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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Sensitivity, robustness, and identifiability in stochastic chemical kinetics models
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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models

机译：随机化学动力学模型中的灵敏度，鲁棒性和可识别性

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摘要

We present a novel and simple method to numerically calculate Fisher information matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic chemical kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.
机译：我们提出了一种新颖而简单的方法，用于数值计算用于随机化学动力学模型的Fisher信息矩阵。线性噪声近似用于导出模型方程和似然函数，从而产生有效的计算算法。我们的方法将计算Fisher信息矩阵的问题简化为求解一组常微分方程。这是不需要随机蒙特卡罗模拟就可以为随机化学动力学模型计算Fisher信息的第一种方法。然后，该方法用于研究随机化学动力学模型中的灵敏度，鲁棒性和参数可识别性。我们表明，在随机模型和确定性模型之间以及在具有时间序列和时间点测量的随机模型之间存在显着差异。我们证明了这些差异是由于分子数量的变化，物种之间的相关性以及时间相关性所引起的，并表明了这种方法如何可以用于分析和设计在细胞水平上探测随机过程的实验。该算法已实现为Matlab软件包，可应要求从作者处获得。

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    Michat Komorowski; Maria J. Costa; David A. Rand; Michael P. H. Stumpf;

  • 作者单位

    Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, United Kingdom;

    Systems Biology Centre, University of Warwick, Coventry CV4 7AL, United Kingdom;

    Systems Biology Centre, University of Warwick, Coventry CV4 7AL, United Kingdom,Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom;

    Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, United Kingdom;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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