Defining Nutrient Combinations for Optimal Growth and Polyhydroxybutyrate Production by Methylosinus trichosporium OB3b Using Response Surface Methodology
Bibliographic record
Abstract
Methane and methanol are common industrial byproducts that can be used as feedstocks for the production of value-added products by methylotrophic bacteria. Alphaproteobacterial methanotrophs are known to produce and accumulate the biopolymer polyhydroxybutyrate (PHB) under conditions of nutrient starvation. The present study determined optimal production of biomass and PHB by Methylosinus trichosporium OB3b as a function of carbon source (methane or methanol), nitrogen source (ammonium or nitrate) and nitrogen to carbon ratio. Statistical regression analysis with interactions was performed to assess the importance of each factor, and their respective interactions, on biomass and PHB production. Higher biomass concentrations were obtained with methane as carbon source and with ammonium as nitrogen source. The nitrogen source that favored PHB production was ammonium for methane-grown cells and nitrate for methanol-grown cells. Response Surface Methodology (RSM) was used to determine conditions leading to optimal biomass and PHB production. As an example, the optimal PHB concentration was predicted to occur when a mixture of 30% methane and 70% methanol (molar basis) was used as carbon source with nitrate as nitrogen source and a nitrogen to carbon molar ratio of 0.017. This was confirmed experimentally, with a PHB concentration of 48.7 ± 8.3 mg/L culture, corresponding to a cell content of 52.5% ± 6.3% (cell dry weight basis). Using RSM to simultaneously interrogate multiple variables towards optimized growth and production of biopolymer serves as a guide for establishing more efficient industrial conditions to convert single-carbon feedstocks into value-added products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".