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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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Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes ...
The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. ...
In Section 4, we propose our automated adaptive parameter inference method. In Section 5, we study the performance of our method for some benchmark reaction networks. ...
doi:10.1007/978-3-319-23401-4_8
fatcat:fhvrznqssfdmndx4mvlxllnrqa
Adaptive moment closure for parameter inference of biochemical reaction networks
2016
Biosystems (Amsterdam. Print)
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Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes ...
The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. ...
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
Parameter estimation for biochemical reaction networks using Wasserstein distances
[article]
2019
arXiv
pre-print
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We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances. ...
We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters. ...
Acknowledgments We would like to thank our reviewers for their suggestions and careful reading of the manuscript. ...
arXiv:1907.07986v2
fatcat:qzgvos7khrepha2ozrwes5rhvy
Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series
2016
PLoS Computational Biology
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networks by implicitly accounting for billions of topology variants. ...
structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. ...
Acknowledgments We acknowledge Justin Feigelman, Will Macnair, Eirini Arvaniti and Dimitris Christodoulou for helpful discussions and feedback on the manuscript.
Formal analysis: AK MC. ...
doi:10.1371/journal.pcbi.1005234
pmid:27923064
pmcid:PMC5140059
fatcat:mmsid5eoo5fb3js5aeh7yglrru
Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models
[article]
2022
bioRxiv
pre-print
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We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. ...
trade accuracy for tractability. ...
As a consequence Bayesian inference for biochemical reaction networks often relies on a variety of approximations to the likelihood function [8, 10] . ...
doi:10.1101/2022.01.25.477666
fatcat:l5i2in2zxzcyjhygqtbl7tpbaq
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 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 this paper we explore the usefulness of the GMM for moment-based simulations of stochastic reaction networks. ...
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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Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the Chemical Master Equation. ...
First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods. ...
Computational methods for Bayesian inference in stochastic chemical reaction networks 6.4.1. Methods for general networks. ...
doi:10.1088/1751-8121/aa54d9
fatcat:bdds2szh5zhlpc6j3kesftujgi
Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion
2016
PLoS Computational Biology
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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 ...
Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. ...
Parameter estimation To infer the parameters of biochemical reaction networks we employ maximum likelihood and Bayesian parameter estimation. ...
doi:10.1371/journal.pcbi.1005030
pmid:27447730
pmcid:PMC4957800
fatcat:y5sor4k225bypfm7gqaw66d75a
Molecular circuits for dynamic noise filtering
2016
Proceedings of the National Academy of Sciences of the United States of America
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Here we develop an optimal filtering theory that is suitable for noisy biochemical networks. ...
filtering | noise cancellation | adaptive design ...
The authors are grateful to Yuan Chen and Sundipta Rao for providing technical assistance to the first author while he was performing the experiments. ...
doi:10.1073/pnas.1517109113
pmid:27078094
pmcid:PMC4855548
fatcat:m2turhi62fcg3cvqojvkrytqq4
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 ...
Introduction Given a stochastic reaction network, even one specified by high-level 'parameterized reactions' or 'rule-based' notation [2] [3] [4] [5] [6] , there is a corresponding chemical master equation ...
doi:10.1088/1478-3975/12/4/045005
pmid:26086598
pmcid:PMC4489159
fatcat:bpqivawfdzchtaoza3rb3cezmu
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. ...
Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. ...
The reduced system size can be beneficial in most inference tasks, as these algorithms involve evaluating the system for thousands of parameter sets. ...
doi:10.1063/1.4929837
pmid:26342359
fatcat:doyt7wkc7jc4hl2cd3syi4tfpq
Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
2019
Frontiers in Genetics
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Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. ...
The key processes in biological and chemical systems are described by networks of chemical reactions. ...
., 2011) , or as networks of biochemical reactions (Ashyraliyev et al., 2009) . ...
doi:10.3389/fgene.2019.00549
pmid:31258548
pmcid:PMC6588029
fatcat:moxooil5argjhgcb6bpv7zz2gu
Comprehensive review of models and methods for inferences in bio-chemical reaction networks
[article]
2019
arXiv
pre-print
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Over past decade, the interest in parameter and state estimation in models of (bio-)chemical reaction networks (BRNs) grew considerably. ...
Key processes in biological and chemical systems are described by networks of chemical reactions. ...
., 2011) , or as networks of biochemical reactions (Ashyraliyev et al., 2009) . ...
arXiv:1902.05828v1
fatcat:sjb4jm53a5h3jf2swbiz7vk6hu
Model-Checking Based Approaches to Parameter Estimation of Gene Regulatory Networks
2014
2014 19th International Conference on Engineering of Complex Computer Systems
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Our goal is to develop new algorithms and tools, which are tailored for the modelling and analysis of gene regulatory networks, by exploring model checking techniques that have been developed and widely ...
of gene regulatory networks. ...
There exist a number of methods for parameter estimation for Markov models describing networks of biochemical reactions. ...
doi:10.1109/iceccs.2014.38
dblp:conf/iceccs/MizeraPY14
fatcat:ejgc4esdqne5hh6eejbyzaukwq
Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data
2018
Processes
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Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. ...
Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems. ...
In this paper, we discuss identifiability and reconstruction of unknown parameters and interactions in biochemical reaction networks. ...
doi:10.3390/pr6090136
fatcat:hddkkz6e5rfxpgx7brv6ibdo4y
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