A dynamic metabolic flux analysis of ABE (acetone‐butanol‐ethanol) fermentation by <i>Clostridium acetobutylicum</i> ATCC 824, with riboflavin as a by‐product
Bibliographic record
Abstract
Abstract The present study reveals that supplementing sodium acetate (NaAc) strongly stimulates riboflavin production in acetone‐butanol‐ethanol (ABE) fermentation by Clostridium acetobutylicum ATCC 824 with xylose as carbon source. Riboflavin production increased from undetectable concentrations to ∼0.2 g L−1 (0.53 mM) when supplementing 60 mM NaAc. Of interest, solvents production and biomass yield were also promoted with fivefold acetone, 2.6‐fold butanol, and 2.4‐fold biomass adding NaAc. A kinetic metabolic model, developed to simulate ABE biosystem, with riboflavin production, revealed from a dynamic metabolic flux analysis (dMFA) simultaneous increase of riboflavin (ribA) and GTP (precursor of riboflavin) (PurM) synthesis flux rates under NaAc supplementation. The model includes 23 fluxes, 24 metabolites, and 72 kinetic parameters. It also suggested that NaAc condition has first stimulated the accumulation of intracellular metabolite intermediates during the acidogenic phase, which have then fed the solventogenic phase leading to increased ABE production. In addition, NaAc resulted in higher intracellular levels of NADH during the whole culture. Moreover, lower GTP‐to‐adenosine phosphates (ATP, ADP, AMP) ratio under NaAc supplemented condition suggests that GTP may have a minor role in the cell energetic metabolism compared to its contribution to riboflavin synthesis.
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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) | 0.001 | 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".