脉动力作用下单根RNA时间序列的贝叶斯分析
Bayesian analysis of time series of single RNA under fluctuating force
摘要：
从单分子动力学数据中提取生物分子的内在动力学信息具有重要的生物物理意义。在这项工作中，我们从理论上探讨了从光镊中进行的强制折叠/展开实验获得的时间序列推断单个RNA的内在动力学参数的可行性，其中分子两侧是长的双链DNA/RNA手柄，并被拴在两个大珠子之间。我们首先构建了一个粗粒度的实验系统的物理模型。该模型捕捉了主要的物理因素：布朗运动的珠，分子结构的转变，和手柄和RNA的弹性。然后，基于该模型的解析解，提出了一种基于蒙特卡罗马尔可夫链的贝叶斯方法，从距离或力的噪声时间序列中推断RNA的内在动力学参数。由于微珠的布朗运动和结构转变所引起的力的涨落对RNA的跃迁速率有显著的调节作用，因此我们证明了这种统计方法比传统的直方图拟合方法更能准确、有效地推断分子的内禀参数.
英文摘要：
Extracting the intrinsic kinetic information of biological molecule from its single-molecule kinetic data is of considerable biophysical interest. In this work, we theoretically investigate the feasibility of inferring single RNA's intrinsic kinetic parameters from the time series obtained by forced folding/unfolding experiment done in the light tweezer, where the molecule is flanked by long double-stranded DNA/RNA handles and tethered between two big beads. We first construct a coarse-grain physical model of the experimental system. The model has captured the major physical factors: the Brownian motion of the bead, the molecular structural transition, and the elasticity of the handles and RNA. Then based on an analytic solution of the model, a Bayesian method using Monte Carlo Markov Chain is proposed to infer the intrinsic kinetic parameters of the RNA from the noisy time series of the distance or force. Because the force fluctuation induced by the Brownian motion of the bead and the structural transition can significantly modulate the transition rates of the RNA, we prove that, this statistic method is more accurate and efficient than the conventional histogram fitting method in inferring the molecule's intrinsic parameters.
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