Modeling the growth of diverse microorganisms during feast‐famine enrichment
Bibliographic record
Abstract
Polyhydroxyalkanoates (PHAs) are biodegradable polymers that can decrease the severe environmental pollution of petroleum plastics. PHA production by mixed microbial communities has been extensively studied to lower the high PHA prices. However, the competition between distinct microbial communities during the enrichment of PHA accumulators in mixed cultures has not been widely investigated. Thus, in this work, we developed a mathematical model for the competition between PHA accumulators and non-PHA accumulators in the feast-famine enrichment strategy. The developed model successfully simulated published lab-scale experimental data for Plasticicumulans acidivorans, a well-studied PHA accumulator that can store PHA up to 90% of the cell weight. The growth kinetics for both PHA and non-PHA accumulators were estimated and compared to the values in the literature. The uncertainties in the model kinetics were studied by expanding the model to include additional sub-biomass components for each heterotrophic group. As a result, the microbial diversity of microbial communities was observed to influence the enrichment of PHA accumulators in mixed cultures. Additionally, the calibrated model was applied to investigate the cultivation conditions, such as cycle lengths, carbon-to-nitrogen ratio, and solids retention time for successful P. acidivorans enrichment in mixed cultures. The developed model can be applied to control the cultivation and enrichment of PHA accumulators in large-scale PHA production systems. PRACTITIONER POINTS: A new model for the enrichment of PHA accumulators was developed. The model can simulate PHA accumulation by enriched cultures. The model was calibrated and validated for Plasticicumulans acidivorans. The impact of microbial diversity on enriching PHA accumulators was investigated. Short cycles (<12 h) and SRT (<10 d) are suggested for successful enrichment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".