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Record W6243263 · doi:10.5555/1999416.1999480

Applying the TPS method to modeling and simulation of biological systems

2010· article· en· W6243263 on OpenAlexaff
Sanaa Lissari, Nada Farran, Hamel Yigang, Rhys Goldstein, Gabriel Wainer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsImpulse (physics)VisualizationBiological systemParticle systemParticle (ecology)Computer scienceImmersed boundary methodFinite element methodMechanicsPhysicsBoundary (topology)Computer graphics (images)Classical mechanicsArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Deformable biological structures are typically modeled with the finite element method, but we have designed a much simpler impulse-based method called the "tethered particle system " (TPS). The TPS involves the use of discrete-event simulation to track the positions of a large number of particles. Two of these particles approaching to each other may collide and rebound off. The difference is that, provided they are "tethered", two separating particles may also collide and retract inwards. This constrains the distances between pairs of particles, allowing various deformable structures to be represented. In this article, we present the application of the TPS method to the simulation of different biological structures found in nerve cells: actin filaments, cell membranes and synapsin protein that bind with actin, mitochondria, and water particles which influence the motion of other structures. This includes advanced visualization methods. 1.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.153

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 head0.026
GPT teacher head0.300
Teacher spread0.275 · 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 designSimulation or modeling
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
Published2010
Admission routes1
Has abstractyes

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