The fidelity of dynamic signalling by noisy biomolecular networks

Clive G. Bowsher*, Margaritis Voliotis, Peter S. Swain

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

41 Citations (Scopus)

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.

Original languageEnglish
Article number1002965
Number of pages9
JournalPLoS Computational Biology
Volume9
Issue number3
DOIs
Publication statusPublished - 28 Mar 2013

Structured keywords

  • Bristol BioDesign Institute

Keywords

  • STOCHASTIC GENE-EXPRESSION
  • CELLULAR DECISION-MAKING
  • EXTRINSIC FLUCTUATIONS
  • BIOCHEMICAL NETWORKS
  • BIOLOGICAL-SYSTEMS
  • INFORMATION
  • TRANSDUCTION
  • TRANSCRIPTION
  • FEEDBACK
  • SYNTHETIC BIOLOGY

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