Modeling Microtubule Dynamics on Lomonosov-2 Supercomputer of Moscow State University: from Atomistic to Cellular Scale Simulations
Bibliographic record
Abstract
Cytoskeletal polymers of tubulin, the microtubules, are critically important for cellular physiology. Their remarkable non-equilibrium dynamics and unusual mechanical properties have nurtured interest in exploring microtubules with diverse experimental methods and modeling their properties at different scales. In this work, we overview the studies of microtubules from the atomistic level of detail to the cellular dimension, focusing on the computational modeling work that has been carried out by our group on Lomonosov-2 supercomputer of Moscow State University since 2015. Our computational efforts have been aimed at understanding of microtubules through a set of models at multiple spatial and temporal scales, starting from examining the properties of tubulin dimers, as the building blocks, and further elucidating how those properties enable more complex assembly/disassembly and force-generation behaviors of microtubules, emerging at larger scales. Our methodology includes different approaches, from atomistic molecular dynamics to more coarse-grained techniques, such as Brownian dynamics and Monte Carlo simulations. We describe the motivation and the context for each model, overview the major conclusions from the simulations, which we believe were instrumental in building an integrative understanding of these polymers. We also discuss some technical aspects of the modeling, such as the computational performance of different types of simulations, current limitations and potential future directions for description of the microtubule dynamics, using the multi-scale approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".