Reconstruction and analysis of a three‐compartment genome‐scale metabolic model for <i>Pseudomonas fluorescens</i>
Bibliographic record
Abstract
With the versatile metabolic diversity, Pseudomonas fluorescens is a potential candidate in petroleum aromatic hydrocarbon (PAH) bioremediation. Genome-scale metabolic model (GSMM) can provide systematic information to guide the development of metabolic engineering strategy to improve microbial activity. In this study, a GSMM for P. fluorescens SBW25 was reconstructed, which is termed as lCW1057. The reconstruction was based on automatic reannotation and manual curation. The periplasmic compartment was constructed to better represent the proton gradient profile. The reconstructed proton transport chain has a P/O (ATP generated per atom oxygen consumed by the respiratory chain) ratio of 11/8. Flux balance analysis (FBA) was performed to explore the whole-cell metabolic flow. The model suggested that instead of Embden-Meyerhof-Parnas pathway, Entner-Doudoroff pathway was used in glycolytic metabolism of P. fluorescens, indicating that the growth of P. fluorescens is more energy dependent. Furthermore, P. fluorescens can use nitrate as the terminal electron acceptor for the glucose metabolism. The β-ketoadipate pathway was involved in catechol metabolism. The uptake of oxygen is mandatory for the aromatic ring cleavage. The in silico and in vitro maximum specific growth rate was compared, resulting in 10% difference when catechol was used as the sole carbon source.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".