CERENA: ChEmical REaction Network Analyzer—A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics

Kazeroonian, Atefeh; Fröhlich, Fabian; Raue, Andreas; Theis, Fabian J.; Hasenauer, Jan

doi:10.1371/journal.pone.0146732

Cited by 41 publications

(47 citation statements)

References 38 publications

(45 reference statements)

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“…In what follows we shall collectively refer to the EMRE and IOS as higher-order SSEs, meaning they are obtained using the SSE truncated to a higher-order than that giving the LNA. The simulation routines were generated using the CERENA toolbox [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

et al. 2016

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Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity.

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Section: Methodsmentioning

confidence: 99%

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

et al. 2016

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“…Unconditional moments can then be derived by summing up the weighted conditional moments. It is important to note that if then algebraic equations arise turning the equation system into a system of differential-algebraic equations, which renders its solution more difficult (see [ 12 , 26 ] for details).…”

Section: Methodsmentioning

confidence: 99%

Generalized method of moments for estimating parameters of stochastic reaction networks

Lück

Wolf

2016

BMC Syst Biol

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BackgroundDiscrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks.ResultsWe propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies.ConclusionsThe generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0342-8) contains supplementary material, which is available to authorized users.

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“…These toolboxes can be found under https://github.com/ICB-DCM. For the momentclosure approximation, we employed the toolbox CERENA (Kazeroonian et al, 2016) available under https://cerenadevelopers.github.io/CERENA/. The whole analysis was performed using…”

Section: Implementation and Code Availabilitymentioning

confidence: 99%

Robust calibration of hierarchical population models for heterogeneous cell populations

Loos

Hasenauer

2019

Preprint

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Highlights• Generalizes hierarchical population model to various distribution assumptions • Provides framework for efficient calibration of the hierarchical population model• Simulation study and application to experimental data reveal improved robustness and optimization performance AbstractCellular heterogeneity is known to have important effects on signal processing and cellular decision making. To understand these processes, multiple classes of mathematical models have been introduced. The hierarchical population model builds a novel class which allows for the mechanistic description of heterogeneity and explicitly takes into account subpopulation structures. However, this model requires a parametric distribution assumption for the cell population and, so far, only the normal distribution has been employed. Here, we incorporate alternative distribution assumptions into the model, assess their robustness against outliers and evaluate their influence on the performance of model calibration in a simulation study and a real-world application example. We found that alternative distributions provide reliable parameter estimates even in the presence of outliers, and can in fact increase the convergence of model calibration.

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CERENA: ChEmical REaction Network Analyzer—A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics

Cited by 41 publications

References 38 publications

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

Generalized method of moments for estimating parameters of stochastic reaction networks

Robust calibration of hierarchical population models for heterogeneous cell populations

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