ArticlePLoS computational biology2013
The fidelity of dynamic signaling by noisy biomolecular networks.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Continuous variable responses and signal gating form kinetic bases for pulsatile insulin signaling and emergence of resistance.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Information transmission in a two-step cascade: interplay of activation and repression.Theory in biosciences = Theorie in den Biowissenschaften · 2021Article
- Optimal ligand discrimination by asymmetric dimerization and turnover of interferon receptors.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- Signaling pathways have an inherent need for noise to acquire information.BMC bioinformatics · 2020Article
- Multiplexing information flow through dynamic signalling systems.PLoS computational biology · 2020Article
- Information transmission from NFkB signaling dynamics to gene expression.PLoS computational biology · 2020Article
- Intrinsic and extrinsic noise of gene expression in lineage trees.Scientific reports · 2019Article
- Mathematical modeling of gonadotropin-releasing hormone signaling.Molecular and cellular endocrinology · 2017Review
- Information Transfer via Gonadotropin-Releasing Hormone Receptors to ERK and NFAT: Sensing GnRH and Sensing Dynamics.Journal of the Endocrine Society · 2017Article
- Stochastic Simulation of Biomolecular Networks in Dynamic Environments.PLoS computational biology · 2016Article
- Molecular circuits for dynamic noise filtering.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Information Transfer in Gonadotropin-releasing Hormone (GnRH) Signaling: EXTRACELLULAR SIGNAL-REGULATED KINASE (ERK)-MEDIATED FEEDBACK LOOPS CONTROL HORMONE SENSING.The Journal of biological chemistry · 2016Article
- Only accessible information is useful: insights from gradient-mediated patterning.Royal Society open science · 2015Article
- Information processing by simple molecular motifs and susceptibility to noise.Journal of the Royal Society, Interface · 2015Article
- Role of relaxation time scale in noisy signal transduction.PloS one · 2015Article
- Uncoupled analysis of stochastic reaction networks in fluctuating environments.PLoS computational biology · 2014Article
- Information transfer by leaky, heterogeneous, protein kinase signaling systems.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.
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