IEEE/ACM Trans Comput Biol Bioinform. 2019 Jan-Feb;16(1):301-311. doi: 10.1109/TCBB.2017.2773083. Epub 2017 Nov 14.
Parameter estimation in discrete or continuous deterministic cell cycle models is challenging for several reasons, including the nature of what can be observed, and the accuracy and quantity of those observations. The challenge is even greater for stochastic models, where the number of simulations and amount of empirical data must be even larger to obtain statistically valid parameter estimates. The two main contributions of this work are (1) stochastic model parameter estimation based on directly matching multivariate probability distributions, and (2) a new quasi-Newton algorithm class QNSTOP for stochastic optimization problems. QNSTOP directly uses the random objective function value samples rather than creating ensemble statistics. QNSTOP is used here to directly match empirical and simulated joint probability distributions rather than matching summary statistics. Results are given for a current state-of-the-art stochastic cell cycle model of budding yeast, whose predictions match well some summary statistics and one-dimensional distributions from empirical data, but do not match well the empirical joint distributions. The nature of the mismatch provides insight into the weakness in the stochastic model.
离散或连续确定性细胞周期模型中的参数估计由于多种原因具有挑战性，包括可观察到的事物的性质，以及这些观察的准确性和数量。对于随机模型来说，挑战更大，因为必须进行更多的模拟和更多的经验数据，才能获得具有统计学意义的有效参数估计。这项工作的两个主要贡献是：（1）基于直接匹配多元概率分布的随机模型参数估计，（2）用于随机优化问题的新拟牛顿算法类 QNSTOP。QNSTOP 直接使用随机目标函数值样本，而不是创建集合统计信息。QNSTOP 在这里用于直接匹配经验和模拟联合概率分布，而不是匹配汇总统计信息。结果给出了当前最先进的芽殖酵母随机细胞周期模型，其预测与一些汇总统计信息和来自经验数据的一维分布很好地匹配，但与经验联合分布不太匹配。不匹配的性质提供了对随机模型弱点的深入了解。
