Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.
Bull Math Biol. 2012 May;74(5):1066-97. doi: 10.1007/s11538-011-9697-6. Epub 2011 Oct 14.
We develop a stochastic model for variable-length stepping of kinesins engineered with extended neck linkers. This requires that we consider the separation in microtubule binding sites between the heads of the motor at the beginning of a step. We show that this separation is stationary and can be included in the calculation of standard experimental quantities. We also develop a corresponding matrix computational framework for conducting computer experiments. Our matrix approach is more efficient computationally than large-scale Monte Carlo simulation. This efficiency greatly eases sensitivity analysis, an important feature when there is considerable uncertainty in the physical parameters of the system. We demonstrate the application and effectiveness of our approach by showing that the worm-like chain model for the neck linker can explain recently published experimental data. While we have focused on a particular scenario for kinesins, these methods could also be applied to myosin and other processive motors.
我们开发了一个用于具有扩展颈部接头的动力蛋白的可变长度步长的随机模型。这要求我们考虑在步长开始时马达头部的微管结合位点之间的分离。我们表明，这种分离是稳定的，可以包含在标准实验量的计算中。我们还为进行计算机实验开发了相应的矩阵计算框架。与大规模的蒙特卡罗模拟相比，我们的矩阵方法在计算上更加高效。当系统的物理参数存在相当大的不确定性时，这种效率极大地简化了敏感性分析，这是一个重要的特征。我们通过展示蠕虫状链模型对颈部接头可以解释最近发表的实验数据，证明了我们方法的应用和有效性。虽然我们专注于动力蛋白的特定情况，但这些方法也可以应用于肌球蛋白和其他进行性马达。
