Using temporal correlations and full distributions to separate intrinsic and extrinsic fluctuations in biological systems.
Using temporal correlations and full distributions to separate intrinsic and extrinsic fluctuations in biological systems.
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DOI：
10.1103/physrevlett.109.248104
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发表时间：
2012-12-14
影响因子：
8.6
通讯作者：
Paulsson J
中科院分区：
文献类型：
--
作者：
Hilfinger A;Chen M;Paulsson J
随机生物动力学的研究通常将观察到的波动与理论预测的方差进行比较，有时在将系统的内在随机性与不断变化的环境的奴役影响分开之后。但是，已经表明，差异在不同机制之间的区别令人惊讶地差，而对于其他系统特性，没有方法可以严格地将环境影响与内在影响分开。在这里，我们应用随机环境中的广义随机漫步理论来推导分解时间序列和更高统计量的精确规则，而不仅仅是方差。我们展示了哪些性质和哪类系统的内在波动可以在不考虑外在随机性的情况下进行分析，反之亦然。我们推导了两种独立的实验方法来测量单独的噪声贡献，并展示了如何使用时间相关性中的附加信息来检测动力系统中的乘法效应。
Studies of stochastic biological dynamics typically compare observed fluctuations to theoretically predicted variances, sometimes after separating the intrinsic randomness of the system from the enslaving influence of changing environments. But variances have been shown to discriminate surprisingly poorly between alternative mechanisms, while for other system properties no approaches exist that rigorously disentangle environmental influences from intrinsic effects. Here we apply the theory of generalized random walks in random environments to derive exact rules for decomposing time series and higher statistics rather than just variances. We show for which properties and for which classes of systems intrinsic fluctuations can be analyzed without accounting for extrinsic stochasticity and vice versa. We derive two independent experimental methods to measure the separate noise contributions, and show how to use the additional information in temporal correlations to detect multiplicative effects in dynamical systems.