Assessing transfer entropy from biochemical data

Abstract

We address the problem of evaluating the transfer entropy (TE) produced by biochemical reactions from experimentally measured data. Although these reactions are generally non-linear and non-stationary 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 non-stationary 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 apparent difference is hardly found in average profiles of the raw signals. Such comparison may help in unveiling important pathways of biochemical reactions.

arXiv comment30 pages, 11 figures

References in corpus (9)

• Novel type of phase transition in a system of self-driven particles
• Measuring Information Transfer
• Granger causality and transfer entropy are equivalent for Gaussian variables
• Kernel method for nonlinear Granger causality
• Model-free reconstruction of neuronal network connectivity from calcium imaging signals
• Causation Entropy Identifies Indirect Influences, Dominance of Neighbors and Anticipatory Couplings
• Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
• Expanding the Transfer Entropy to Identify Information Subgraphs in Complex Systems
• Understanding causation via correlations and linear response theory