As the scientific pursuit of knowledge faces intimidating challenges, collaboration becomes even more important. I'm a staunch believer in "night-science", which works best when done with others (see this great article from Itai Yanai and Martin Lercher; doi.org/10.1038/s41587-023-02074-2).

Learning about this problem has been incredibly fun and exciting. I met @akshitg.bsky.social a little over a year ago at APS 2024. Since then, we've worked together on a number of projects. Collaborating with kind and curious people is my favorite part of science and Akshit is a prime example.

When we combine all known mechanisms, we still can't quantitatively account for the level of strain diversity observed in nature. This suggests a fundamental gap in our ecological understanding that requires new experimental measurements, new theoretical frameworks, and new dialogue between them.

We discuss several potential mechanisms that maintain this diversity and highlight their limitations: (1) niche-based (nutrient specialization, physiological tradeoffs, phage interactions, spatiotemporal dynamics), (2) neutral (migration, stochasticity), and (3) evolutionary (mutation, HGT).

We call this the "Paradox of the Sub-plankton" - how do organisms with >99.9% genetic identity avoid competitive exclusion and maintain diversity?

The microbial 'Paradox of the Plankton' asks why many species can coexist on limited nutrients. While we have some understanding of how this work at the species level, another puzzle exists at a finer scale: within species, nearly genetically identical 'strains' show distinct ecological behaviors.

Beyond putting together documentation and tutorials, we put together a brief preprint on the Chemrxiv
outlining the major goals and performance of the software. We would love to know how it works on your chromatographic data! t.co/8YXu7YZ3MS

Unlike extant chromatographic processing software, hplc-py allows the user to constrain specific parameters of the fit distribution. This allows one to deconvolve completely overlapping signals like the following:

This programmatic interface i) automatically detects peaks in a chromatogram, ii) fits a plausible mixture model to reconstruct the observed signal, and iii) computes and returns properties of each analyte. With hplc-py, you can go from raw data to this in ≤10 lines of Python.

If you’re interested in comprehensive guidance for improving your communication skills I cannot recommend the book “Trees, Maps, and Theorems” enough, www.principiae.be/X0100.php. 2/5