A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation

ACS Synthetic Biology

Volumn 4, Issue 8, 2015, Pages 880-889

  Angione, Claudio ^a   Pratanwanich, Naruemon ^a   Lió, Pietro ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Bayesian factor modeling; Escherichia coli; flux balance analysis; multiomics; pathway correlation; temporal pathway activation

Indexed keywords

GLUCOSE; MESSENGER RNA;

ANALYTICAL ERROR; ARTICLE; BAYESIAN LEARNING; BIOMASS; ESCHERICHIA COLI; GENE EXPRESSION; GLUCOSE TRANSPORT; METABOLIC FLUX ANALYSIS; METABOLISM; METHODOLOGY; NONHUMAN; PRIORITY JOURNAL; BAYES THEOREM; BIOLOGICAL MODEL; GENETICS; METABOLOME; PHYSIOLOGY;

ESCHERICHIA COLI;

BAYES THEOREM; ESCHERICHIA COLI; METABOLOME; MODELS, BIOLOGICAL;

EID: 84939810702     PISSN: None     EISSN: 21615063     Source Type: Journal    
DOI: 10.1021/sb5003407     Document Type: Article

References (40)

1. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., and Lander, E. S. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles Proc. Natl. Acad. Sci. U.S.A. 102, 15545-15550
2. Hood, L. (2013) Systems biology and p4 medicine: past,present and future Rambam Maimonides Med. J. 4, e0012
3. Wang, T., Gu, J., Yuan, J., Tao, R., Li, Y., and Li, S. (2013) Inferring pathway crosstalk networks using gene set co-expression signatures Mol. BioSyst. 9, 1822-1828
4. Li, Y., Agarwal, P., and Rajagopalan, D. (2008) A global pathway crosstalk network Bioinformatics 24, 1442-1447
5. Pratanwanich, N. and Lió, P. (2014) Pathway-based Bayesian inference of drug-disease interactions Mol. BioSyst. 10, 1538-1548
6. Pan, X.-H. (2012) Pathway crosstalk analysis based on protein-protein network analysis in ovarian cancer Asian Pac. J. Cancer Prev. 13, 3905-3909
7. Kim, H. U., Kim, T. Y., and Lee, S. Y. (2011) Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network BMC Syst. Biol. 5, S14
8. Scott, M., Gunderson, C., Mateescu, E., Zhang, Z., and Hwa, T. (2010) Interdependence of cell growth and gene expression: origins and consequences Science 330, 1099-1102
9. Lee, D., Smallbone, K., Dunn, W., Murabito, E., Winder, C., Kell, D., Mendes, P., and Swainston, N. (2012) Improving metabolic flux predictions using absolute gene expression data BMC Syst. Biol. 6, 73
10. Bordbar, A., Monk, J. M., King, Z. A., and Palsson, B. O. (2014) Constraint-based models predict metabolic and associated cellular functions Nat. Rev. Genet. 15, 107-120
11. von Kamp, A. and Klamt, S. (2014) Enumeration of smallest intervention strategies in genome-scale metabolic networks PLoS Comput. Biol. 10, e1003378
12. Chindelevitch, L., Trigg, J., Regev, A., and Berger, B. (2014) An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models Nat. Commun. 5, 4893
13. Edwards, J. S., Covert, M., and Palsson, B. (2002) Metabolic modelling of microbes: the flux-balance approach Environ. Microbiol. 4, 133-140
14. Angione, C., Carapezza, G., Costanza, J., Lió, P., and Nicosia, G. (2013) Pareto optimality in organelle energy metabolism analysis IEEE/ACM Trans. Comput. Biol. Bioinf. 10, 1032-1044
15. Costanza, J., Carapezza, G., Angione, C., Lió, P., and Nicosia, G. (2012) Robust design of microbial strains Bioinformatics 28, 3097-3104
16. Angione, C., Costanza, J., Carapezza, G., Lió, P., and Nicosia, G. (2013) A design automation framework for computational bioenergetics in biological networks Mol. BioSyst. 9, 2554-2564
17. Potera, C. (2005) Making succinate more successful Environ. Health Perspect. A833-A835
18. Dittrich, C. R., Bennett, G. N., and San, K.-Y. (2005) Characterization of the acetate-producing pathways in Escherichia coli Biotechnol. Prog. 21, 1062-1067
