Kinetic Modeling and Isotopic Investigation of Isobutanol Fermentation by Two Engineered Escherichia coli Strains
Bibliographic record
Abstract
We constructed an Escherichia coli BL21 strain with the Ehrlich pathway (the low-performance strain for isobutanol production). We also obtained a high isobutanol-producing E. coli strain JCL260 from the James Liao group (University of California). To compare the fermentation performances of the two engineered strains, we employed a general Monod-based model coupled with mixed-growth-associated isobutanol formation kinetics to simulate glucose consumption, biomass growth, and product secretion/loss under different cultivation conditions. On the basis of both kinetic data and additional 13 C-isotopic investigation, we found that the low-performance strain demonstrated robust biomass growth in the minimal growth medium (20 g/L glucose), achieving isobutanol production (up to 0.95 g/L). It utilized significant amounts of yeast extract to synthesize isobutanol when it grew in the rich medium. The rich medium also enhanced waste product secretion, and thus reduced the glucose-based isobutanol yield. In contrast, JCL260 had poor biomass growth in the minimal medium due to an inflated Monod constant ( K S ), while the rich medium greatly promoted both biomass growth and isobutanol productivity (∼60% of the theoretical isobutanol yield). With the optimized keto-acid pathway, JCL260 synthesized isobutanol mostly from glucose even in the presence of sufficient yeast extract. This study not only provided a kinetic model for scaled-up isobutanol fermentation but also offered metabolic insights into the performance trade-off between the two engineered E. coli strains.
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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 | 0.001 | 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 | 0.000 | 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 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".