Advances in engineering the production of the natural red pigment lycopene: A systematic review from a biotechnology perspective
Bibliographic record
Abstract
BACKGROUND: Lycopene is a natural red compound with potent antioxidant activity that can be utilized both as pigment and as a raw material in functional food, and so possesses good commercial prospects. The biosynthetic pathway has already been documented, which provides the foundation for lycopene production using biotechnology. AIM OF REVIEW: Although lycopene production has begun to take shape, there is still an urgent need to alleviate the yield of lycopene. Progress in this area can provide useful reference for metabolic engineering of lycopene production utilizing multiple approaches. KEY SCIENTIFIC CONCEPTS OF REVIEW: Using conventional microbial fermentation approaches, biotechnologists have enhanced the yield of lycopene by selecting suitable host strains, utilizing various additives, and optimizing culture conditions. With the development of modern biotechnology, genetic engineering, protein engineering, and metabolic engineering have been applied for lycopene production. Extraction from natural plants is the main way for lycopene production at present. Based on the molecular mechanism of lycopene accumulation, the production of lycopene by plant bioreactor through genetic engineering has a good prospect. Here we summarized common strategies for optimizing lycopene production engineering from a biotechnology perspective, which are mainly carried out by microbial cultivation. We reviewed the challenges and limitations of this approach, summarized the critical aspects, and provided suggestions with the aim of potential future breakthroughs for lycopene production in plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".