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© Inria / Photo C. Morel
Quantitative biology: numbers and fluorescent cells. InBio team (Inria/Institut Pasteur)
Publication : PLoS computational biology

Estimating information in time-varying signals

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in PLoS computational biology - 03 Sep 2019

Cepeda-Humerez SA, Ruess J, Tkačik G

Link to Pubmed [PMID] – 31479447

PLoS Comput. Biol. 2019 09;15(9):e1007290

Across diverse biological systems-ranging from neural networks to intracellular signaling and genetic regulatory networks-the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks.

https://www.ncbi.nlm.nih.gov/pubmed/31479447