Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks

PLoS Computational Biology

Volumn 9, Issue 11, 2013, Pages

  Berestovsky, Natalie ^a   Zhou, Wanding ^a   Nagrath, Deepak ^a   Nakhleh, Luay ^a  

^a Rice University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINERY; METABOLISM; PHYSIOLOGY; SIGNAL TRANSDUCTION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

BEHAVIOUR CHANGES; BOOLEAN NETWORKS; CELL-BE; CELL/B.E; CELL/BE; CELLULAR CIRCUITRY; CELLULAR MACHINERY; CELLULARS; INTEGRATED NETWORKS; PHENOTYPIC CHANGES;

PETRI NETS;

ARTICLE; BIOLOGICAL MODEL; BOOLEAN NETWORK; CONTROLLED STUDY; GLUCOSE METABOLISM; INTEGRATED HYBRID MODEL; MATHEMATICAL MODEL; OSMOREGULATION; PETRI NET; QUALITATIVE ANALYSIS; SACCHAROMYCES CEREVISIAE; SIMULATION; STOCHASTIC MODEL; TRANSCRIPTION REGULATION;

COMPUTATIONAL BIOLOGY; GLUCOSE; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; OSMOREGULATION; REPRODUCIBILITY OF RESULTS; SACCHAROMYCES CEREVISIAE; SIGNAL TRANSDUCTION; TRANSCRIPTION, GENETIC;

EID: 84888243694     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003306     Document Type: Article

References (72)

