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Quantifying information accumulation encoded in the dynamics of biochemical signaling

Tang, Ying and Adelaja, Adewunmi and Ye, Felix X.-F. and Deeds, Eric and Wollman, Roy and Hoffmann, Alexander (2021) Quantifying information accumulation encoded in the dynamics of biochemical signaling. Nature Communications, 12 . Art. No. 1272. ISSN 2041-1723. https://resolver.caltech.edu/CaltechAUTHORS:20210224-132930780

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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 NFκB 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 IκBα 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41467-021-21562-0DOIArticle
https://github.com/signalingsystemslab/dMIRelated ItemCode
https://sites.google.com/view/dmipackageRelated ItemCode
ORCID:
AuthorORCID
Tang, Ying0000-0002-9272-8570
Deeds, Eric0000-0002-2868-7495
Wollman, Roy0000-0003-3865-2605
Hoffmann, Alexander0000-0002-5607-3845
Additional Information:© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 18 September 2020; Accepted 29 January 2021; Published 24 February 2021. We thank Diane Lefaudeux, Haripriya Vaidehi Narayanan, Supriya Sen, Katherine Sheu, and Ning Wang for valuable discussions. We also thank Sheng-hong Chen, Jeremy Purvis, and Galit Lahav for sharing the dataset of p53, and Sergi Regot and Markus Covert for sharing the dataset of ERK, p38, and JNK. The work was funded by NIH Grant R01AI127864 (to A.H.). Y.T is supported by Collaboratory fellowship at UCLA. A.A. was funded by the UCLA-Caltech MSTP: T32GM008042, Vascular Biology Training grant: T32HL69766, and a NRSA F31 fellowship: 1F31AI138450. Data availability: The authors declare that the data supporting the findings of this study are available within the paper [and its supplementary information files]. Source data are provided with this paper. Code availability: The MATLAB code package dMI is available at GitHub (https://github.com/signalingsystemslab/dMI)49 with a guideline on the website (https://sites.google.com/view/dmipackage). All the simulations were done with MATLAB version R2018b. Author Contributions: Y.T., R.W., E.D. and A.H. designed research. Y.T. developed the theoretical workflow, with technical guidance from R.W., E.D. and F.X.-F.Y. A.A. generated experimental data. Y.T. analyzed data. Y.T. and A.H. wrote the paper, with critical input from all A.A., R.W., E.D. and F.X.-F.Y. The authors declare no competing interests. Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.
Funders:
Funding AgencyGrant Number
NIHR01AI127864
UCLA-Caltech Medical Scientist Training ProgramUNSPECIFIED
NIH Predoctoral FellowshipT32GM008042
NIH Predoctoral FellowshipT32HL69766
NIH Postdoctoral Fellowship1F31AI138450
Subject Keywords:Biological physics; Cellular signalling networks; Information theory; NF-kappaB
Record Number:CaltechAUTHORS:20210224-132930780
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210224-132930780
Official Citation:Tang, Y., Adelaja, A., Ye, F.XF. et al. Quantifying information accumulation encoded in the dynamics of biochemical signaling. Nat Commun 12, 1272 (2021). https://doi.org/10.1038/s41467-021-21562-0
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108176
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Feb 2021 22:43
Last Modified:24 Feb 2021 22:43

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