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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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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. |
format | Online Article Text |
id | pubmed-3549916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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