Microtubule patterning in the presence of moving motor proteins
Bibliographic record
Abstract
Cytoskeletal polymers such as microtubules (MTs) interact with motor proteins to form higher-order structures. In vitro experiments have shown that MT patterns such as asters, bundles, and vortices can form under the influence of a single type of dynamic motor protein. MTs also can form anti-parallel bundles, similar to bundles that form the mitotic spindle during cell division, under the influence of two types of moving motors with opposite directionality. Despite the importance of MT structures, their mechanism of formation is not yet understood. We develop an integro-partial differential equation model to describe the dynamic interactions between MTs and moving motor proteins. Our model takes into account motor protein speed, processivity, density, and directionality, as well as MT treadmilling and reorganization due to interactions with motors. Simulation results show that plus-end directed motor proteins can form vortex patterns at low motor density, while minus-end directed motor proteins form aster patterns at similar densities. Also, motor proteins with opposite directionality are able to organize MTs into anti-parallel bundles. Our model is able to provide a quantitative and qualitative description of MT patterning, providing insights into possible mechanisms of spindle formation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".