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

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)

The inherent stochasticity of biological systems 😉

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.