复杂生物系统随机计算模型中罕见事件预测的多保真度分析。

IF 2.3 Q3 ENGINEERING, BIOMEDICAL
Biomedical Engineering and Computational Biology Pub Date : 2018-08-03 eCollection Date: 2018-01-01 DOI:10.1177/1179597218790253
Elsje Pienaar
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引用次数: 0

摘要

基因突变或细胞-细胞相互作用等罕见事件是复杂生物系统(如耐药感染)动力学的重要贡献者。计算方法可以帮助分析难以通过实验研究的罕见事件。然而,分析计算模型中罕见事件的频率和动态也可能具有挑战性,因为对计算资源的需求很高,特别是对于高保真度的随机计算模型。为了便于分析复杂生物系统中的罕见事件,我们提出了一种多保真度分析方法,该方法使用中保真度分析(蒙特卡罗模拟)和/或低保真度分析(马尔可夫链模型)来分析高保真度随机模型结果。中等保真度分析可以为单个高保真度模型模拟产生大量可能的罕见事件轨迹。这允许在比高保真模型低得多的频率下预测罕见事件动态和概率分布。低保真度分析可以通过在高保真度模型的每个离散事件之后更新罕见事件状态空间的概率来计算任意频率下罕见事件随时间的概率分布。为了验证该方法,我们将多保真度分析应用于结核病的高保真度模型。我们根据高保真度模型结果验证了该方法,并说明了多保真度分析在预测罕见事件轨迹,执行灵敏度分析和外推预测到复杂系统中非常低频率方面的应用。我们相信,我们的方法将补充正在进行的努力,使高保真计算模型中罕见事件动力学的准确预测成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems.

Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems.

Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems.

Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems.

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.

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