Bioproduction of fumaric acid: an insight into microbial strain improvement strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fumaric acid (FA), a metabolic intermediate, has been identified as an important carbohydrate derived platform chemical. Currently, it is commercially sourced from petrochemicals by chemical conversion. The shift to biochemical synthesis has become essential for sustainable development and for the transition to a biobased economy from a petroleum-based economy. The main limitation is that the concentrations of FA achieved during bioproduction are lower than that from a chemical process. Moreover, the high cost associated with bioproduction necessitates a higher yield to improve the feasibility of the process. To this effect, genetic modification of microorganism can be considered as an important tool to improve FA yield. This review discusses various genetic modifications strategies that have been studied in order to improve FA production. These strategies include the development of recombinant strains of Rhizopus oryzae, Escherichia coli, Saccharomyces cerevisiae, and Torulopsis glabrata as well as their mutants. The transformed strains were able to accumulate fumaric acid at a higher concentration than the corresponding wild strains but the fumaric acid titers obtained were lower than that reported with native fumaric acid producing R. oryzae strains. Moreover, one plausible adoption of gene editing tools, such as Agrobacterium-mediated transformation (AMT), CRISPR CAS-9 and RNA interference (RNAi) mediated knockout and silencing, have been proposed in order to improve fumaric acid yield. Additionally, the introduction of the glyoxylate pathway in R. oryzae to improve fumaric acid yield as well as the biosynthesis of fumarate esters have been proposed to improve the economic feasibility of the bioprocess. The adoption of some of these genetic engineering strategies may be essential to enable the development of a feasible bioproduction process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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 it