Integrative genome-scale metabolic modeling reveals versatile metabolic strategies for methane utilization in <i>Methylomicrobium album</i> BG8
Bibliographic record
Abstract
Methylomicrobium album BG8 is an aerobic methanotrophic bacterium that can mitigate environmental methane emission, and is a promising microbial cell factory for the conversion of methane to value-added chemicals. However, the lack of a genome-scale metabolic model (GEM) of M. album BG8 has hindered the development of systems biology and metabolic engineering of this methanotroph. To fill this gap, a high-quality GEM was constructed to facilitate a system-level understanding on the biochemistry of M. album BG8. Next, experimental time-series growth and exometabolomics data were integrated into the model to generate context-specific GEMs. Flux balance analysis (FBA) constrained with experimental data derived from varying levels of methane, oxygen, and biomass were used to model the metabolism of M. album BG8 and investigate the metabolic states that promote the production of biomass and the excretion of carbon dioxide, formate, and acetate. The experimental and modeling results indicated that the system-level metabolic functions of M. album BG8 require a ratio > 1:1 between the oxygen and methane specific uptake rates for optimal growth. Integrative modeling revealed that at a high ratio of oxygen-to-methane uptake flux, carbon dioxide and formate were the preferred excreted compounds; at lower ratios, however, acetate accounted for a larger fraction of the total excreted flux. The results of this study reveal a trade-off between biomass production and organic compound excretion and provide evidence that this trade-off is linked to the ratio between the oxygen and methane specific uptake rates.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".