Global Parameter Identification of Stochastic Reaction Networks from Single Trajectories

  • Christian L. Müller
  • Rajesh Ramaswamy
  • Ivo F. Sbalzarini
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)


We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy (FCS) provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell–cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation (GaA), and efficient exact stochastic simulation algorithms (SSA) that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.


Markov Chain Monte Carlo Forward Model Reaction Network Fluorescence Correlation Spectroscopy Approximate Bayesian Computation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



RR was financed by a grant from the Swiss initiative (grant WingX), evaluated by the Swiss National Science Foundation. This project was also supported with a grant from the Swiss initiative, grant LipidX-2008/011, to IFS.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Christian L. Müller
    • 1
  • Rajesh Ramaswamy
    • 1
  • Ivo F. Sbalzarini
    • 1
  1. 1.Institute of Theoretical Computer Science and Swiss Institute of BioinformaticsETH ZurichZurichSwitzerland

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