1. Desvergne B, Michalik L, Wahli W, (2006) Transcriptional regulation of metabolism. Physical Reviews 86: 465-514.
2. Komurov K, Tseng JT, Muller M, Seviour EG, Moss TJ, et al. (2012) The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant erbb2-positive breast cancer cells. Molecular Systems Biology 8: 596.
3. Zhu J, Sova P, Xu Q, Dombek KM, Xu EY, et al. (2012) Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biology 10: e1001301.
4. Eckel-Mahana KL, Patelb VR, Mohneyc RP, Vignolac KS, Baldib P, et al. (2011) Coordination of the transcriptome and metabolome by the circadian clock. Proceedings of the National Academy of Sciences 109: 5541-5546.
5. Wynn ML, Merajver SD, Schnell S, (2012) Unraveling the complex regulatory relationships between metabolism and signal transduction in cancer. Advances in Experimental Medicine and Biology 736: 179-189.
6. Wei H, Persson S, Mehta T, Srinivasasainagendra V, Chen L, et al. (2006) Transcriptional coordination of the metabolic network in arabidopsis. Plant Physiology 142: 762-774.
7. Covert MW, Palsson BO, (2002) Transcriptional regulation in constraints-based metabolic models of Escherichia coli. The Journal of biological chemistry 277: 28058-64.
8. Shlomi T, EisenBerg Y, Sharan R, Ruppin E, (2007) A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Molecular Systems Biology 3: 101.
9. Covert MW, Xiao N, Chen TJ, Karr JR, (2008) Integrating metabolic, transciptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24: 2044-2050.
10. Lee JM, Gianchandani EG, Eddy JA, Papin JA, (2008) Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Computational Biology 4: e1000086.
11. Chandrasekaran S, Price ND, (2003) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in escherichia coli and mycobacterium tuberculosis. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 107: 17845-17850.
12. Richard G, Chang H, Cizelj I, Julius AA, Amar S (2011) Integration of large-scale metabolic, signaling, and gene regulatory networks with application to infection responses. In: 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC). pp. 2227-2232.
13. Feng X, Xu Y, Chen Y, Tang YJ, (2012) Integrating flux balance analysis into kinetic models to decipher the dynamic metabolism of Shewanella oneidensis MR-1. PLoS Computation Biology 8: e1002376.
14. Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, et al. (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150: 389-401.
15. Orth JD, Thiele I, Palsson B, (2010) What is flux balance analysis? Nature Biotechnology 28: 245-248.
16. Gianchandani EP, Brautigan DL, Papin JA, (2006) Systems analyses characterize integrated functions of biochemical networks. Trends in Biochemical Sciences 31: 284-291.
17. Tenazinha N, Vinga S, (2011) A survey on methods for modeling and analyzing integrated biological networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8: 943-958.
18. Fisher J, Henzinger TA, (2007) Executable cell biology. Computational Biology 25: 1239-1249.
19. Koch I, Reisig W, Schreiber F, editors(2011) Modeling in Systems Biology: The Petri net approach. Springer.
20. Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA, (2010) Logic-based models for the analysis of cell signaling networks. Biochemistry 49: 3216-24.
21. Berestovsky N, Nakhleh L, (2013) An evaluation of methods for inferring Boolean networks from time-series data. PloS One 8: e66031.
22. Reddy VN, Mavrovouniotis ML, Liebman MN (1993) Petri net representation in metabolic pathways. In: 1st International Conference on Intelligent Systems for Molecular Biology (ISMB-93). AAAI, pp. 328-336.
23. Reddy VN, Liebman MN, Mavrovouniotis ML, (1996) Qualitative analysis of biochemical reaction systems. Computational Biology 26: 9-24.
24. Heiner M, Koch I (2004) Petri net based models validation in systems biology. In: 25th International Conference on Application and Theory of Petri Nets (ICATPN), Springer-Verlag Berlin Heidelberg, LNCS. pp. 216-237.
25. Genrich H, Kuffner R, Voss K (2001) Executable Petri net models for the analysis of metabolic pathways. In: International Journal of Software Tools for Technology Transfers (STTT), Springer-Link, volume 3. pp. 394-404.
26. Hofestadt R, Thelen S, (1998) Quantitative modeling of biochemical networks. In Silico Biology 1: 39-53.
