Designing experiments to understand the variability in biochemical reaction networks.

Jakob Ruess, Andreas Milias-Argeitis, John Lygeros

Research output: Contribution to journalArticleAcademicpeer-review

47 Citations (Scopus)

Abstract

Exploiting the information provided by the molecular noise of a biological process has proved to be valuable in extracting knowledge about the underlying kinetic parameters and sources of variability from single-cell measurements. However, quantifying this additional information a priori, to decide whether a single-cell experiment might be beneficial, is currently only possible in systems where either the chemical master equation is computationally tractable or a Gaussian approximation is appropriate. Here, we provide formulae for computing the information provided by measured means and variances from the first four moments and the parameter derivatives of the first two moments of the underlying process. For stochastic kinetic models for which these moments can be either computed exactly or approximated efficiently, the derived formulae can be used to approximate the information provided by single-cell distribution experiments. Based on this result, we propose an optimal experimental design framework which we employ to compare the utility of dual-reporter and perturbation experiments for quantifying the different noise sources in a simple model of gene expression. Subsequently, we compare the information content of a set of experiments which have been performed in an engineered light-switch gene expression system in yeast and show that well-chosen gene induction patterns may allow one to identify features of the system which remain hidden in unplanned experiments.
Original languageEnglish
Article number20130588
Number of pages9
JournalJournal of the Royal Society Interface
Volume10
Issue number88
DOIs
Publication statusPublished - 6-Nov-2013
Externally publishedYes

Keywords

  • Fisher information
  • cell-to-cell variability
  • continuous-time Markov chains
  • gene expression
  • optimal experimental design

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