Global parameter estimation methods for stochastic biochemical systems.
Global parameter estimation methods for stochastic biochemical systems.
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DOI：
10.1186/1471-2105-11-414
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发表时间：
2010-08-06
影响因子：
3
通讯作者：
Gunawan R
中科院分区：
文献类型：
--
作者：
Poovathingal SK;Gunawan R
随机性在细胞过程中的重要性，有少量的分子，导致了随机模型的发展，如化学主方程。与其他建模框架一样，伴随的速率常数对于分析系统特性（例如鲁棒性）或预测遗传扰动的影响等最终应用非常重要。动力学常数的先验知识通常是有限的，模型识别程序通常包括从实验数据的参数估计。虽然确定性模型的参数估计问题已经很成熟，但对于化学主方程的参数估计问题还不是常规问题。此外，测量技术的最新进展使得将遗传底物定量到单分子水平成为可能。因此，这项工作的目的是开发实用和有效的方法来估计动力学模型参数的化学主方程和其他随机模型从单细胞和细胞群体的实验数据。提出了基于最大似然和密度函数距离的三种参数估计方法，包括概率和累积密度函数。由于诸如化学主方程的随机模型通常使用蒙特卡罗方法求解，其中只有有限数量的蒙特卡罗实现在计算上是可行的，因此给出具体考虑以考虑状态密度函数的直方图分箱中的有限采样的影响。三个实际案例研究的应用表明，虽然最大似然法可以有效地处理低重复测量，密度函数距离的方法，特别是累积密度函数距离估计，是更强大的参数估计一致的更高的精度，即使是多模态系统。在这项工作中所描述的参数估计方法提供了一个有效的和实用的方法在估计的动力学参数的随机系统，无论是稀疏或密集的细胞群体数据。然而，类似于其他建模框架中的动力学参数估计，并非所有参数都可以准确估计，这是由于缺乏从现有数据中获得的完整参数可识别性而引起的常见问题。
The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data. Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality. The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.
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影响因子：
1.7
作者：
Golightly, Andrew;Wilkinson, Darren J.
通讯作者：
Wilkinson, Darren J.
影响因子：
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作者：
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作者：
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影响因子：
3.3
作者：
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通讯作者：
GILLESPIE, DT