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CellDynaMo-stochastic reaction-diffusion-dynamics model: Application to search-and-capture process of mitotic spindle
Evgenii Kliuchnikov1, Artem Zhmurov2, Kenneth A Marx1
1Department of Chemistry, University of Massachusetts, Lowell, Massachusetts, United States of America.
Plos Computational Biology
|June 3, 2022
Summary
We developed a new computational model for cellular processes. This model accurately simulates microtubule-kinetochore interactions during cell division, optimizing spindle assembly accuracy.
Area of Science:
- Computational biology
- Cellular mechanics
- Biophysics
Background:
- Cellular mechanochemical processes require high-resolution simulation.
- Accurate modeling of molecular dynamics is crucial for understanding cell division.
Purpose of the Study:
- Introduce a Stochastic Reaction-Diffusion-Dynamics Model (SRDDM) for cellular simulations.
- Apply SRDDM to analyze mitotic spindle assembly and microtubule-kinetochore interactions.
Main Methods:
- Coupling reaction-diffusion master equation with Langevin Dynamics (LD) in the CellDynaMo package.
- 3D simulation of microtubule dynamics, kinetochore mechanics, and kinase reactions.
- Investigating Ndc80 linker kinetics and phosphorylation-dependent attachments.
Main Results:
- Identified an optimal microtubule-kinetochore detachment rate for accurate chromosome connections.
- Chromosome arms enhance accuracy by slowing chromosome movement.
- Thermal fluctuations improve kinetochore capture and spindle assembly accuracy.
Conclusions:
- SRDDM enables efficient simulation of complex cellular dynamics.
- Model provides insights into factors governing accurate mitotic spindle assembly.
- Findings highlight the importance of mechanical and chemical regulation in cell division.
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