Assessing transfer entropy from biochemical data
Assessing transfer entropy from biochemical data
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DOI：
10.1103/physreve.105.034403
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发表时间：
2022-03-08
影响因子：
2.4
通讯作者：
Kabashima，Yoshiyuki
中科院分区：
文献类型：
--
作者：
Imaizumi，Takuya;Umeki，Nobuhisa;Kabashima，Yoshiyuki
我们解决的问题，评估的转移熵（TE）从实验测量数据的生化反应。虽然这些反应通常是非线性和非平稳的过程，使其具有挑战性，以实现准确的建模，高斯近似可以促进TE评估，只有通过估计协方差矩阵使用多个数据获得的同时测量的时间序列代表生物分子，如蛋白质的活化水平。然而，生化信号的非平稳性质使得理论上难以评估TE的采样分布，这对于评估数据驱动估计的统计置信度和显著性是必要的。我们解决了这个困难，通过计算评估的抽样分布使用的技术，从计算统计。通过使用它们在分析从理论上易处理的时变信号模型，这导致了一种方法的发展，筛选只有统计上显着的估计所产生的数据的计算方法进行测试。所开发的方法的有用性进行检查，通过将其应用到真实的生物数据实验测量ERBB-RAS-MAPK系统，监督不同的细胞命运的决定。含有野生型和突变蛋白的细胞之间的比较在TE的时间演变中表现出明显的差异，而在原始信号的平均曲线中几乎没有发现任何明显的差异。这种比较可能有助于揭示生物化学反应的重要途径。
We address the problem of evaluating the transfer entropy (TE) produced by biochemical reactions from experimentally measured data. Although these reactions are generally nonlinear and nonstationary processes making it challenging to achieve accurate modeling, Gaussian approximation can facilitate the TE assessment only by estimating covariance matrices using multiple data obtained from simultaneously measured time series representing the activation levels of biomolecules such as proteins. Nevertheless, the nonstationary nature of biochemical signals makes it difficult to theoretically assess the sampling distributions of TE, which are necessary for evaluating the statistical confidence and significance of the data-driven estimates. We resolve this difficulty by computationally assessing the sampling distributions using techniques from computational statistics. The computational methods are tested by using them in analyzing data generated from a theoretically tractable time-varying signal model, which leads to the development of a method to screen only statistically significant estimates. The usefulness of the developed method is examined by applying it to real biological data experimentally measured from the ERBB-RAS-MAPK system that superintends diverse cell fate decisions. A comparison between cells containing wild-type and mutant proteins exhibits a distinct difference in the time evolution of TE while any apparent difference is hardly found in average profiles of the raw signals. Such a comparison may help in unveiling important pathways of biochemical reactions.