Computational analysis into the potential of azo dyes as a feedstock for actinorhodin biosynthesis in Pseudomonas putida
Bibliographic record
Abstract
Fermentation-based biosynthesis in synthetic biology relies heavily on sugar-derived feedstocks, a limited and carbon-intensive commodity. Unconventional feedstocks from less-noble sources such as waste are being utilized to produce high-value chemical products. Azo dyes, a major pollutant commonly discharged by food, textile, and pharmaceutical industries, present significant health and environmental risks. We explore the potential of engineering Pseudomonas putida KT2440 to utilize azo dyes as a substrate to produce a polyketide, actinorhodin (ACT). Using the constrained minimal cut sets (cMCS) approach, we identified metabolic interventions that optimize ACT biosynthesis and compare the growth-coupling solutions attainable on an azo dye compared to glucose. Our results predicted that azo dyes could perform better as a feedstock for ACT biosynthesis than glucose as it allowed growth-coupling regimes that are unfeasible with glucose and generated an 18.28% higher maximum ACT flux. By examining the flux distributions enabled in different carbon sources, we observed that carbon fluxes from aromatic compounds like azo dyes have a unique capability to leverage gluconeogenesis to support both growth and production of secondary metabolites that produce excess NADH. Carbon sources are commonly chosen based on the host organism, availability, cost, and environmental implications. We demonstrated that careful selection of carbon sources is also crucial to ensure that the resulting flux distribution is suitable for further metabolic engineering of microbial cell factories.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".