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

BMC Systems Biology - Tập 5 Số 1 - 2011
Paul A. Jensen1, Kyla Lutz1, Jason A. Papin1
1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA

Tóm tắt

Abstract 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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Tài liệu tham khảo

Oberhardt MA, Palsson BØ, Papin JA: Applications of genome-scale metabolic reconstructions. Mol Syst Biol. 2009, 5: 320-

Burgard AP, Pharkya P, Maranas CD: Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng. 2003, 84 (6): 647-57. 10.1002/bit.10803

Oberhardt MA, Goldberg JB, Hogardt M, Papin JA: Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection. J Bacteriol. 2010, 192 (20): 5534-48. 10.1128/JB.00900-10

Klitgord N, Segrè D: Environments that induce synthetic microbial ecosystems. PLoS Comput Biol. 2010, 6 (11): e1001002- 10.1371/journal.pcbi.1001002

Schmidt BJ, Lin-Schmidt X, Chamberlin A, Salehi-Ashtiani K, Papin JA: Metabolic systems analysis to advance algal biotechnology. Biotechnol J. 2010, 5 (7): 660-70. 10.1002/biot.201000129

Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N, Yee A, Lewis RA, Eils R, König R, Palsson BØ: Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat Biotechnol. 2010, 28 (12): 1279-85. 10.1038/nbt.1711

Orth JD, Thiele I, Palsson BØ: What is flux balance analysis?. Nat Biotechnol. 2010, 28 (3): 245-8. 10.1038/nbt.1614

Feist AM, Palsson BO: The biomass objective function. Curr Opin Microbiol. 2010, 13 (3): 344-349. 10.1016/j.mib.2010.03.003

Duarte NC, Herrgård MJ, Palsson BØ: Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 2004, 14 (7): 1298-309. 10.1101/gr.2250904

Shlomi T, Eisenberg Y, Sharan R, Ruppin E: A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol Syst Biol. 2007, 3: 101-

Kim J, Reed JL: OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst Biol. 2010, 4: 53- 10.1186/1752-0509-4-53

Jensen PA, Papin JA: Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics. 2011, 27 (4): 541-7. 10.1093/bioinformatics/btq702

Barua D, Kim J, Reed JL: An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLoS Comput Biol. 2010, 6 (10): e1000970- 10.1371/journal.pcbi.1000970

Suthers PF, Zomorrodi A, Maranas CD: Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol Syst Biol. 2009, 5: 301-

Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO: Integrating high-throughput and computational data elucidates bacterial networks. Nature. 2004, 429 (6987): 92-6. 10.1038/nature02456

Herrgård MJ, Lee BS, Portnoy V, Palsson BØ: Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res. 2006, 16 (5): 627-35. 10.1101/gr.4083206

Covert MW, Schilling CH, Palsson B: Regulation of gene expression in flux balance models of metabolism. J Theor Biol. 2001, 213: 73-88. 10.1006/jtbi.2001.2405

Klamt S, Saez-Rodriguez J, Gilles ED: Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol. 2007, 1: 2- 10.1186/1752-0509-1-2

Cvijovic M, Olivares-Hernández R, Agren R, Dahr N, Vongsangnak W, Nookaew I, Patil KR, Nielsen J: BioMet Toolbox: genome-wide analysis of metabolism. Nucleic Acids Res. 2010, W144-W149. 38 Web Server,

Becker SA, Feist AM, Mo ML, Hannum G, Palsson BØ, Herrgard MJ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc. 2007, 2 (3): 727-38. 10.1038/nprot.2007.99

Kuhn HW, Tucker AW, Dantzig GB: Linear inequalities and related systems. 1956, 38: Princeton: Princeton University Press,

Mahadevan R, Schilling CH: The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng. 2003, 5 (4): 264-276. 10.1016/j.ymben.2003.09.002

Gudmundsson S, Thiele I: Computationally efficient flux variability analysis. BMC Bioinformatics. 2010, 11: 489- 10.1186/1471-2105-11-489

Gianchandani EP, Papin JA, Price ND, Joyce AR, Palsson BO: Matrix formalism to describe functional states of transcriptional regulatory systems. PLoS Comput Biol. 2006, 2 (8): e101- 10.1371/journal.pcbi.0020101

Turi TG, Loper JC: Multiple regulatory elements control expression of the gene encoding the Saccharomyces cerevisiae cytochrome P450, lanosterol 14 alpha-demethylase (ERG11). J Biol Chem. 1992, 267 (3): 2046-2056.

Becker SA, Palsson BO: Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol. 2008, 4 (5): e1000082- 10.1371/journal.pcbi.1000082

Shlomi T, Cabili MN, Herrgård MJ, Palsson BØ, Ruppin E: Network-based prediction of human tissue-specific metabolism. Nat Biotechnol. 2008, 26 (9): 1003-10. 10.1038/nbt.1487