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Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks
[chapter]
2015
Lecture Notes in Computer Science
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The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. ...
model parameters are inferred from real measured data. ...
In this paper, we propose an approach for parameter inference based on moment closure that is complemented by stochastic simulation. ...
doi:10.1007/978-3-319-23401-4_8
fatcat:fhvrznqssfdmndx4mvlxllnrqa
Moment-based inference predicts bimodality in transient gene expression
2012
Proceedings of the National Academy of Sciences of the United States of America
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., if they are bimodal. extrinsic variability | high-osmolarity glycerol pathway | moment dynamics | parameter inference | stochastic kinetic models B uilding predictive computational models of intracellular ...
Here we introduce a moment-based inference scheme for calibrating stochastic models with heterogeneous single-cell measurements. ...
The work of J.R. and J.L. was supported in part by SystemsX.ch under the project YeastX and by the European Commission under the project MoVeS. ...
doi:10.1073/pnas.1200161109
pmid:22566653
pmcid:PMC3361437
fatcat:egkzjrx3p5fmdncg2tqpgfmu2e
Adaptive moment closure for parameter inference of biochemical reaction networks
2016
Biosystems (Amsterdam. Print)
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The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. ...
Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. ...
Acknowledgments This work is based on the CMSB 2015 paper "Adaptive moment closure for parameter inference of biochemical reaction networks" (Bogomolov et al., 2015) ...
doi:10.1016/j.biosystems.2016.07.005
pmid:27461396
fatcat:ijnlhenre5eehgf27ql34vmfja
CERENA: ChEmical REaction Network Analyzer—A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics
2016
PLoS ONE
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In this manuscript we introduced CERENA, a toolbox for the analysis of stochastic chemical kinetics using Approximations of the Chemical Master Equation solution statistics. ...
The analysis of these stochastic chemical kinetics is important for understanding cell-to-cell variability and its functional implications, but it is also challenging. ...
The parameters of differential equations can efficiently be inferred using gradient-based optimization methods [17] . ...
doi:10.1371/journal.pone.0146732
pmid:26807911
pmcid:PMC4726759
fatcat:ikkfefujmngopbwjftfr6gkbcu
Diagnostics for assessing the linear noise and moment closure approximations
2016
Statistical Applications in Genetics and Molecular Biology
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In particular, we leverage the normality assumption of the linear noise and moment closure approximations. ...
By constructing an efficient space filling design over the parameter region of interest, we present a number of useful diagnostic tools that aids modellers in assessing whether the approximation is suitable ...
Within the context of stochastic kinetic models, both the moment closure and LNA approaches can be seen as GP emulators. ...
doi:10.1515/sagmb-2014-0071
pmid:27682714
fatcat:7e2bl7marrb77jhe4z2u7dbxxa
Diagnostics for assessing the linear noise and moment closure approximations
[article]
2016
arXiv
pre-print
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In particular, we leverage the normality assumption of the linear noise and moment closure approximations. ...
By constructing an efficient space filling design over the parameter region of interest, we present a number of useful diagnostic tools that aids modellers in assessing whether the approximation is suitable ...
Within the context of stochastic kinetic models, both the moment closure and LNA approaches can be seen as GP emulators. ...
arXiv:1409.1096v2
fatcat:t6j4d4vjffanbdkmoui3nqxixm
Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
2019
Frontiers in Genetics
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The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference ...
Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. ...
A multivariate moment closure method is developed in Lakatos et al. (2015) to describe the non-linear dynamics of stochastic kinetics. ...
doi:10.3389/fgene.2019.00549
pmid:31258548
pmcid:PMC6588029
fatcat:moxooil5argjhgcb6bpv7zz2gu
Multivariate moment closure techniques for stochastic kinetic models
2015
Journal of Chemical Physics
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Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. ...
We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics ...
We adjusted initial conditions and parameters to fit the above stochastic model assuming an average system size of 9.24 · 10 5 (based on the 1540 µm 3 average volume of HeLa cells) and also lowered molecular ...
doi:10.1063/1.4929837
pmid:26342359
fatcat:doyt7wkc7jc4hl2cd3syi4tfpq
Generalized method of moments for estimating parameters of stochastic reaction networks
2016
BMC Systems Biology
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We 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 ...