27. Samarrai W, Barjis I, Yeol JW, Ryu Y (2005) Modeling of carbohydrate metabolism: From dietary carbohydrate to pyruvic acid by Petri-nets (PN). In: Bioengineering Conference, 2005. Proceedings of the IEEE 31st Annual Northeast. pp. 267-268.
28. Gilbert D, Breitling R, Heiner M, Donaldson R (2009) An introduction to biomodel engineering, illustrated for signal transduction pathways. In: Proc. 9th International Workshop on Membrane Computing (WMC 2008). Springer, volume LNCS 5391, pp. 13-28.
29. Hecht M (2009). Modelling of signal transduction pathways. Available: http://www.bio.ifi.lmu.de/webfm_send/340.
30. Li C, Ge QW, Nakata M, Matsuno H, Miyano S, (2007) Modeling and simulation of signaling transduction in an apoptosis pathway by using Petri nets. Journal of Biosciences 32: 113-127.
31. Ruths D, Nakhleh L, Ram P, (2008) Rapidly exploring structural and dynamic properties of signaling networks using pathwayoracle. BMC Systems Biology 2: 76.
32. Ruths D, Muller M, Tseng JT, Nakhleh L, Ram P, (2008) The signaling Petri net-based simulator: A non- parametric strategy for characterizing the dynamics of cell-specific signaling networks. PLoS Computational Biology 4: e1000005.
33. Kauffman SA, (1969) Metabolic stability and epidenesis in randomly constructed genetic nets. Journal of Theoretical Biology 22: 437-467.
34. Thomas R, (1979) Kinetic logic: a boolean approach to the analysis of complex regulatory systems. Lecture Notes in Biomathematics 29: 507.
35. Akutsu T, Miyano S, (1999) Identification of genetic networks from a small number of gene expression patterns under the boolean network model. Pacific Symposium on Biocomputing 4: 17-28.
36. Silvescu A, Honavar V, (1997) Termporal Boolean network models of generic networks and their inference from gene expression time series. Complex Systems 11.
37. Hohmann S, (2012) Osmotic stress signaling and osmoadaptation in yeasts. Microbiology and Molecular Biology Reviews 66: 300-372.
38. Peterson J (1981) Petri net theory and the modeling of systems. Upper Saddle River, NJ, USA: Prentice Hall PTR.
39. Bobbio A (1990) System Modeling with Petri nets. Ph.D. thesis, Instituto Elettrotecnico Nazionale Galileo Ferraris, Torino, Italy.
40. Chaouiya C, (2007) Petri net modeling of biological networks. Briefings in Bioinformatics 8: 210-219.
41. Gillespie D, (1976) A general method for numerically simulating stochastic time evolution of coupled chemical reactions. Journal of Computational Physics 22: 403-434.
42. Wang J (1998) Timed Petri nets: Theory and application, volume 39. Kluwer Academic Publishers Dordrecht.
43. Boulton TG, Nye SH, Robbins DJ, Ip NY, Radzlejewska E, et al. (1991) ERKs: A family of proteinserine/threonine kinases that are activated and tyrosine phosphorylated in response to insulin and NGF. Cell 65: 663-675.
44. Dalle S, Longuet C, Costes S, Broca C, Faruque O, et al. (2004) Glucagon promotes cAMP-response element-binding protein phosphorylation via activation of ERK1/2 in MIN6 cell line and isolated islets of Langerhans. The Journal of Biological Chemistry 279: 20345-55.
45. Kimball SR, Siegfried BA, Jefferson LS, (2004) Glucagon represses signaling through the mammalian target of rapamycin in rat liver by activating AMP-activated protein kinase. The Journal of Biological Chemistry 279: 54103-9.
46. Glauser DA, Schlegel W, (2007) The emerging role of FOXO transcription factors in pancreatic beta cells. The Journal of Endocrinology 193: 195-207.
47. Kitamura T, Nakae J, Kitamura Y, Kido Y, Biggs WH, et al. (2002) The forkhead transcription factor Foxo1 links insulin signaling to Pdx1 regulation of pancreatic beta cell growth. The Journal of Clinical Investigation 110: 1839-47.
48. Liu SH, Rao DD, Nemunaitis J, Senzer N, Zhou G, et al. (2012) PDX-1 is a therapeutic target for pancreatic cancer, insulinoma and islet neoplasia using a novel RNA interference platform. PloS One 7: e40452.
49. Oliver-Krasinski JM, Kasner MT, Yang J, Crutchlow MF, Rustgi AK, et al. (2009) The diabetes gene Pdx1 regulates the transcriptional network of pancreatic endocrine progenitor cells in mice. The Journal of Clinical Investigation 119: 1888-98.
50. Bartoov-Shifman R, Hertz R, Wang H, Wollheim CB, Bar-Tana J, et al. (2002) Activation of the insulin gene promoter through a direct effect of hepatocyte nuclear factor 4 alpha. The Journal of Biological Chemistry 277: 25914-9.
