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Record W2129983905 · doi:10.1002/bit.25600

Temporal characterization of protein production levels from baculovirus vectors coding for GFP and RFP genes under non‐conventional promoter control

2015· article· en· W2129983905 on OpenAlexaff
Steve George, Altamash M. Jauhar, Jennifer MacKenzie, Sascha Kieβlich, Marc G. Aucoin

Bibliographic record

VenueBiotechnology and Bioengineering · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAutographa californicaGreen fluorescent proteinGeneBiologyGene expressionPromoterExpression vectorCell biologyComputational biologyMolecular biologyRecombinant DNAGeneticsSpodoptera

Abstract

fetched live from OpenAlex

The ease of use and versatility of the Baculovirus Expression Vector System (BEVS) has made it one of the most widely used systems for recombinant protein production However, co-expression systems currently in use mainly make use of the very strong very late p10 and polyhedron (polh) promoters to drive expression of foreign genes, which does not provide much scope for tailoring expression ratios within the cell. This work demonstrates the use of different Autographa californica multicapsid nucleopolyhedrovirus (AcMNPV) promoters to control the timing and expression of two easily traceable fluorescent proteins, the enhanced green fluorescent protein (eGFP), and a red fluorescent protein (DsRed2) in a BEVS co-expression system. Our results show that gene expression levels can easily be controlled using this strategy, and also that modulating the expression level of one protein can influence the level of expression of the other protein within the system, thus confirming the concept of genes "competing" for limited cellular resources. Plots of "expression ratios" of the two model genes over time were obtained, and may be used in future work to tightly control timing and levels of foreign gene expression in an insect cell co-expression system.

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 score0.046
Threshold uncertainty score0.414

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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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