Abstract
The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lazebnik Y (2002) Can a biologist fix a radio? Or, what I learned while studying apoptosis. Cancer Cell 2(3):179–182
Mendes P, Kell D (1998) Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 14(10):869–883. doi:10.1093/bioinformatics/14.10.869
Szallasi Z, Stelling J, Periwal V (2006) System modeling in cellular biology: from concepts to nuts and bolts, 1st edn. The MIT Press, Boston
Covert MW, Famili I, Palsson BO (2003) Identifying constraints that govern cell behavior: a key to converting conceptual to computational models in biology? Biotechnol Bioeng 84(7):763–772. doi:Doi10.1002/Bit.10849
Price ND, Reed JL, Palsson BO (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2(11):886–897. doi:Doi10.1038/Nrmicro1023
Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14(5):491–496. doi:Doi10.1016/j.copbio.2003.08.001
Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 107(41):17845–17850. doi:Doi10.1073/pnas.1005139107
Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429(6987):92–96
Herrgard MJ, Lee BS, Portnoy V, Palsson BO (2006) Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res 16(5):627–635. doi:Doi10.1101/Gr.4083206
Covert MW, Schilling CH, Palsson B (2001) Regulation of gene expression in flux balance models of metabolism. J Theor Biol 213(1):73–88. doi:Doi10.1006/jtbi.2001.2405
Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5(4):264–276. doi:Doi10.1016/j.ymben.2003.09.002
Young IT (1977) Proof without prejudice—Use of Kolmogorov-Smirnov test for analysis of histograms from flow systems and other sources. J Histochem Cytochem 25(7):935–941
Feist AM, Herrgard MJ, Thiele I, Reed JL, Palsson BO (2009) Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 7(2):129–143. doi:Doi10.1038/Nrmicro1949
Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D (2007) How to infer gene networks from expression profiles. Mol Syst Biol 3:122. doi:Artn 78.doi: Doi 10.1038/Msb4100120
Davidson EH, Rast JP, Oliveri P, Ransick A, Calestani C, Yuh CH, Minokawa T, Amore G, Hinman V, Arenas-Mena C, Otim O, Brown CT, Livi CB, Lee PY, Revilla R, Rust AG, Pan ZJ, Schilstra MJ, Clarke PJC, Arnone MI, Rowen L, Cameron RA, McClay DR, Hood L, Bolouri H (2002) A genomic regulatory network for development. Science 295(5560):1669–1678
Schlitt T, Brazma A (2007) Current approaches to gene regulatory network modelling. BMC Bioinformatics 8. doi:Artn S9. doi: Doi 10.1186/1471-2105-8-S6-S9
Shen-Orr SS, Milo R, Mangan S, Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31(1):64–68. doi:Doi10.1038/Ng881
Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, Young RA (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298(5594):799–804
Chandrasekaran S, Ament SA, Eddy JA, Rodriguez-Zas SL, Schatz BR, Price ND, Robinson GE (2011) Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states. Proc Natl Acad Sci U S A 108(44):18020–18025. doi:Doi10.1073/pnas.1114093108
Shmulevich I, Dougherty ER, Kim S, Zhang W (2002) Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18(2):261–274
Shlomi T, Cabili MN, Herrgard MJ, Palsson BO, Ruppin E (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26(9):1003–1010. doi:Doi10.1038/Nbt.1487
Acknowledgments
We acknowledge funding from the Grand Duchy of Luxembourg for ES and NDP, a NIH Howard Temin Pathway to Independence Award in Cancer Research, an NSF CAREER grant, and the Camille Dreyfus Teacher-Scholar Program for NDP, and a Howard Hughes Medical Institute Predoctoral Fellowship for SC.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Simeonidis, E., Chandrasekaran, S., Price, N.D. (2013). A Guide to Integrating Transcriptional Regulatory and Metabolic Networks Using PROM (Probabilistic Regulation of Metabolism). In: Alper, H. (eds) Systems Metabolic Engineering. Methods in Molecular Biology, vol 985. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-299-5_6
Download citation
DOI: https://doi.org/10.1007/978-1-62703-299-5_6
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-298-8
Online ISBN: 978-1-62703-299-5
eBook Packages: Springer Protocols