Buonfiglio Valentina, Zagli Niccolò, Pertici Irene, Lombardi Vincenzo, Bianco Pasquale, Fanelli Duccio
PhysioLab, University of Florence, Sesto Fiorentino (FI), Italy.
NORDITA, Stockholm University and KTH Royal Institute of Technology, Stockholm, Sweden.
J R Soc Interface. 2025 Apr;22(225):20250040. doi: 10.1098/rsif.2025.0040. Epub 2025 Apr 30.
A data fitting procedure is devised and thoroughly tested to provide self-consistent estimates of the relevant mechanokinetic parameters involved in a plausible scheme underpinning the output of an ensemble of myosin II molecular motors mimicking the contraction of skeletal muscle. The method builds on a stochastic model accounting for the force exerted by the motor ensemble operated both in the low and high force-generating regimes corresponding to different temperature ranges. The proposed interpretative framework is successfully challenged against simulated data, meant to mimic the experimental output of a one-dimensional synthetic nanomachine powered by pure muscle myosin isoforms.
设计并全面测试了一种数据拟合程序，以对相关的机械动力学参数进行自洽估计，这些参数涉及一个合理的机制，该机制支撑着模拟骨骼肌收缩的肌球蛋白II分子马达集合的输出。该方法基于一个随机模型，该模型考虑了在对应于不同温度范围的低力产生和高力产生状态下运行的马达集合所施加的力。所提出的解释框架成功地针对模拟数据进行了验证，这些模拟数据旨在模拟由纯肌肉肌球蛋白同工型驱动的一维合成纳米机器的实验输出。
