Koster Daniel A, Wiggins Chris H, Dekker Nynke H
Kavli Institute of Nanoscience, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands.
Proc Natl Acad Sci U S A. 2006 Feb 7;103(6):1750-5. doi: 10.1073/pnas.0510509103. Epub 2006 Jan 26.
Most analyses of single-molecule experiments consist of binning experimental outcomes into a histogram and finding the parameters that optimize the fit of this histogram to a given data model. Here we show that such an approach can introduce biases in the estimation of the parameters, thus great care must be taken in the estimation of model parameters from the experimental data. The bias can be particularly large when the observations themselves are not statistically independent and are subjected to global constraints, as, for example, when the iterated steps of a motor protein acting on a single molecule must not exceed the total molecule length. We have developed a maximum-likelihood analysis, respecting the experimental constraints, which allows for a robust and unbiased estimation of the parameters, even when the bias well exceeds 100%. We demonstrate the potential of the method for a number of single-molecule experiments, focusing on the removal of DNA supercoils by topoisomerase IB, and validate the method by numerical simulation of the experiment.
大多数单分子实验分析包括将实验结果整理成直方图，并找出能使该直方图与给定数据模型最佳拟合的参数。在这里，我们表明这种方法可能会在参数估计中引入偏差，因此在从实验数据估计模型参数时必须格外小心。当观测值本身并非统计独立且受到全局约束时，偏差可能会特别大，例如，当作用于单个分子的运动蛋白的迭代步长不能超过分子总长度时。我们开发了一种考虑实验约束的最大似然分析方法，即使偏差远超100%，该方法也能对参数进行稳健且无偏的估计。我们通过多个单分子实验展示了该方法的潜力，重点是拓扑异构酶IB去除DNA超螺旋的实验，并通过对该实验的数值模拟验证了该方法。
