MétaCan
Menu
Back to cohort
Record W4379376773 · doi:10.1021/acs.jafc.3c01356

Co-culture of <i>Kluyveromyces marxianus</i> and <i>Meyerozyma guilliermondii</i> with <i>In Situ</i> Product Recovery of 2-Phenylethanol

2023· article· en· W4379376773 on OpenAlexaff
Benedict Ryan Lukito, Nurhidayah Basri, Aaron Thong, Christian Hermansen, Melanie Weingarten, Eric Charles Peterson

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsInstitut National de la Recherche Scientifique
FundersAgency for Science, Technology and Research
KeywordsKluyveromyces marxianusBioproductionChemistryFermentationFood scienceYeastBiochemistrySaccharomyces cerevisiae

Abstract

fetched live from OpenAlex

Production of 2-phenylethanol (2-PE) via Kluyveromyces marxianus is well-established. However, co-culture with other microbes in combination with in situ product recovery (ISPR) yields improved selectivity and volumetric productivity. Fermentation of K. marxianus (MUCL 53775) with direct inclusion of absorptive polymer Hytrel3548 achieved ISPR, but accumulation of the byproduct phenylethyl acetate (PEA) was strongly favored. Co-culture of K. marxianus (MUCL 53775) with Meyerozyma guilliermondii (MUCL 28072) with ISPR limited PEA production, thereby improving the 2-PE selectivity from 13 to 90%, compared to a pure culture of K. marxianus (MUCL 53775) under similar conditions. This improved the volumetric productivity by 85% compared to 2-PE ISPR with a pure culture of K. marxianus . This is the first report of co-culture in a two-phase fermentation for 2-PE bioproduction and demonstrates that interactions between co-culture and ISPR techniques can modulate bioproduction between 2-PE and byproduct PEA, and this technique will be explored for other strain combinations and for other high-value molecules of interest.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.004
GPT teacher head0.183
Teacher spread0.179 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations13
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural and Food ChemistrySame topicMicrobial Metabolic Engineering and BioproductionFrench-language works237,207