Stochastic multi-particle Brownian Dynamics simulation of biological ion channels: A Finite Element approach
Bibliographic record
Abstract
Biological ion channels are protein tubes that span the cell membrane. They provide a conduction pathway and regulate the flow of ions though the low dielectric membrane. Modeling the dynamics of these channels is crucial in understanding their functionality. This paper proposes a novel simulation framework for modeling ion channels that is based on Finite Element Method (FEM). By using FEM, this is the first framework to allow the use of multiple dielectric constants inside the channel thus providing a more realistic model of the channel. Due to the run-time complexity of the problem, lookup tables must be constructed in memory to store pre- calculated electric potential information. Because of the large number of elements involved in FEM and channel resolution requirements there is the potential for very large lookup tables leading to a performance "bottleneck". This paper discusses strategies for minimizing table size and shows that currently available personal computers are sufficient for attaining reasonable levels of accuracy. For the framework proposed, results show diminishing returns in accuracy with tables sized greater than 2.2 GB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".