Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabol...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fiona Achcar, Eduard J Kerkhoven, SilicoTryp Consortium, Barbara M Bakker, Michael P Barrett, Rainer Breitling
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/217bc04f7831484fb678adb5621bcc68
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:217bc04f7831484fb678adb5621bcc68
record_format	dspace
spelling	oai:doaj.org-article:217bc04f7831484fb678adb5621bcc682021-11-18T05:51:38ZDynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.1553-734X1553-735810.1371/journal.pcbi.1002352https://doaj.org/article/217bc04f7831484fb678adb5621bcc682012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22379410/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.Fiona AchcarEduard J KerkhovenSilicoTryp ConsortiumBarbara M BakkerMichael P BarrettRainer BreitlingPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 1, p e1002352 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Fiona Achcar Eduard J Kerkhoven SilicoTryp Consortium Barbara M Bakker Michael P Barrett Rainer Breitling Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.
description	Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.
format	article
author	Fiona Achcar Eduard J Kerkhoven SilicoTryp Consortium Barbara M Bakker Michael P Barrett Rainer Breitling
author_facet	Fiona Achcar Eduard J Kerkhoven SilicoTryp Consortium Barbara M Bakker Michael P Barrett Rainer Breitling
author_sort	Fiona Achcar
title	Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.
title_short	Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.
title_full	Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.
title_fullStr	Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.
title_full_unstemmed	Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.
title_sort	dynamic modelling under uncertainty: the case of trypanosoma brucei energy metabolism.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/217bc04f7831484fb678adb5621bcc68
work_keys_str_mv	AT fionaachcar dynamicmodellingunderuncertaintythecaseoftrypanosomabruceienergymetabolism AT eduardjkerkhoven dynamicmodellingunderuncertaintythecaseoftrypanosomabruceienergymetabolism AT silicotrypconsortium dynamicmodellingunderuncertaintythecaseoftrypanosomabruceienergymetabolism AT barbarambakker dynamicmodellingunderuncertaintythecaseoftrypanosomabruceienergymetabolism AT michaelpbarrett dynamicmodellingunderuncertaintythecaseoftrypanosomabruceienergymetabolism AT rainerbreitling dynamicmodellingunderuncertaintythecaseoftrypanosomabruceienergymetabolism
_version_	1718424715098324992

Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.

Ejemplares similares