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Record W1999881927 · doi:10.1002/bmb.20578

Using biocatalysis to integrate organic chemistry into a molecular biology laboratory course

2012· article· en· W1999881927 on OpenAlexaff
Mande Beers, Crystal R. Archer, Brent D. Feske, Scott C. Mateer

Bibliographic record

VenueBiochemistry and Molecular Biology Education · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsTellabs (Canada)
FundersDivision of ChemistryUniversity of FloridaNational Science Foundation
KeywordsSynthetic biologyChemical biologyBiocatalysisComputational biologyBiochemical engineeringChemistryBiologyNanotechnologyEngineering ethicsBiochemistryEngineeringMaterials science

Abstract

fetched live from OpenAlex

Current cutting-edge biomedical investigation requires that the researcher have an operational understanding of several diverse disciplines. Biocatalysis is a field of science that operates at the crossroads of organic chemistry, biochemistry, microbiology, and molecular biology, and provides an excellent model for interdisciplinary research. We have developed an inquiry-based module that uses the mutagenesis of the yeast reductase, YDL124w, to study the bioorganic synthesis of the taxol side-chain, a pharmacologically important molecule. Using related structures, students identify regions they think will affect enzyme stereoselective, design and generate site-specific mutants, and then characterize the effect of these changes on enzyme activity. This laboratory activity gives our students experience, working in a scientific discipline outside of biology and exposes them to techniques and equipment they do not normally work with in a molecular biology course. These inter-disciplinary experiences not only show the relevance of other sciences to biology, but also give our students the ability to communicate more effectively with scientists outside their discipline.

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 categoriesMeta-epidemiology (narrow)
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 score0.035
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.006
GPT teacher head0.301
Teacher spread0.295 · 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.

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
Published2012
Admission routes1
Has abstractyes

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