19. Faith, J. J., Driscoll, M. E., Fusaro, V. A., Cosgrove, E. J., Hayete, B., Juhn, F. S., Schneider, S. J., and Gardner, T. S. (2008) Many microbe microarrays database: uniformly normalized Affymetrix compendia with structured experimental metadata Nucleic Acids Res. 36, D866-D870
20. Orth, J., Conrad, T., Na, J., Lerman, J., Nam, H., Feist, A., and Palsson, B. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011 Mol. Syst. Biol. 7, 535
21. Mar, J. C., Matigian, N. A., Mackay-Sim, A., Mellick, G. D., Sue, C. M., Silburn, P. A., McGrath, J. J., Quackenbush, J., and Wells, C. A. (2011) PLoS Genet. 7, e1002207
22. Firczuk, H., Kannambath, S., Pahle, J., Claydon, A., Beynon, R., Duncan, J., Westerhoff, H., Mendes, P., and McCarthy, J. E. (2013) An in vivo control map for the eukaryotic mRNA translation machinery Mol. Syst. Biol. 9, 635
23. Paltanea, M., Tabirca, S., Scheiber, E., and Tangney, M. (2010) Logarithmic Growth in Biological Processes, pp 116-121, 12th International Conference on Computer Modelling and Simulation (UKSim), Cambridge, March 24-26.
24. Guimaraes, J. C., Rocha, M., and Arkin, A. P. (2014) Transcript level and sequence determinants of protein abundance and noise in Escherichia coli Nucleic Acids Res. 42, 4791-4799
25. Csárdi, G., Franks, A., Choi, D. S., Airoldi, E. M., and Drummond, D. A. (2014) Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast bioRxiv 009472
26. Rue, H. and Held, L. (2005) Gaussian Markov Random Fields: Theory and Applications, CRC Press, Boca Raton, FL.
27. Wu, G. (2009) Amino acids: metabolism, functions, and nutrition Amino Acids 37, 1-17
28. Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J. J., and Gardner, T. S. (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles PLoS Biol. 5, e8
29. Kleman, G. L. and Strohl, W. R. (1994) Acetate metabolism by Escherichia coli in high-cell-density fermentation Appl. Environ. Microbiol. 60, 3952-3958
30. Wolfe, A. J. (2005) The acetate switch Microbiol. Mol. Biol. Rev. 69, 12-50
31. Kotte, O., Volkmer, B., Radzikowski, J. L., and Heinemann, M. (2014) Phenotypic bistability in Escherichia coli's central carbon metabolism Mol. Syst. Biol. 10, 736
32. Martinez-Antonio, A. and Collado-Vides, J. (2003) Identifying global regulators in transcriptional regulatory networks in bacteria Curr. Opin. Microbiol. 6, 482-489
33. Rolfe, M. D., Ocone, A., Stapleton, M. R., Hall, S., Trotter, E. W., Poole, R. K., Sanguinetti, G., and Green, J. (2012) Systems analysis of transcription factor activities in environments with stable and dynamic oxygen concentrations Open Biol. 2, 120091
34. Trageser, M. and Unden, G. (1989) Role of cysteine residues and of metal ions in the regulatory functioning of FNR, the transcriptional regulator of anaerobic respiration in Escherichia coli Mol. Microbiol. 3, 593-599
35. Hopper, D. and Cooper, R. (1972) The purification and properties of Escherichia coli methylglyoxal synthase Biochem. J. 128, 321-329
36. Pepper, E. D., Farrell, M. J., Nord, G., and Finkel, S. E. (2010) Antiglycation effects of carnosine and other compounds on the long-term survival of Escherichia coli Appl. Environ. Microbiol. 76, 7925-7930
37. Ferguson, G. P. (1999) Protective mechanisms against toxic electrophiles in Escherichia coli Trends Microbiol. 7, 242-247
38. + efflux system in Escherichia coli Mol. Microbiol. 27, 563-571
39. Carrera, J., Estrela, R., Luo, J., Rai, N., Tsoukalas, A., and Tagkopoulos, I. (2014) An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli Mol. Syst. Biol. 10, 735
40. Kohanski, M. A., Dwyer, D. J., and Collins, J. J. (2010) How antibiotics kill bacteria: from targets to networks Nat. Rev. Microbiol. 8, 423-435