Effect of process parameters on succinic acid production in <i>Escherichia coli</i> W3110 and enzymes involved in the reductive tricarboxylic acid cycle
Bibliographic record
Abstract
The effect of process optimization on succinic acid production by Escherichia coli W3110 and on enzymes involved in the reverse tricarboxylic acid cycle was studied. Approximately, 7.02 g L–1 of succinic acid was produced in 60 h at pH 7.0 in 500 mL anaerobic bottles containing 300 mL of the medium, wherein the sucrose concentration was 2.5%, the ratio of tryptone to ammonium hydrogen phosphate was 1:1, and the concentration of magnesium carbon ate was 1.5%. When these optimized fermentation conditions were employed in a 10 L bioreactor, 11.2 g L–1 of succinic acid was produced in 48 h. This is a 10-fold increase in succinic acid production from the initial titer of 0.94 g L–1. This clearly indicates the importance of process optimization, where by manipulating the media composition and production conditions, a remarkable increase in the production of the desired biomolecule can be obtained. The production of succinic acid is a multi-step reaction through the reverse tricarboxylic acid cycle. A linear relationship was observed between succinic acid production and the enzyme activities. The enzyme activities were found to increase in the order phospho-enol-pyruvate carboxylase < malate dehydrogenase < fumarase < fumarate reductase. The activity of phospho-enol-pyruvate carboxykinase was also estimated. Results indicate that this enzyme was not a very active participant in the production of succinic acid, since it catalyzes the phosphorylation of oxaloacetic acid to yield phospho-enol-pyruvate.Key words: anaerobic production, succinic acid, Escherichia coli, process optimization, reverse tricarboxylic acid cycle enzymes, fermentation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".