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Record W2897892189 · doi:10.1002/9781119248002.ch5

Cellular versus Biochemical Control over Microbial Products

2018· other· en· W2897892189 on OpenAlexaff
Carlos S. Osorio‐González, Krishnamoorthy Hegde, Satinder Kaur Brar

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsInstitut National de la Recherche Scientifique
FundersU.S. Department of Energy
KeywordsAldehyde dehydrogenaseEscherichia coliBiochemistryFumaric acidMetabolic engineeringEnzymeMetaboliteChemistryGlycerolItaconic acidIndustrial microbiologyDehydrataseMetabolic pathwayBiologyFermentationOrganic chemistryGene

Abstract

fetched live from OpenAlex

The development of metabolic and genetic engineering promotes the progress in obtaining modified microbial strains for the improvement of the processes and an increase in the yields of the final products through cellular and biochemical control. This chapter summarizes the application and cellular and biochemical control of Escherichia coli strains for efficient production of an array of platform chemicals. However, E. coli cannot naturally metabolize glycerol to 3-hydroxy-propionic acid as it lacks the enzyme glycerol dehydratase (dhaB) and the negligible expression of aldehyde dehydrogenase (aldH) as both are key enzymes to obtaining this metabolite. The chapter demonstrates the possibility of efficient production of fumaric acid with genetically modified strains of E. coli. In general, the process of production of itaconic acid from fungi is relatively long. Conventional production of glucaric acid from glucose is associated with low yields and the production of toxic byproducts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.003

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.

Opus teacher head0.005
GPT teacher head0.205
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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