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TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

BACKGROUND: Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate en...

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Autores principales: Jensen, Paul A, Lutz, Kyla A, Papin, Jason A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224351/
https://www.ncbi.nlm.nih.gov/pubmed/21943338
http://dx.doi.org/10.1186/1752-0509-5-147
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author Jensen, Paul A
Lutz, Kyla A
Papin, Jason A
author_facet Jensen, Paul A
Lutz, Kyla A
Papin, Jason A
author_sort Jensen, Paul A
collection PubMed
description BACKGROUND: Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. RESULTS: We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. CONCLUSION: The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.
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spelling pubmed-32243512011-11-30 TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks Jensen, Paul A Lutz, Kyla A Papin, Jason A BMC Syst Biol Software BACKGROUND: Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. RESULTS: We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. CONCLUSION: The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data. BioMed Central 2011-09-23 /pmc/articles/PMC3224351/ /pubmed/21943338 http://dx.doi.org/10.1186/1752-0509-5-147 Text en Copyright ©2011 Jensen et al; licensee BioMed Central Ltd. 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 Software
Jensen, Paul A
Lutz, Kyla A
Papin, Jason A
TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks
title TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks
title_full TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks
title_fullStr TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks
title_full_unstemmed TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks
title_short TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks
title_sort tiger: toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224351/
https://www.ncbi.nlm.nih.gov/pubmed/21943338
http://dx.doi.org/10.1186/1752-0509-5-147
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