Modelling Cell Growth and Polyhydroxyalkanoate (PHA) Polymer Synthesisby <i>Pseudomonas Putida</i> LS46 under Oxygen-Limiting Conditions
Bibliographic record
Abstract
Background: Polyhydroxyalkanoates (PHAs) are biodegradable, biocompatible, and non-toxic polymers synthesized by bacteria that may be used to displace some petroleum-based plastic materials. One of the major barriers to the commercialization of PHA biosynthesis is the high cost of production. Objective: Oxygen-limitation is known to greatly influence bacterial cell growth and PHA production. In this study, the growth and synthesis of medium chain length PHAs (mcl-PHAs) by Pseudomonas putida LS46, cultured in batch-mode with octanoic acid, under oxygen-limited conditions, was modeled. Methods: Four models, including the Monod model, incorporated Leudeking-Piret (MLP), the Moser model incorporated Leudeking-Piret (Moser-LP), the Logistic model incorporated Leudeking- Piret (LLP), and the Modified Logistic model incorporated Leudeking-Piret (MLLP) were investigated. Kinetic parameters of each model were calibrated using the multi-objective optimization algorithm, Pareto Archived Dynamically Dimensioned Search (PA-DDS), by minimizing the sum of absolute error (SAE) for PHA production and growth simultaneously. Results and Conclusions: Among the four models, MLP and Moser-LP models adequately represented the experimental data for oxygen-limited conditions. However, the MLP and Moser-LP models could not adequately simulate PHA production under oxygen-excess conditions. Modeling cell growth and PHA will assist in the development of a strategy for industrial-scale production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".