiol.org with the Mugler lab (UPitt) on bacterial population dynamics during tumor colonization (mouse model). Our study was guided by a Luria–Delbrück-style idea: infer mechanism from statistics (1/7) 🧪🦠
doi.org/10.1038/s443...

The pivot penalty in research - Nature An analysis of millions of scientific papers and patents reveals a ‘pivot penalty’ when researchers shift direction, with the impact of studies decreasing rapidly the further they move from their prev...

The (very real) cost of changing your research field. Absolutely true! But the freedom to pivot is one of the greatest things in this profession.
www.nature.com/articles/s41...

Great opportunity to participate in scientific outreach!

YouTube video by MicrobeTV Matters Microbial #89: Can AI Point Us to New Antibiotics

Had a fantastic time with @markowenmartin.bsky.social’s on his #MattersMicrobial podcast (@microbetv.bsky.social). We discussed how to uncover the killing mechanisms of hundreds of antibacterials simultaneously and how #AI can transform the search for new antibiotics 🧪🦠💊
youtu.be/Wa0zd-TRCO8?...

Yo, micro/evo/qbio/physics of living systems/astrobiology/renegade cell bio folks- if you are interested in starting a feed, reply/repost this. Let's get the crew back together!

Fascinating shift in the geography of drug development (excellent writing too!)

The inherent stochasticity of biological systems 😉

Interested in Antimicrobial Resistance & Quantitative Bio? Join a 3-day meeting+workshop with some of my favorite #AMR #QBio scientists this April (University of Exeter, UK). Register here: tinyurl.com/QAMR2025reg 🧪🦠

Amazing book, shaped my worldview on Science. Kuhn’s genius was also in claiming that “paradigm shifts” are not purely logical, but are tainted by social & psychological factors. He claimed that established scientists are often conservatives resisting such shifts.

It was all code written in Matlab