Optimal experiment design for model selection in biochemical networks

Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring sim...
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Gespeichert in:

Autor*in:	Vanlier, Joep [verfasserIn] Tiemann, Christian A Hilbers, Peter AJ van Riel, Natal AW

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Model selection Inference Bayes factor Uncertainty

Anmerkung:	© Vanlier et al.; licensee BioMed Central Ltd. 2014

Übergeordnetes Werk:	Enthalten in: BMC systems biology - London : BioMed Central, 2007, 8(2014), 1 vom: 20. Feb.
Übergeordnetes Werk:	volume:8 ; year:2014 ; number:1 ; day:20 ; month:02

Links:	Volltext

DOI / URN:	10.1186/1752-0509-8-20

Katalog-ID:	SPR028417771

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520			\|a Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors.
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allfields	10.1186/1752-0509-8-20 doi (DE-627)SPR028417771 (SPR)1752-0509-8-20-e DE-627 ger DE-627 rakwb eng Vanlier, Joep verfasserin aut Optimal experiment design for model selection in biochemical networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vanlier et al.; licensee BioMed Central Ltd. 2014 Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. Model selection (dpeaa)DE-He213 Inference (dpeaa)DE-He213 Bayes factor (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Tiemann, Christian A aut Hilbers, Peter AJ aut van Riel, Natal AW aut Enthalten in BMC systems biology London : BioMed Central, 2007 8(2014), 1 vom: 20. Feb. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:8 year:2014 number:1 day:20 month:02 https://dx.doi.org/10.1186/1752-0509-8-20 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 1 20 02
spelling	10.1186/1752-0509-8-20 doi (DE-627)SPR028417771 (SPR)1752-0509-8-20-e DE-627 ger DE-627 rakwb eng Vanlier, Joep verfasserin aut Optimal experiment design for model selection in biochemical networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vanlier et al.; licensee BioMed Central Ltd. 2014 Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. Model selection (dpeaa)DE-He213 Inference (dpeaa)DE-He213 Bayes factor (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Tiemann, Christian A aut Hilbers, Peter AJ aut van Riel, Natal AW aut Enthalten in BMC systems biology London : BioMed Central, 2007 8(2014), 1 vom: 20. Feb. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:8 year:2014 number:1 day:20 month:02 https://dx.doi.org/10.1186/1752-0509-8-20 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 1 20 02
allfields_unstemmed	10.1186/1752-0509-8-20 doi (DE-627)SPR028417771 (SPR)1752-0509-8-20-e DE-627 ger DE-627 rakwb eng Vanlier, Joep verfasserin aut Optimal experiment design for model selection in biochemical networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vanlier et al.; licensee BioMed Central Ltd. 2014 Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. Model selection (dpeaa)DE-He213 Inference (dpeaa)DE-He213 Bayes factor (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Tiemann, Christian A aut Hilbers, Peter AJ aut van Riel, Natal AW aut Enthalten in BMC systems biology London : BioMed Central, 2007 8(2014), 1 vom: 20. Feb. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:8 year:2014 number:1 day:20 month:02 https://dx.doi.org/10.1186/1752-0509-8-20 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 1 20 02
allfieldsGer	10.1186/1752-0509-8-20 doi (DE-627)SPR028417771 (SPR)1752-0509-8-20-e DE-627 ger DE-627 rakwb eng Vanlier, Joep verfasserin aut Optimal experiment design for model selection in biochemical networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vanlier et al.; licensee BioMed Central Ltd. 2014 Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. Model selection (dpeaa)DE-He213 Inference (dpeaa)DE-He213 Bayes factor (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Tiemann, Christian A aut Hilbers, Peter AJ aut van Riel, Natal AW aut Enthalten in BMC systems biology London : BioMed Central, 2007 8(2014), 1 vom: 20. Feb. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:8 year:2014 number:1 day:20 month:02 https://dx.doi.org/10.1186/1752-0509-8-20 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 1 20 02
allfieldsSound	10.1186/1752-0509-8-20 doi (DE-627)SPR028417771 (SPR)1752-0509-8-20-e DE-627 ger DE-627 rakwb eng Vanlier, Joep verfasserin aut Optimal experiment design for model selection in biochemical networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Vanlier et al.; licensee BioMed Central Ltd. 2014 Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. Model selection (dpeaa)DE-He213 Inference (dpeaa)DE-He213 Bayes factor (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Tiemann, Christian A aut Hilbers, Peter AJ aut van Riel, Natal AW aut Enthalten in BMC systems biology London : BioMed Central, 2007 8(2014), 1 vom: 20. Feb. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:8 year:2014 number:1 day:20 month:02 https://dx.doi.org/10.1186/1752-0509-8-20 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 1 20 02
language	English
source	Enthalten in BMC systems biology 8(2014), 1 vom: 20. Feb. volume:8 year:2014 number:1 day:20 month:02
sourceStr	Enthalten in BMC systems biology 8(2014), 1 vom: 20. Feb. volume:8 year:2014 number:1 day:20 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Model selection Inference Bayes factor Uncertainty
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authorswithroles_txt_mv	Vanlier, Joep @@aut@@ Tiemann, Christian A @@aut@@ Hilbers, Peter AJ @@aut@@ van Riel, Natal AW @@aut@@
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author	Vanlier, Joep
spellingShingle	Vanlier, Joep misc Model selection misc Inference misc Bayes factor misc Uncertainty Optimal experiment design for model selection in biochemical networks
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topic_title	Optimal experiment design for model selection in biochemical networks Model selection (dpeaa)DE-He213 Inference (dpeaa)DE-He213 Bayes factor (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213
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title	Optimal experiment design for model selection in biochemical networks
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title_full	Optimal experiment design for model selection in biochemical networks
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author_browse	Vanlier, Joep Tiemann, Christian A Hilbers, Peter AJ van Riel, Natal AW
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title_sort	optimal experiment design for model selection in biochemical networks
title_auth	Optimal experiment design for model selection in biochemical networks
abstract	Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. © Vanlier et al.; licensee BioMed Central Ltd. 2014
abstractGer	Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. © Vanlier et al.; licensee BioMed Central Ltd. 2014
abstract_unstemmed	Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. © Vanlier et al.; licensee BioMed Central Ltd. 2014
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title_short	Optimal experiment design for model selection in biochemical networks
url	https://dx.doi.org/10.1186/1752-0509-8-20
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author2	Tiemann, Christian A Hilbers, Peter AJ van Riel, Natal AW
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Optimal experiment design for model selection in biochemical networks

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