CRISPR Tools in Bacterial Whole-Cell Biocatalysis
Bibliographic record
Abstract
Biocatalysis has emerged as a promising alternative to conventional chemical processes for the production of a wide range of chemicals, providing a sustainable solution to the problem of limited resources due to an ever-increasing global population. This approach involves the use of biobased catalysts, such as whole microorganisms or enzymes, to perform chemical conversions. While whole-cell biocatalysts offer advantages over the use of free enzymes, limitations related to productivity and undesired compound production have been observed when using microorganisms. Offering high specificity, broad applicability, and increased efficiency over traditional genetic engineering methods, CRISPR-based technologies may be the quintessential tool for the fit-for-purpose design of efficient bacterial biocatalysts. In this work, we aim to demonstrate the potential of CRISPR-based technologies to enhance whole-cell bacterial biotransformations for a more sustainable obtention of industrially important products. We have included a comprehensive and in-depth analysis of the current state of the art, emphasizing challenges and opportunities for future research. Through a critical analysis of reported examples, we intend to highlight the opportunities and advantages offered by CRISPR-based technologies in the field of biocatalysis for more efficient, sustainable, and translational processes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".