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 ...
Methods
Stochastic chemical kinetics Our inference approach relies on a Markov modeling approach that follows Gillespie's theory of stochastic chemical kinetics. ...
doi:10.1186/s12918-016-0342-8
pmid:27769280
pmcid:PMC5073941
fatcat:uismfqeyxjfntdlop4tb73l6fi
Approximation and inference methods for stochastic biochemical kinetics—a tutorial review
2017
Journal of Physics A: Mathematical and Theoretical
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In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics. ...
Next, we discuss several approximation methods, including the chemical Langevin equation, the system size expansion, moment closure approximations, time-scale separation approximations and hybrid methods ...
In summary, we have presented an introduction to modelling, approximation and inference methods for stochastic chemical kinetics based on the chemical master equation. ...
doi:10.1088/1751-8121/aa54d9
fatcat:bdds2szh5zhlpc6j3kesftujgi
Comprehensive review of models and methods for inferences in bio-chemical reaction networks
[article]
2019
arXiv
pre-print
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The aim of this review paper is to explore developments of past decade to understand what BRN models are commonly used in literature, and for what inference tasks and inference methods. ...
Over past decade, the interest in parameter and state estimation in models of (bio-)chemical reaction networks (BRNs) grew considerably. ...
Multivariate moment closure method is developed in (Lakatos et al., 2015) to describe nonlinear dynamics of stochastic kinetics. ...
arXiv:1902.05828v1
fatcat:sjb4jm53a5h3jf2swbiz7vk6hu
A general moment expansion method for stochastic kinetic models
2013
Journal of Chemical Physics
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Moment approximation methods are gaining increasing attention for their use in the approximation of the stochastic kinetics of chemical reaction systems. ...
We demonstrate the method using a dimerisation reaction, Michaelis-Menten kinetics and a model of an oscillating p53 system. ...
Other analyses have employed approximate Bayesian computation schemes 42 , and used moment-based inference employing the mean and variance to infer parameters of a bimodal system 43 . ...
doi:10.1063/1.4802475
pmid:23656108
fatcat:mygtx5kjzjcu3kqlvhxwwahskm
A data-integrated method for analyzing stochastic biochemical networks
2011
Journal of Chemical Physics
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the kinetic rate constants of stochastic network models and subsequent analysis of their dynamics. ...
By coupling this moment-closure method with a parameter search procedure, we further demonstrate how a model's kinetic parameters can be iteratively determined in order to fit measured distribution data ...
To achieve this, we develop a practical data-based methodology for truncating moment equations in the stochastic formulation of biological network models. ...
doi:10.1063/1.3664126
pmid:22149782
pmcid:PMC3324259
fatcat:xca7tzjhfjelfel7bc6av77sja
Model reduction for stochastic CaMKII reaction kinetics in synapses by graph-constrained correlation dynamics
2015
Physical Biology
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A stochastic reaction network model of Ca(2+) dynamics in synapses (Pepke et al PLoS Comput. ...
Data from numerically intensive simulations is used to train a reduced model that, out of sample, correctly predicts the evolution of interaction parameters characterizing the instantaneous probability ...
Additional model reductions for stochastic chemical kinetics, other than moment closure methods, include the classic strategy of using separation of time scales to eliminate fast degrees of freedom as ...
doi:10.1088/1478-3975/12/4/045005
pmid:26086598
pmcid:PMC4489159
fatcat:bpqivawfdzchtaoza3rb3cezmu
Moment Fitting for Parameter Inference in Repeatedly and Partially Observed Stochastic Biological Models
2012
PLoS ONE
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We demonstrate the potential of moment fitting for parameter inference by means of illustrative stochastic biological models from the literature and address topics for future research. ...
Based on the chemical master equation we furthermore derive closed systems of parameter dependent nonlinear ordinary differential equations that predict the time evolution of the statistical moments. ...
Acknowledgments I have developed the moment fitting concept presented in this paper during my visiting fellowship at the Isaac Newton Institute for Mathematical Sciences, Cambridge, UK and kindly acknowledge ...
doi:10.1371/journal.pone.0043001
pmid:22900079
pmcid:PMC3416831
fatcat:jhxqfikhqnajho6jmz7jcrbtca
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