Baker A, Bechhoefer J
Department of Physics, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Mar;89(3):032703. doi: 10.1103/PhysRevE.89.032703. Epub 2014 Mar 6.
We generalize a stochastic model of DNA replication to the case where replication-origin-initiation rates vary locally along the genome and with time. Using this generalized model, we address the inverse problem of inferring initiation rates from experimental data concerning replication in cell populations. Previous work based on curve fitting depended on arbitrarily chosen functional forms for the initiation rate, with free parameters that were constrained by the data. We introduce a nonparametric method of inference that is based on Gaussian process regression. The method replaces specific assumptions about the functional form of the initiation rate with more general prior expectations about the smoothness of variation of this rate, along the genome and in time. Using this inference method, we recover, with high precision, simulated replication schemes from noisy data that are typical of current experiments.
我们将DNA复制的随机模型推广到复制起始速率沿基因组局部且随时间变化的情况。利用这个广义模型，我们解决了从细胞群体中复制的实验数据推断起始速率的反问题。以前基于曲线拟合的工作依赖于为起始速率任意选择的函数形式，其自由参数由数据约束。我们引入了一种基于高斯过程回归的非参数推断方法。该方法用关于该速率沿基因组和随时间变化的平滑性的更一般先验期望，取代了关于起始速率函数形式的特定假设。使用这种推断方法，我们从当前实验典型的噪声数据中高精度地恢复了模拟复制方案。
