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Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades

The transcriptional network determines a cell’s internal state by regulating protein expression in response to changes in the local environment. Due to the interconnected nature of this network, information encoded in the abundance of various proteins will often propagate across chains of noisy inte...

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Autores principales: Rowland, Michael A., Pilkiewicz, Kevin R., Mayo, Michael L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806174/
https://www.ncbi.nlm.nih.gov/pubmed/33439904
http://dx.doi.org/10.1371/journal.pone.0245094
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author Rowland, Michael A.
Pilkiewicz, Kevin R.
Mayo, Michael L.
author_facet Rowland, Michael A.
Pilkiewicz, Kevin R.
Mayo, Michael L.
author_sort Rowland, Michael A.
collection PubMed
description The transcriptional network determines a cell’s internal state by regulating protein expression in response to changes in the local environment. Due to the interconnected nature of this network, information encoded in the abundance of various proteins will often propagate across chains of noisy intermediate signaling events. The data-processing inequality (DPI) leads us to expect that this intracellular game of “telephone” should degrade this type of signal, with longer chains losing successively more information to noise. However, a previous modeling effort predicted that because the steps of these signaling cascades do not truly represent independent stages of data processing, the limits of the DPI could seemingly be surpassed, and the amount of transmitted information could actually increase with chain length. What that work did not examine was whether this regime of growing information transmission was attainable by a signaling system constrained by the mechanistic details of more complex protein-binding kinetics. Here we address this knowledge gap through the lens of information theory by examining a model that explicitly accounts for the binding of each transcription factor to DNA. We analyze this model by comparing stochastic simulations of the fully nonlinear kinetics to simulations constrained by the linear response approximations that displayed a regime of growing information. Our simulations show that even when molecular binding is considered, there remains a regime wherein the transmitted information can grow with cascade length, but ends after a critical number of links determined by the kinetic parameter values. This inflection point marks where correlations decay in response to an oversaturation of binding sites, screening informative transcription factor fluctuations from further propagation down the chain where they eventually become indistinguishable from the surrounding levels of noise.
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spelling pubmed-78061742021-01-25 Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades Rowland, Michael A. Pilkiewicz, Kevin R. Mayo, Michael L. PLoS One Research Article The transcriptional network determines a cell’s internal state by regulating protein expression in response to changes in the local environment. Due to the interconnected nature of this network, information encoded in the abundance of various proteins will often propagate across chains of noisy intermediate signaling events. The data-processing inequality (DPI) leads us to expect that this intracellular game of “telephone” should degrade this type of signal, with longer chains losing successively more information to noise. However, a previous modeling effort predicted that because the steps of these signaling cascades do not truly represent independent stages of data processing, the limits of the DPI could seemingly be surpassed, and the amount of transmitted information could actually increase with chain length. What that work did not examine was whether this regime of growing information transmission was attainable by a signaling system constrained by the mechanistic details of more complex protein-binding kinetics. Here we address this knowledge gap through the lens of information theory by examining a model that explicitly accounts for the binding of each transcription factor to DNA. We analyze this model by comparing stochastic simulations of the fully nonlinear kinetics to simulations constrained by the linear response approximations that displayed a regime of growing information. Our simulations show that even when molecular binding is considered, there remains a regime wherein the transmitted information can grow with cascade length, but ends after a critical number of links determined by the kinetic parameter values. This inflection point marks where correlations decay in response to an oversaturation of binding sites, screening informative transcription factor fluctuations from further propagation down the chain where they eventually become indistinguishable from the surrounding levels of noise. Public Library of Science 2021-01-13 /pmc/articles/PMC7806174/ /pubmed/33439904 http://dx.doi.org/10.1371/journal.pone.0245094 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Rowland, Michael A.
Pilkiewicz, Kevin R.
Mayo, Michael L.
Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades
title Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades
title_full Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades
title_fullStr Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades
title_full_unstemmed Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades
title_short Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades
title_sort devil in the details: mechanistic variations impact information transfer across models of transcriptional cascades
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806174/
https://www.ncbi.nlm.nih.gov/pubmed/33439904
http://dx.doi.org/10.1371/journal.pone.0245094
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