Griffin Chure
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gchure.bsky.social
Griffin Chure
@gchure.bsky.social
880 followers 570 following 12 posts
A computational scientist building tools for AI-enabled biological discovery Profluent Bio. https://gchure.github.io
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gchure.bsky.social
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).
The figure is organized in four sections (A-D), each presenting a different mechanism with its takeaway insight and caveat: (A) Nutrient diversity: Shows bacteria interacting with different nutrients and metabolites. The takeaway is that strains can develop different nutrient preferences and abilities to consume secreted metabolic byproducts. The caveat notes that strain diversity typically exceeds plausible nutrient diversity, forcing strains to compete within the same niche. (B) Physiological tradeoffs: Visualizes how different strains allocate their enzyme budget. The takeaway is that tradeoffs in enzyme allocation can yield strains with different metabolic abilities but identical fitness, enabling coexistence. The caveat highlights that any difference in enzyme budgets would alter fitness and disrupt coexistence. (C) Anti-symmetric interactions: Depicts phage-bacteria interactions. The takeaway is that variation in susceptibility to phage predation can indirectly benefit other community members. The caveat notes that high genetic similarity between strains makes significant differences in susceptibility unlikely. (D) Spatiotemporal dynamics: Shows bacteria in different spatial compartments. The takeaway is that compartmentalization in space or time can lead to apparent coexistence when averaged across dimensions. The caveat points out that limited space and stochastic temporal dynamics make robust compartmentalization difficult to achieve. At the top, an overarching question is highlighted: "Niches are not infinitely divisible. How do we quantitatively account for high strain diversity, but limited niche diversity?" Figure 3 illustrates neutral and evolutionary mechanisms that could contribute to strain-level coexistence in microbial communities. The figure is organized in two main sections (A-B), each presenting different mechanisms with their takeaway insights and caveats: (A) Neutral mechanisms: Divided into "migration" (left) showing bacteria moving between spatial compartments, and "stochastic events" (right) depicting regulatory stochasticity, vegetative growth, and spore formation. The takeaway states that dynamics aren't always fitness-dependent as random chance can overwhelm competition. The caveat notes these effects only matter in small population regimes. An overarching question asks at what genetic, spatial, and temporal scales these neutral mechanisms affect coexistence. (B) Evolutionary mechanisms: Divided into "mutation" (left) showing genetic changes within a lineage, and "horizontal transfer" (right) depicting acquisition of genetic material from other organisms. The takeaway highlights that unlike species, new strains can emerge de novo on ecologically relevant timescales. The caveat emphasizes that strains must originate as single cells and overcome neutral mechanisms to establish. The opening question asks how plastic genomes are over ecological timescales and how widespread horizontal transfer is in nature. Each mechanism is visually represented with bacterial cells containing simplified genomes, with genetic changes highlighted in red.
April 25, 2025 at 6:10 PM
gchure.bsky.social
We call this the "Paradox of the Sub-plankton" - how do organisms with >99.9% genetic identity avoid competitive exclusion and maintain diversity?
Figure 1 illustrates the concept of coexistence across taxonomic scales. Panel A shows a hierarchical view of ecological interactions and community dynamics at three levels of resolution. The top row depicts schematic interactions (shown with arrows) at ecosystem, species, and strain levels, zooming in progressively from left to right. The bottom row shows corresponding temporal dynamics of biomass composition at each level, with colored areas representing relative abundance over time. Panel B provides two specific examples of strain coexistence: Left shows an experimental E. coli community (from Good et al. 2017) with two strain clades persisting over 50,000-55,000 generations in a laboratory flask. Right shows a natural Pseudomonas community from pitcher plants (from Goyal et al. 2022) with two strains coexisting over 300 generations with fluctuating but persistent relative frequencies. The figure demonstrates how diversity and coexistence patterns exist at multiple biological scales, with particular focus on strain-level diversity that represents the "paradox of the sub-plankton" discussed in the paper.
April 25, 2025 at 6:10 PM
gchure.bsky.social
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:
October 10, 2023 at 4:51 PM
gchure.bsky.social
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.
October 10, 2023 at 4:50 PM
gchure.bsky.social
Over the past two years, I've used HPLC to measure carbon turnover in microbes. The most painful part was quantifying the resulting chromatograms. Today, I (with Jonas Cremer) released hplc-py: A python tool that makes quantification easy. cremerlab.github.io/hplc-py
October 10, 2023 at 4:49 PM