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Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns

Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system’s Jacobian matrix that depends solely on the n...

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Autores principales: Childs, Dorothee, Grimbs, Sergio, Selbig, Joachim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765856/
https://www.ncbi.nlm.nih.gov/pubmed/26072485
http://dx.doi.org/10.1093/bioinformatics/btv243
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author Childs, Dorothee
Grimbs, Sergio
Selbig, Joachim
author_facet Childs, Dorothee
Grimbs, Sergio
Selbig, Joachim
author_sort Childs, Dorothee
collection PubMed
description Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system’s Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling. Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase. Contact: dorothee.childs@embl.de Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-47658562016-03-04 Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns Childs, Dorothee Grimbs, Sergio Selbig, Joachim Bioinformatics Ismb/Eccb 2015 Proceedings Papers Committee July 10 to July 14, 2015, Dublin, Ireland Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system’s Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling. Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase. Contact: dorothee.childs@embl.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-06-15 2015-06-10 /pmc/articles/PMC4765856/ /pubmed/26072485 http://dx.doi.org/10.1093/bioinformatics/btv243 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Ismb/Eccb 2015 Proceedings Papers Committee July 10 to July 14, 2015, Dublin, Ireland
Childs, Dorothee
Grimbs, Sergio
Selbig, Joachim
Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
title Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
title_full Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
title_fullStr Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
title_full_unstemmed Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
title_short Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
title_sort refined elasticity sampling for monte carlo-based identification of stabilizing network patterns
topic Ismb/Eccb 2015 Proceedings Papers Committee July 10 to July 14, 2015, Dublin, Ireland
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765856/
https://www.ncbi.nlm.nih.gov/pubmed/26072485
http://dx.doi.org/10.1093/bioinformatics/btv243
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