Multi-omics approaches to unravel regulatory dynamics in yeast bioreactor cultivations
Bibliographic record
Abstract
Climate change is a multifaceted problem that requires multiple scientific discoveries and engineering innovations. Among the innovations that have emerged in recent years are microbial cell factories, engineered microorganisms that produce desired molecules through their metabolism. A promising microbial cell factory is Yarrowia lipolytica, an oleaginous yeast that has gained significant traction since it proved a versatile host to produce lipids as well as both bulk and fine chemicals. However, further research is needed to better understand this host and to design better bioprocesses.To improve the current understanding of Y. lipolytica as a microbial cell factory, I combined chemostat cultivations with transcriptomic analysis. I studied the underlying biology of a platform strain with disrupted lipid synthesis, revealing that abolishing storage lipids induces protein misfolding and stress responses. I then explored the use of urea as an alternative and more sustainable nitrogen source, demonstrating that it does not alter the cell transcriptome and can reduce media acidification. I combined this information to improve a fed-batch cultivation to produce high titres of itaconic acid. Meanwhile, I laid the foundations for single-cell transcriptomics to explore cell heterogeneity in bioreactor cultivations. I performed a proof-of-concept analysis in the well‑characterized yeast Saccharomyces cerevisiae to understand the potential challenges in translating single‑cell transcriptomics to Y. lipolytica. I found that cell cycle genes are a major source of variability that needs to be minimized. The work performed combines bioreactor cultivation with omics analyses to inform and guide future strain improvement. Overall, this thesis explores and expands knowledge in relevant areas to develop Y. lipolytica as a microbial cell factory for the sustainable production of non‑lipid chemicals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".