Physiology-informed use of Cupriavidus necator in biomanufacturing: a review of advances and challenges
Bibliographic record
Abstract
Biomanufacturing offers a potentially sustainable alternative to deriving chemicals from fossil fuels. However, traditional biomanufacturing, which uses sugars as feedstocks, competes with food production and yields unfavourable land use changes, so more sustainable options are necessary. Cupriavidus necator is a chemolithoautotrophic bacterium capable of consuming carbon dioxide and hydrogen as sole carbon and energy sources, or formate as the source of both. This autotrophic metabolism potentially makes chemical production using C. necator sustainable and attractive for biomanufacturing. Additionally, C. necator natively fixes carbon in the form of poly-3-hydroxybutyrate, which can be processed to make biodegradable plastic. Recent progress in development of modelling and synthetic biology tools have made C. necator much more usable as a biomanufacturing chassis. However, these tools and applications are often limited by a lack of consideration for the unique physiology and metabolic features of C. necator. As such, further work is required to better understand the intricate mechanisms that allow it to prioritise generalization over specialization. In this review, progress toward physiology-informed engineering of C. necator across several dimensions is critically discussed, and recommendations for moving toward a physiological approach are presented. Arguments for metabolic specialization, more focus on autotrophic fermentation, C. necator-specific synthetic biology tools, and modelling that goes beyond constraints are presented based on analysis of existing literature.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".