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Record W2565177128 · doi:10.1515/ijcre-2015-0055

Statistical Medium Optimization for the Increased Production of Recombinant Phytase in the Fed-Batch Cultivation of <i>Pichia pastoris</i>

2015· article· en· W2565177128 on OpenAlexaff
Gabriel Potvin, Zhi Li, Zisheng Zhang

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPichia pastorisPhytaseChemistryRecombinant DNAComposition (language)BioreactorChromatographyPotassiumFood scienceBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Abstract Using a 20-run central composite design, standard Basal Salt Media (BSM) was optimized for the production of recombinant phytase by Pichia pastoris in bioreactor-based fed-batch cultivations using glucose as the carbon source. The phytase activity in the supernatant of the cultures at the end of 48-hour runs was modeled as a function of the medium composition, and this model was successfully validated. Using Inductively Coupled Plasma spectroscopy (ICP), residual elements were quantified in the cultivation broth at the end of each run to confirm that differences in final cell density and enzyme activity were not due to nutrient depletion, but indeed associated with the variations in medium composition. The optimized media contains significantly lower concentration of the potassium and magnesium sources (28.7 g/l KH 2 PO 4 and 4.8 g/l MgSO 4 ·7H 2 O respectively), as well as a reduced concentration of trace salts, and based on experimental results, significantly outperforms standard media in terms of recombinant product yields under the same conditions.

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)0.001
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 score0.051
Threshold uncertainty score0.120

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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