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Global Parameter Identification of Stochastic Reaction Networks from Single Trajectories


Müller, Christian L; Ramaswamy, Rajesh; Sbalzarini, Ivo F (2011). Global Parameter Identification of Stochastic Reaction Networks from Single Trajectories. Advances in Experimental Medicine and Biology, 736:477-498.

Abstract

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 provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Esti- mating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell–cell variability. We propose a novel com- bination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and ef- ficient exact stochastic simulation algorithms 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 ad- dition, 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.

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 provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Esti- mating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell–cell variability. We propose a novel com- bination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and ef- ficient exact stochastic simulation algorithms 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 ad- dition, 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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > LipidX
Special Collections > SystemsX.ch > Research, Technology and Development Projects > WingX
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2011
Deposited On:05 Jul 2013 10:42
Last Modified:05 Apr 2016 16:51
Publisher:Springer
ISSN:0065-2598
Publisher DOI:https://doi.org/10.1007/978-1-4419-7210-1_28
Permanent URL: https://doi.org/10.5167/uzh-79199

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