Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.

In systems biology, a mathematical description of signal transduction processes is used to gain a more detailed mechanistic understanding of cellular signaling networks. Such models typically depend on a number of parameters that have different influence on the model behavior. Local sensitivity anal...

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Autores principales:	Yvonne Koch, Thomas Wolf, Peter K Sorger, Roland Eils, Benedikt Brors
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/df4bc1a8e8ea42eaa4c5063035f12ece
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spelling	oai:doaj.org-article:df4bc1a8e8ea42eaa4c5063035f12ece2021-11-18T08:41:23ZDecision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.1932-620310.1371/journal.pone.0082593https://doaj.org/article/df4bc1a8e8ea42eaa4c5063035f12ece2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24367526/?tool=EBIhttps://doaj.org/toc/1932-6203In systems biology, a mathematical description of signal transduction processes is used to gain a more detailed mechanistic understanding of cellular signaling networks. Such models typically depend on a number of parameters that have different influence on the model behavior. Local sensitivity analysis is able to identify parameters that have the largest effect on signaling strength. Bifurcation analysis shows on which parameters a qualitative model response depends. Most methods for model analysis are intrinsically univariate. They typically cannot consider combinations of parameters since the search space for such analysis would be too large. This limitation is important since activation of a signaling pathway often relies on multiple rather than on single factors. Here, we present a novel method for model analysis that overcomes this limitation. As input to a model defined by a system of ordinary differential equations, we consider parameters for initial chemical species concentrations. The model is used to simulate the system response, which is then classified into pre-defined classes (e.g., active or not active). This is combined with a scan of the parameter space. Parameter sets leading to a certain system response are subjected to a decision tree algorithm, which learns conditions that lead to this response. We compare our method to two alternative multivariate approaches to model analysis: analytical solution for steady states combined with a parameter scan, and direct Lyapunov exponent (DLE) analysis. We use three previously published models including a model for EGF receptor internalization and two apoptosis models to demonstrate the power of our approach. Our method reproduces critical parameter relations previously obtained by both steady-state and DLE analysis while being more generally applicable and substantially less computationally expensive. The method can be used as a general tool to predict multivariate control strategies for pathway activation and to suggest strategies for drug intervention.Yvonne KochThomas WolfPeter K SorgerRoland EilsBenedikt BrorsPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82593 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yvonne Koch Thomas Wolf Peter K Sorger Roland Eils Benedikt Brors Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.
description	In systems biology, a mathematical description of signal transduction processes is used to gain a more detailed mechanistic understanding of cellular signaling networks. Such models typically depend on a number of parameters that have different influence on the model behavior. Local sensitivity analysis is able to identify parameters that have the largest effect on signaling strength. Bifurcation analysis shows on which parameters a qualitative model response depends. Most methods for model analysis are intrinsically univariate. They typically cannot consider combinations of parameters since the search space for such analysis would be too large. This limitation is important since activation of a signaling pathway often relies on multiple rather than on single factors. Here, we present a novel method for model analysis that overcomes this limitation. As input to a model defined by a system of ordinary differential equations, we consider parameters for initial chemical species concentrations. The model is used to simulate the system response, which is then classified into pre-defined classes (e.g., active or not active). This is combined with a scan of the parameter space. Parameter sets leading to a certain system response are subjected to a decision tree algorithm, which learns conditions that lead to this response. We compare our method to two alternative multivariate approaches to model analysis: analytical solution for steady states combined with a parameter scan, and direct Lyapunov exponent (DLE) analysis. We use three previously published models including a model for EGF receptor internalization and two apoptosis models to demonstrate the power of our approach. Our method reproduces critical parameter relations previously obtained by both steady-state and DLE analysis while being more generally applicable and substantially less computationally expensive. The method can be used as a general tool to predict multivariate control strategies for pathway activation and to suggest strategies for drug intervention.
format	article
author	Yvonne Koch Thomas Wolf Peter K Sorger Roland Eils Benedikt Brors
author_facet	Yvonne Koch Thomas Wolf Peter K Sorger Roland Eils Benedikt Brors
author_sort	Yvonne Koch
title	Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.
title_short	Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.
title_full	Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.
title_fullStr	Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.
title_full_unstemmed	Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.
title_sort	decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/df4bc1a8e8ea42eaa4c5063035f12ece
work_keys_str_mv	AT yvonnekoch decisiontreebasedmodelanalysisforefficientidentificationofparameterrelationsleadingtodifferentsignalingstates AT thomaswolf decisiontreebasedmodelanalysisforefficientidentificationofparameterrelationsleadingtodifferentsignalingstates AT peterksorger decisiontreebasedmodelanalysisforefficientidentificationofparameterrelationsleadingtodifferentsignalingstates AT rolandeils decisiontreebasedmodelanalysisforefficientidentificationofparameterrelationsleadingtodifferentsignalingstates AT benediktbrors decisiontreebasedmodelanalysisforefficientidentificationofparameterrelationsleadingtodifferentsignalingstates
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Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.

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