Precision cellular agriculture: The future role of recombinantly expressed protein as food
Bibliographic record
Abstract
Cellular agriculture is a rapidly emerging field, within which cultured meat has attracted the majority of media attention in recent years. An equally promising area of cellular agriculture, and one that has produced far more actual food ingredients that have been incorporated into commercially available products, is the use of cellular hosts to produce soluble proteins, herein referred to as precision cellular agriculture (PCAg). In PCAg, specific animal- or plant-sourced proteins are expressed recombinantly in unicellular hosts-the majority of which are yeast-and harvested for food use. The numerous advantages of PCAg over traditional agriculture, including a smaller carbon footprint and more consistent products, have led to extensive research on its utility. This review is the first to survey proteins currently being expressed using PCAg for food purposes. A growing number of viable expression hosts and recent advances for increased protein yields and process optimization have led to its application for producing milk, egg, and muscle proteins; plant hemoglobin; sweet-tasting plant proteins; and ice-binding proteins. Current knowledge gaps present research opportunities for optimizing expression hosts, tailoring posttranslational modifications, and expanding the scope of proteins produced. Considerations for the expansion of PCAg and its implications on food regulation, society, ethics, and the environment are also discussed. Considering the current trajectory of PCAg, food proteins from any biological source can likely be expressed recombinantly and used as purified food ingredients to create novel and tailored food products.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".