In silico characterization of microbial electrosynthesis for metabolic engineering of biochemicals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A critical concern in metabolic engineering is the need to balance the demand and supply of redox intermediates such as NADH. Bioelectrochemical techniques offer a novel and promising method to alleviate redox imbalances during the synthesis of biochemicals and biofuels. Broadly, these techniques reduce intracellular NAD+ to NADH and therefore manipulate the cell's redox balance. The cellular response to such redox changes and the additional reducing power available to the cell can be harnessed to produce desired metabolites. In the context of microbial fermentation, these bioelectrochemical techniques can be used to improve product yields and/or productivity. RESULTS: We have developed a method to characterize the role of bioelectrosynthesis in chemical production using the genome-scale metabolic model of E. coli. The results in this paper elucidate the role of bioelectrosynthesis and its impact on biomass growth, cellular ATP yields and biochemical production. The results also suggest that strain design strategies can change for fermentation processes that employ microbial electrosynthesis and suggest that dynamic operating strategies lead to maximizing productivity. CONCLUSIONS: The results in this paper provide a systematic understanding of the benefits and limitations of bioelectrochemical techniques for biochemical production and highlight how electrical enhancement can impact cellular metabolism and biochemical production.
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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 | 0.000 | 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) | 0.001 | 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 it