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Approximate maximum likelihood estimation for stochastic chemical kinetics

Recent experimental imaging techniques are able to tag and count molecular populations in a living cell. From these data mathematical models are inferred and calibrated. If small populations are present, discrete-state stochastic models are widely-used to describe the discreteness and randomness of...

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Detalles Bibliográficos
Autores principales: Andreychenko, Aleksandr, Mikeev, Linar, Spieler, David, Wolf, Verena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3549916/
https://www.ncbi.nlm.nih.gov/pubmed/22809254
http://dx.doi.org/10.1186/1687-4153-2012-9
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author Andreychenko, Aleksandr
Mikeev, Linar
Spieler, David
Wolf, Verena
author_facet Andreychenko, Aleksandr
Mikeev, Linar
Spieler, David
Wolf, Verena
author_sort Andreychenko, Aleksandr
collection PubMed
description Recent experimental imaging techniques are able to tag and count molecular populations in a living cell. From these data mathematical models are inferred and calibrated. If small populations are present, discrete-state stochastic models are widely-used to describe the discreteness and randomness of molecular interactions. Based on time-series data of the molecular populations, the corresponding stochastic reaction rate constants can be estimated. This procedure is computationally very challenging, since the underlying stochastic process has to be solved for different parameters in order to obtain optimal estimates. Here, we focus on the maximum likelihood method and estimate rate constants, initial populations and parameters representing measurement errors.
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spelling pubmed-35499162013-01-24 Approximate maximum likelihood estimation for stochastic chemical kinetics Andreychenko, Aleksandr Mikeev, Linar Spieler, David Wolf, Verena EURASIP J Bioinform Syst Biol Research Recent experimental imaging techniques are able to tag and count molecular populations in a living cell. From these data mathematical models are inferred and calibrated. If small populations are present, discrete-state stochastic models are widely-used to describe the discreteness and randomness of molecular interactions. Based on time-series data of the molecular populations, the corresponding stochastic reaction rate constants can be estimated. This procedure is computationally very challenging, since the underlying stochastic process has to be solved for different parameters in order to obtain optimal estimates. Here, we focus on the maximum likelihood method and estimate rate constants, initial populations and parameters representing measurement errors. BioMed Central 2012 2012-07-18 /pmc/articles/PMC3549916/ /pubmed/22809254 http://dx.doi.org/10.1186/1687-4153-2012-9 Text en Copyright ©2012 Andreychenko et al.; licensee Springer. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Andreychenko, Aleksandr
Mikeev, Linar
Spieler, David
Wolf, Verena
Approximate maximum likelihood estimation for stochastic chemical kinetics
title Approximate maximum likelihood estimation for stochastic chemical kinetics
title_full Approximate maximum likelihood estimation for stochastic chemical kinetics
title_fullStr Approximate maximum likelihood estimation for stochastic chemical kinetics
title_full_unstemmed Approximate maximum likelihood estimation for stochastic chemical kinetics
title_short Approximate maximum likelihood estimation for stochastic chemical kinetics
title_sort approximate maximum likelihood estimation for stochastic chemical kinetics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3549916/
https://www.ncbi.nlm.nih.gov/pubmed/22809254
http://dx.doi.org/10.1186/1687-4153-2012-9
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