Super-transient scaling in time-delay autonomous Boolean network motifs.

Published

Journal Article

Autonomous Boolean networks are commonly used to model the dynamics of gene regulatory networks and allow for the prediction of stable dynamical attractors. However, most models do not account for time delays along the network links and noise, which are crucial features of real biological systems. Concentrating on two paradigmatic motifs, the toggle switch and the repressilator, we develop an experimental testbed that explicitly includes both inter-node time delays and noise using digital logic elements on field-programmable gate arrays. We observe transients that last millions to billions of characteristic time scales and scale exponentially with the amount of time delays between nodes, a phenomenon known as super-transient scaling. We develop a hybrid model that includes time delays along network links and allows for stochastic variation in the delays. Using this model, we explain the observed super-transient scaling of both motifs and recreate the experimentally measured transient distributions.

Full Text

Duke Authors

Cited Authors

  • D'Huys, O; Lohmann, J; Haynes, ND; Gauthier, DJ

Published Date

  • September 2016

Published In

Volume / Issue

  • 26 / 9

Start / End Page

  • 094810 -

PubMed ID

  • 27781448

Pubmed Central ID

  • 27781448

Electronic International Standard Serial Number (EISSN)

  • 1089-7682

International Standard Serial Number (ISSN)

  • 1054-1500

Digital Object Identifier (DOI)

  • 10.1063/1.4954274

Language

  • eng