Polymer characterization and optimization of conditions for the enhanced bioproduction of benzaldehyde by <i>Pichia pastoris</i> in a two‐ ;phase partitioning bioreactor
Bibliographic record
Abstract
Benzaldehyde, with its apricot and almond-like aroma, is the second most abundantly used molecule in the flavor industry, and is most commonly produced via chemical routes, such as by the oxidation of toluene. Biologically produced benzaldehyde, whether by extraction of plant material or via microbial biotransformation, commands a substantial price advantage, and greater consumer acceptance. Methylotrophic yeast, such as Pichia pastoris, contain the enzyme alcohol oxidase (AOX), which, in the presence of alcohols other than methanol, are able to yield aldehydes as dead-end products, for example, benzaldehyde from benzyl alcohol. In this work, we have determined that benzaldehyde, and not benzyl alcohol, is inhibitory to the transformation reaction by P. pastoris, prompting the development of a selection strategy for identifying sequestering polymers for use in a partitioning bioreactor that was based on the ratio of partition coefficients (PCs) for the two target molecules. Additionally, we have now confirmed for the first time, that the mechanism of solute uptake by amorphous polymers is via absorption, not adsorption. Finally, we have adopted a common strategy used for the production of heterologous proteins by P. pastoris, namely the use of a mixed methanol/glycerol feed for inducing the required AOX enzyme, while reducing the time required for high density biomass generation. All of these components were combined in a final experiment in which 10% of the polymer Kraton D1102K, whose PC ratio of benzaldehyde to benzyl alcohol was 14.9, was used to detoxify the biotransformation in a 5 L partitioning bioreactor, resulting in a 3.4-fold increase in benzaldehyde produced (14.4 g vs. 4.2 g) relative to single phase operation, at more than double the volumetric productivity (97 mg L(-1) h(-1) vs. 41 mg L(-1) h(-1) ).
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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".