Diatoms’ Breakthroughs in Biotechnology: <i>Phaeodactylum tricornutum</i> as a Model for Producing High-Added Value Molecules
Bibliographic record
Abstract
With a world growing in population and nutritional needs, diatoms are considered nowadays as microalgae of a very important potential, thus they are exploited in several fields such as ecology, aquaculture, molecular farming, and pharma nutraceuticals. These coveted microalgae are characterized by their diversity, their high division rates, their complex life cycle, likewise their silicified cell walls named frustules. Thus, diatoms have been used for over a century after proving an efficient production of several molecules including Triacylglycerols (TAGs), H2, free fatty acids, vitamins, nutraceuticals, amino acids, proteins, terpenoids, alcohols and carbohydrates like starch, glycogen, and sucrose. Phaeodactylum tricornutum is the most promising diatom exploited to date, especially as a platform of pharmaceutical production. Herein, we expose diatoms’ main features that allowed using them for molecular farming. This review exposes likewise, the metabolism and the post-translational modifications (PTMs) of diatoms as well as current tools and challenges for their molecular and metabolic engineering for a more efficient production of valuable molecules. The knowledge on the biology of the diatoms, the molecular tools, and the various transformation methods available demonstrate the potential in biotechnology of these photosynthetic microorganisms. The widely studied P. tricornutum, as a model organism, is a promising diatom for production of valuable metabolites, despite the challenges and issues related to cultivation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".