Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems.
Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems.
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DOI：
10.1093/bioinformatics/btx253
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发表时间：
2017-07-15
期刊：
影响因子：
--
通讯作者：
Flassig RJ
中科院分区：
文献类型：
--
作者：
Pischel D;Sundmacher K;Flassig RJ
生物细胞在受内在、外在和外部噪音影响的噪音环境中运行，这导致个体细胞状态的巨大差异。必须考虑随机效应才能准确表征生化动力学。由于大多数生化系统无法导出控制基本随机过程的化学主方程的精确解，因此使用近似方法来获得解。在本研究中，提出了一种同时有效模拟各种噪声源的方法，并在几个示例上进行了基准测试。该方法依靠西格玛点方法的组合来描述外部和外部变异性，并结合τ-跳跃算法来解释由概率反应引起的随机性。我们的方法与广泛的蒙特卡罗计算的比较表明了巨大的计算优势，同时损失了可接受的精度。此外，还展示了在随机生化反应网络中参数优化问题的应用，但由于其巨大的计算负担而很少应用。为了进一步深入了解，提供了一个 MATLAB 脚本，其中包括应用于基因表达的简单玩具示例的建议方法。 MATLAB 代码可在 Bioinformatics 在线获取。 补充数据可在生物信息学在线获取。
Biological cells operate in a noisy regime influenced by intrinsic, extrinsic and external noise, which leads to large differences of individual cell states. Stochastic effects must be taken into account to characterize biochemical kinetics accurately. Since the exact solution of the chemical master equation, which governs the underlying stochastic process, cannot be derived for most biochemical systems, approximate methods are used to obtain a solution. In this study, a method to efficiently simulate the various sources of noise simultaneously is proposed and benchmarked on several examples. The method relies on the combination of the sigma point approach to describe extrinsic and external variability and the τ-leaping algorithm to account for the stochasticity due to probabilistic reactions. The comparison of our method to extensive Monte Carlo calculations demonstrates an immense computational advantage while losing an acceptable amount of accuracy. Additionally, the application to parameter optimization problems in stochastic biochemical reaction networks is shown, which is rarely applied due to its huge computational burden. To give further insight, a MATLAB script is provided including the proposed method applied to a simple toy example of gene expression. MATLAB code is available at Bioinformatics online. Supplementary data are available at Bioinformatics online.
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