51. Odom DT, Zizlsperger N, Gordon DB, Bell GW, Rinaldi NJ, et al. (2004) Control of pancreas and liver gene expression by HNF transcription factors. Science (New York, NY) 303: 1378-81.
52. Kulkarni RN, Kahn CR, (2004) Molecular biology. HNFs-linking the liver and pancreatic islets in diabetes. Science (New York, NY) 303: 1311-2.
53. Fruman DA, Meyers RE, Cantley LC, (1998) Phosphoinositide kinases. Annual Review of Biochemistry 67: 481-507.
54. Iynedjian PB, Roth RA, Fleischmann M, Gjinovci A, (2000) Activation of protein kinase B/cAkt in hepatocytes is sufficient for the induction of expression of the gene encoding glucokinase. The Biochemical Journal 351 (Pt 3) (): 621-7.
55. Korach-André M, Roth H, Barnoud D, Péan M, Péronnet F, et al. (2004) Glucose appearance in the peripheral circulation and liver glucose output in men after a large 13C starch meal. American Journal of Clinical Nutrition 80: 881-886.
56. Liu W, Tang F, (2008) Modeling a simplified regulatory system of blood glucose at molecular levles. Journal of Theoretical Biology 252: 608-620.
57. Aronoff SL, Berkowitz K, Shreiner B, Want L, (2004) Glucose metabolism and regulation: Beyond insulin and glucagon. Diabetes Spectrum 17: 183-190.
58. Nakae J, Biggs WH, Kitamura T, Cavenee WK, Wright CVE, et al. (2002) Regulation of insulin action and pancreatic beta-cell function by mutated alleles of the gene encoding forkhead transcription factor Foxo1. Nature Genetics 32: 245-253.
59. Kahn SE, Hull RL, Utzschneider KM, (2006) Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444: 840-6.
60. Kasuga M, (2006) Insulin resistance and pancreatic beta cell failure. The Journal of Clinical Investigation 116: 1756-60.
61. Hashimoto N, Kido Y, Uchida T, Asahara Si, Shigeyama Y, et al. (2006) Ablation of PDK1 in pancreatic beta cells induces diabetes as a result of loss of beta cell mass. Nature Genetics 38: 589-93.
62. Bernal-Mizrachi E, Fatrai S, James D Johnson MO, Otani K, Han Z, et al. (2004) Defective insulin secretion and increased susceptibility to experimental diabetes are induced by reduced akt activity in pancreatic islet β cells. The Journal of Clinical Investigation 114: 928-936.
63. Smith GC, Ong WK, Rewcastle GW, Kendall JD, Han W, et al. (2012) Effects of acutely inhibiting PI3K isoforms and mTOR on regulation of glucose metabolism in vivo. The Biochemical journal 442: 161-9.
64. Hersen P, McClean MN, Mahadevan L, Ramanathan S, (2008) Signal processing by the HOG MAP kinase pathway. Proceedings of the National Academy of Sciences of the United States of America 105: 7165-70.
65. Krantz M, Ahmadpour D, Ottosson LG, Warringer J, Waltermann C, et al. (2009) Robustness and fragility in the yeast high osmolarity glycerol (HOG) signal-transduction pathway. Molecular Systems Biology 5: 281.
66. Raitt DC, Posas F, Saito H, (2000) Yeast Cdc42 GTPase and Ste20 PAK-like kinase regulate Sho1-dependent activation of the Hog1 MAPK pathway. The EMBO Journal 19: 4623-31.
67. Tatebayashi K, Yamamoto K, Tanaka K, Tomida T, Maruoka T, et al. (2006) Adaptor functions of Cdc42, Ste50, and Sho1 in the yeast osmoregulatory HOG MAPK pathway. The EMBO Journal 25: 3033-44.
68. Li S, Ault A, Malone CL, Raitt D, Dean S, et al. (1998) The yeast histidine protein kinase, Sln1p, mediates phosphotransfer to two response regulators, Ssk1p and Skn7p. The EMBO Journal 17: 6952-62.
69. Rep M, Reiser V, Gartner U, Thevelein JM, Hohmann S, et al. (1999) Osmotic stress-induced gene expression in Saccharomyces cerevisiae requires Msn1p and the novel nuclear factor Hot1p. Molecular and Cellular Biology 19: 5474-85.
70. Bermejo C, Rodríguez E, García R, Rodríguez-Peña JM, Rodríguez de la Concepción ML, et al. (2008) The sequential activation of the yeast HOG and SLT2 pathways is required for cell survival to cell wall stress. Molecular Biology of the Cell 19: 1113-24.
71. Jacoby T, Flanagan H, Faykin A, Seto AG, Mattison C, et al. (1997) Two protein-tyrosine phosphatases inactivate the osmotic stress response pathway in yeast by targeting the mitogen-activated protein kinase, Hog1. The Journal of Biological Chemistry 272: 17749-55.
72. Ruths D, Nakhleh L (2010) Generating executable models from signaling network connectivity and semi-quantitative proteomic measurements. In: Computational Systems Bioinformatics Conference. volume 9, pp. 136-145.