English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider the problem of inferring the unknown parameters of a stochasticbiochemical network model from a single measured time-course of theconcentration of some of the involved species. Such measurements are available,e.g., from live-cell fluorescence microscopy in image-based systems biology. Inaddition, fluctuation time-courses from, e.g., fluorescence correlationspectroscopy provide additional information about the system dynamics that canbe used to more robustly infer parameters than when considering only meanconcentrations. Estimating model parameters from a single experimentaltrajectory enables single-cell measurements and quantification of cell--cellvariability. We propose a novel combination of an adaptive Monte Carlo sampler,called Gaussian Adaptation, and efficient exact stochastic simulationalgorithms that allows parameter identification from single stochastictrajectories. We benchmark the proposed method on a linear and a non-linearreaction network at steady state and during transient phases. In addition, wedemonstrate that the present method also provides an ellipsoidal volumeestimate of the viable part of parameter space and is able to estimate thephysical volume of the compartment in which the observed reactions take place.

Global parameter identification of stochastic reaction networks from single trajectories