Coexistence of fluorescent <i>Escherichia coli</i> strains in millifluidic droplet reactors
Bibliographic record
Abstract
Understanding competition and cooperation within microbiota is of high fundamental and clinical importance, helping to comprehend species' evolution and biodiversity. We co-encapsulated and cultured two isogenic Escherichia coli strains expressing blue (BFP) and yellow (YFP) fluorescent proteins into numerous emulsion droplets and quantified their growth by employing fluorescence measurements. To characterize and compare the bacterial growth kinetics and behavior in mono and co-culture, we compared the experimental observations with predictions from a simple growth model. Varying the initial ratio (R0) of both cell types injected, we observed a broad landscape from competition to cooperation between both strains in their confined microenvironments depending on start frequency: from a nearly symmetric situation at R0 = 1, up to the domination of one subpopulation when R0 ≫ 1 (or R0 ≪ 1). Due to competition between the strains, their doubling times and final biomass ratios (R1) continuously deviate from the monoculture behavior. The correlation map of the two strains' doubling times reveals that the R0 is one of the critical parameters affecting the competitive interaction between isogenic bacterial strains. Thanks to this strategy, different species of bacteria can be monitored simultaneously in real-time. Further advantages include high statistical output, unaffected bacteria growth, and long-time measurements in a well-mixed environment. We expect that the millifluidic droplet-based reactor can be utilized for practical clinical applications, such as bacterial antibiotic resistance and enzyme reaction kinetics studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".