Understanding the emerging potential of synthetic biology for food science: Achievements, applications and safety considerations
Bibliographic record
Abstract
The advent of the first synthetic cell has triggered significant interest in synthetic biology research and its potential impact on various fields, including industry, agriculture, health, and the environment. With its unique blend of molecular biology, genetics, engineering, and computational modelling, synthetic biology is becoming a crucial tool in addressing challenges in food science. For example, the development of synthetic genetic circuits is underway for the improvement of food safety and quality, such as creating biosensors for detecting toxins and heavy metals, enhancing the nutritional value of foods, and preserving the environment using sustainable technologies. Regardless of its potential, the use of synthetic biology in food production raises concerns regarding biosafety, asking for stringent control and comprehensive legislation to prevent misuse of the technology. This review provides an overview of synthetic biology in food science, discussing the current state-of-art of its toolbox achievements for cost-effective and nutritional improvement, likewise, the legal, ethical, and biosecurity considerations surrounding the use of synthetic circuits for the advancement of food science.
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 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) | 0.000 | 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".