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Record W2323006690 · doi:10.1091/mbc.e13-10-0594

Dynamic instability 30 years later: complexities in microtubule growth and catastrophe

2015· review· en· W2323006690 on OpenAlexafffund
Gary J. Brouhard

Bibliographic record

VenueMolecular Biology of the Cell · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsMicrotubuleTubulinBiologyInstabilityGTP'ActinBiophysicsCell biologyBiochemistryPhysicsMechanicsEnzyme

Abstract

fetched live from OpenAlex

Microtubules are not like other polymers. Whereas polymers such as F-actin will grow continuously as long as the subunit concentration is high enough, a steadily growing microtubule can suddenly shrink even when there is ample αβ-tubulin around. This remarkable behavior was discovered in 1984 when Tim Mitchison and Marc Kirschner deduced that microtubules switch from growth to shrinkage when they lose their GTP caps. Here, I review the canonical explanation of dynamic instability that was fleshed out in the years after its discovery. Many aspects of this explanation have been recently subverted, particularly those related to how GTP-tubulin forms polymers and why GTP hydrolysis disrupts them. I describe these developments and speculate on how our explanation of dynamic instability can be changed to accommodate them.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.013
GPT teacher head0.270
Teacher spread0.258 · 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
GenreReview

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

Citations129
Published2015
Admission routes2
Has abstractyes

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