Quasi-Newton Stochastic Optimization Algorithm for Parameter Estimation of a Stochastic Model of the Budding Yeast Cell Cycle
Quasi-Newton Stochastic Optimization Algorithm for Parameter Estimation of a Stochastic Model of the Budding Yeast Cell Cycle
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DOI：
10.1109/tcbb.2017.2773083
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发表时间：
2019-01-01
影响因子：
4.5
通讯作者：
Kakoti，Gisella
中科院分区：
文献类型：
--
作者：
Chen，Minghan;Amos，Brandon D.;Kakoti，Gisella
由于多种原因，离散或连续确定性细胞周期模型中的参数估计具有挑战性，包括可观察到的内容的性质以及这些观察的准确性和数量。对于随机模型来说，挑战甚至更大，其中模拟次数和经验数据量必须更大才能获得统计上有效的参数估计。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，以及（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，而不是创建集成统计数据。这里使用 QNSTOP 直接匹配经验和模拟的联合概率分布，而不是匹配汇总统计数据。给出了当前最先进的芽殖酵母随机细胞周期模型的结果，其预测与经验数据的一些汇总统计数据和一维分布很好地匹配，但与经验联合分布不太匹配。不匹配的本质使我们能够深入了解随机模型的弱点。
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.