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Quantifying information accumulation encoded in the dynamics of biochemical signaling.


ABSTRACT: Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is, in part, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here, we develop a quantitative framework, based on inferred trajectory probabilities, to calculate the mutual information encoded in signaling dynamics while accounting for cell-cell variability. We use it to understand NF?B transcriptional dynamics in response to different immune threats, and reveal that some threats are distinguished faster than others. Our analyses also suggest specific temporal phases during which information distinguishing threats becomes available to immune response genes; one specific phase could be mapped to the functionality of the I?B? negative feedback circuit. The framework is generally applicable to single-cell time series measurements, and enables understanding how temporal regulatory codes transmit information over time.

SUBMITTER: Tang Y 

PROVIDER: S-EPMC7904837 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Quantifying information accumulation encoded in the dynamics of biochemical signaling.

Tang Ying Y   Adelaja Adewunmi A   Ye Felix X-F FX   Deeds Eric E   Wollman Roy R   Hoffmann Alexander A  

Nature communications 20210224 1


Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is, in part, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here, we develop a quantitative framework, based on inferred trajectory probabilities, to calculate the mutu  ...[more]

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