Engineered yeasts and lignocellulosic biomaterials: shaping a new dimension for biorefinery and global bioeconomy
Bibliographic record
Abstract
The next milestone of synthetic biology research relies on the development of customized microbes for specific industrial purposes. Metabolic pathways of an organism, for example, depict its chemical repertoire and its genetic makeup. If genes controlling such pathways can be identified, scientists can decide to enhance or rewrite them for different purposes depending on the organism and the desired metabolites. The lignocellulosic biorefinery has achieved good progress over the past few years with potential impact on global bioeconomy. This principle aims to produce different bio-based products like biochemical(s) or biofuel(s) from plant biomass under microbial actions. Meanwhile, yeasts have proven very useful for different biotechnological applications. Hence, their potentials in genetic/metabolic engineering can be fully explored for lignocellulosic biorefineries. For instance, the secretion of enzymes above the natural limit (aided by genetic engineering) would speed-up the down-line processes in lignocellulosic biorefineries and the cost. Thus, the next milestone would greatly require the development of synthetic yeasts with much more efficient metabolic capacities to achieve basic requirements for particular biorefinery. This review gave comprehensive overview of lignocellulosic biomaterials and their importance in bioeconomy. Many researchers have demonstrated the engineering of several ligninolytic enzymes in heterologous yeast hosts. However, there are still many factors needing to be well understood like the secretion time, titter value, thermal stability, pH tolerance, and reactivity of the recombinant enzymes. Here, we give a detailed account of the potentials of engineered yeasts being discussed, as well as the constraints associated with their development and applications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".