Information-theoretic analysis of multivariate single-cell signaling responses.

ABSTRACT: Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI-statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimat

SUBMITTER: Jetka T 

PROVIDER: S-EPMC6655862 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature