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Record W2282267212

Biophysical properties of growing actin networks measured with atomic force microscopy

2008· article· en· W2282267212 on OpenAlexfundno aff
Sapun H. Parekh

Bibliographic record

VenueeScholarship (California Digital Library) · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Defense Science and Engineering GraduateAmerican Society for Engineering EducationNational Institutes of HealthNational Science Foundation
KeywordsActin remodelingCell biologyActin cytoskeletonActinActin remodeling of neuronsActin-binding proteinCytoskeletonPseudopodiaBiologyForminsChemistryBiophysicsCellBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

The dynamic actin cytoskeleton plays a key role in a number of cellular processes including motility and shape change. Composed of individual filaments polymerized from actin monomers, the actin cytoskeleton is organized into a branched and cross-linked dendritic network by a diverse set of actin binding proteins. Directed growth of dendritic actin networks by monomer addition, such as at the leading edge of a crawling cell, generates the mechanical forces necessary for deforming the membrane during cell motility, endocytosis, and phagocytosis. Dysfunctional actin network regulation is associated with metastatic cancers, immune system disorders, and bacterial infection and pathogenesis. \tSignificant biochemical work over the past four decades has culminated into the dendritic nucleation model for actin network growth. This model summarizes the role of the major actin binding proteins, and interactions among them, that form and maintain a growing, dendritic actin network in crawling cells. Though actin biochemistry has been well studied, the force-generating ability and mechanical properties of growing dendritic actin networks that produce dynamic cellular shape changes remain unclear. This dissertation presents development of a unique measurement system for the purpose of understanding the biophysics of dendritic actin network growth. An experimental platform was built around a custom differential atomic force microscope by adapting a method for reconstituting actin network growth from cell-free extract in vitro to measure network force production and mechanics. The results described here demonstrate that dendritic actin networks possess a built-in force feedback system that enables active remodeling to support increasing forces. In addition, these networks exhibit the ability to reversibly stress soften under large loads, thereby avoiding catastrophic failure and retaining their underlying network structure as a molecular scaffold. These results have implications for understanding how crawling cells navigate through the physical barriers of the extracellular matrix and connective tissue in vivo while feeling a wide range of compressive forces.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.197
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2008
Admission routes1
Has abstractyes

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