dc.contributor.author | Bowsher, CG | |
dc.contributor.author | Voliotis, M | |
dc.contributor.author | Swain, PS | |
dc.date.accessioned | 2017-06-02T13:29:00Z | |
dc.date.issued | 2013-03-28 | |
dc.description.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. | en_GB |
dc.description.sponsorship | We acknowledge support from a Medical Research Council and Engineering and Physical Sciences Council funded Fellowship in Biomedical Informatics (CGB) and a Scottish Universities Life Sciences Alliance chair in Systems Biology (PSS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | en_GB |
dc.identifier.citation | Vol. 9 (3), article e1002965 | en_GB |
dc.identifier.doi | 10.1371/journal.pcbi.1002965 | |
dc.identifier.uri | http://hdl.handle.net/10871/27776 | |
dc.language.iso | en | en_GB |
dc.publisher | Public Library of Science | en_GB |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/23555208 | en_GB |
dc.rights | Copyright: © 2013 Bowsher et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_GB |
dc.subject | Computational Biology | en_GB |
dc.subject | Feedback, Physiological | en_GB |
dc.subject | Gene Expression | en_GB |
dc.subject | Models, Biological | en_GB |
dc.subject | Signal Transduction | en_GB |
dc.title | The fidelity of dynamic signaling by noisy biomolecular networks | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2017-06-02T13:29:00Z | |
exeter.place-of-publication | United States | en_GB |
dc.description | This is the final version of the article. Available from Public Library of Science via the DOI in this record. | en_GB |
dc.identifier.journal | PLoS Computational Biology | en_GB |