细胞过程间方向性影响的信息论分析
Information-theoretic analysis of the directional influence between cellular processes
摘要：
推断细胞过程之间相互作用的方向性是系统生物学的一个主要挑战。时滞相关性允许区分替代模型，但它们仍然依赖于假设的潜在相互作用。在这里，我们使用传递熵（TE），一个信息理论的量，以无模型的方式量化波动变量之间的方向影响。我们提出了一种理论方法来计算的转移熵，即使当噪声有一个外在的组件或存在反馈。我们重新分析了Kiviet等人（2014）的实验数据，其中在大肠杆菌单细胞中测量了代谢酶的基因表达和生长速率的波动。杆菌我们确认以前检测到的模式之间的增长和基因表达，同时规定更严格的条件上的结构的噪声源。我们还指出了实际的要求，必须满足的时间序列的长度和采样时间，以推断最佳的时间序列的波动传递熵。
英文摘要：
Inferring the directionality of interactions between cellular processes is a major challenge in systems biology. Time-lagged correlations allow to discriminate between alternative models, but they still rely on assumed underlying interactions. Here, we use the transfer entropy (TE), an information-theoretic quantity that quantifies the directional influence between fluctuating variables in a model-free way. We present a theoretical approach to compute the transfer entropy, even when the noise has an extrinsic component or in the presence of feedback. We re-analyze the experimental data from Kiviet et al. (2014) where fluctuations in gene expression of metabolic enzymes and growth rate have been measured in single cells of E. coli. We confirm the formerly detected modes between growth and gene expression, while prescribing more stringent conditions on the structure of noise sources. We furthermore point out practical requirements in terms of length of time series and sampling time which must be satisfied in order to infer optimally transfer entropy from times series of fluctuations.
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