Metabolic engineering strategies for biomanufacturing of chemicals using Yarrowia lipolytica and Escherichia coli
Bibliographic record
Abstract
This dissertation advances metabolic engineering by optimizing the genetic and metabolic capabilities of Yarrowia lipolytica and Escherichia coli to enhance their applications in biotechnology. It focuses on improving Y. lipolytica's mannitol and amino acids production by varying fermentation temperatures and employing techniques like shake flask fermentation, HPLC, and NMR. Notably, mannitol production was enhanced through targeted modifications of FBP1 gene at elevated temperatures. RNAseq analyses highlighted shifts in metabolic pathways under thermal stress, markedly in lipid, sugar and amino acids metabolism. Additionally, a dual-gRNA CRISPR-Cas9 system was integrated within the pCRISPRYL2 plasmid, noticeably improving genetic editing precision by overcoming the constraints of the non-homologous end joining (NHEJ) pathway. Furthermore, the study pioneered a Cell-Free Metabolic Engineering (CFME) strategy to synthesize 5-Aminolevulinic Acid (5-ALA) utilizing optimized enzymatic reactions and operational conditions, presenting a scalable and eco-friendly alternative to conventional whole-cell systems. In parallel, engineered E. coli demonstrated robust heme production capabilities in both whole-cell and cell-free systems. Heme derivatives, including valuable pigments like biliverdin, Phycocyanobilin (PCB) and Phycoerythrobilin (PEB) were also produced at a 1L bioreactor scale utilizing E. coli engineered with unexplored enzymes. Overall, this work not only expands the scope of metabolic engineering but also sets a foundational work for future innovations in biomanufacturing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".