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Rule-based modeling of transcriptional attenuation at the tryptophan operon. (English) Zbl 1275.92024
Priami, Corrado (ed.) et al., Transactions on Computational Systems Biology XII. Special issue on modeling methodologies. Berlin: Springer (ISBN 978-3-642-11711-4/pbk). Lecture Notes in Computer Science 5945. Lecture Notes in Bioinformatics. Journal Subline, 199-228 (2010).
Summary: Transcriptional attenuation at E.coli’s tryptophan operon is a prime example of RNA-mediated gene regulation. In this paper, we present a discrete stochastic model of the fine-grained control of attenuation, based on chemical reactions. Stochastic simulation of our model confirms results that were previously obtained by master or differential equations. Our approach is easier to understand than master equations, although mathematically well founded. It is compact due to rule schemas that define finite sets of chemical reactions. Object-centered languages based on the \(\pi \)-calculus would yield less intelligible models. Such languages are confined to binary interactions, whereas our model heavily relies on reaction rules with more than two reactants, in order to concisely capture the control of attenuation.
For the entire collection see [Zbl 1204.92037].

MSC:
92C42 Systems biology, networks
68Q85 Models and methods for concurrent and distributed computing (process algebras, bisimulation, transition nets, etc.)
92C40 Biochemistry, molecular biology
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[1] Arkin, A., Ross, J., McAdams, H.H.: Stochastic kinetic analysis of developmental pathway bifurcation in phage \(\lambda\)-infected Escherichia coli cells. Genetics 149, 1633–1648 (1998)
[2] Baader, F., Nipkow, T.: Term rewriting and all that. Cambridge University Press, New York (1998) · Zbl 0948.68098 · doi:10.1017/CBO9781139172752
[3] Barboric, M., Peterlin, B.M.: A new paradigm in eukaryotic biology: HIV Tat and the control of transcriptional elongation. PLoS Biology 3(2), 0200–2003 (2005) · doi:10.1371/journal.pbio.0030076
[4] Beisel, C.L., Smolke, C.D.: Design principles for riboswitch function. PLoS Computational Biology 5(4), e1000363, 04 (2009) · doi:10.1371/journal.pcbi.1000363
[5] Cardelli, L., Zavattaro, G.: On the computational power of biochemistry. In: Horimoto, K., Regensburger, G., Rosenkranz, M., Yoshida, H. (eds.) AB 2008. LNCS, vol. 5147, pp. 65–80. Springer, Heidelberg (2008) · Zbl 1171.92318 · doi:10.1007/978-3-540-85101-1_6
[6] Chabrier-Rivier, N., Fages, F., Soliman, S.: The biochemical abstract machine BioCham. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 172–191. Springer, Heidelberg (2005) · Zbl 1088.68817 · doi:10.1007/978-3-540-25974-9_14
[7] Ciocchetta, F., Hillston, J.: Bio-PEPA: a framework for modelling and analysis of biological systems. Theoretical Computer Science (to apppear) · Zbl 1173.68041
[8] Danos, V., Feret, J., Fontana, W., Krivine, J.: Scalable simulation of cellular signaling networks. In: Shao, Z. (ed.) APLAS 2007. LNCS, vol. 4807, pp. 139–157. Springer, Heidelberg (2007) · Zbl 05275792 · doi:10.1007/978-3-540-76637-7_10
[9] Danos, V., Feret, J., Fontana, W., Harmer, R., Krivine, J.: Rule-based modelling of cellular signalling. In: Caires, L., Vasconcelos, V.T. (eds.) CONCUR 2007. LNCS, vol. 4703, pp. 17–41. Springer, Heidelberg (2007) · Zbl 1151.68723 · doi:10.1007/978-3-540-74407-8_3
[10] Dematté, L., Priami, C., Romanel, A.: The beta workbench: A tool to study the dynamics of biological systems. Briefings in Bioinformatics 9(5), 437–449 (2008) · doi:10.1093/bib/bbn023
[11] Elf, J., Ehrenberg, M.: What makes ribosome-mediated trascriptional attenuation sensitive to amino acid limitation? PLoS Computational Biology 1(1), 14–23 (2005) · doi:10.1371/journal.pcbi.0010002
[12] Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics 22, 403–434 (1976) · doi:10.1016/0021-9991(76)90041-3
[13] Gollnick, P.: Trp operon and attenuation. In: Lennarz, W.J., Lane, M.D. (eds.) Encyclopedia of Biological Chemistry, pp. 267–271. Elsevier, New York (2004) · doi:10.1016/B0-12-443710-9/00247-7
[14] Gollnick, P., Babitzke, P., Antson, A., Yanofsky, C.: Complexity in regulation of tryptophan biosynthesis in Bacillus subtilis. Annual Review of Genetics 39(1), 47–68 (2005) · doi:10.1146/annurev.genet.39.073003.093745
[15] Gutierrez-Preciado, A., Jensen, R.A., Yanofsky, C., Merino, E.: New insights into regulation of the tryptophan biosynthetic operon in Gram-positive bacteria. Trends in Genetics 21(8), 432–436 (2005) · doi:10.1016/j.tig.2005.06.001
[16] Blinov, M.L., Faeder, J.R., Hlavacek, W.S.: Rule-Based Modeling of Biochemical Systems with BioNetGen. In: Systems Biology. Methods in Molecular Biology, vol. 500, pp. 1–55. Humana Press (2009)
[17] John, M., Lhoussaine, C., Niehren, J., Uhrmacher, A.M.: The attributed pi-calculus with priorities. Transactions on Computational Systems Biology (to appear, 2009) · Zbl 1275.92023
[18] Nakamura, Y., Roesser, J.R., Yanofsky, C.: Regulation of basal level expression of the tryptophan operon of Escherichia coli. J. Biol. Chem. 264(21), 12284–12288 (1989)
[19] Kasai, T.: Regulation of the expression of the histidine operon in Salmonella typhimurium. Nature 249, 523–527 (1974) · doi:10.1038/249523a0
[20] Konan, K.V., Yanofsky, C.: Role of ribosome release in regulation of tna operon expression in Escherichia coli. J. Bacteriol. 181, 1530–1536 (1999)
[21] Krivine, J., Milner, R., Troina, A.: Stochastic bigraphs. In: 24th Conference on the Mathematical Foundations of Programming Semantics. Electronical notes in theoretical computer science, vol. 218, pp. 73–96. Elsevier, Amsterdam (2008) · Zbl 1286.68354
[22] Kuttler, C., Lhoussaine, C., Niehren, J.: A stochastic pi calculus for concurrent objects. In: Anai, H., Horimoto, K., Kutsia, T. (eds.) AB 2007. LNCS, vol. 4545, pp. 232–246. Springer, Heidelberg (2007) · Zbl 1126.92003 · doi:10.1007/978-3-540-73433-8_17
[23] Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Heidelberg (1987) · Zbl 0668.68004 · doi:10.1007/978-3-642-83189-8
[24] Pedersen, M., Plotkin, G.: A language for biochemical systems. In: Priami, C., et al. (eds.) Trans. on Comput. Syst. Biol. XII. LNCS (LNBI), vol. 5945, pp. 77–145. Springer, Heidelberg (2010) · Zbl 1275.92020 · doi:10.1007/978-3-642-11712-1_3
[25] Phillips, A., Cardelli, L.: Efficient, correct simulation of biological processes in the stochastic pi-calculus. In: Calder, M., Gilmore, S. (eds.) CMSB 2007. LNCS (LNBI), vol. 4695, pp. 184–199. Springer, Heidelberg (2007) · Zbl 05282354 · doi:10.1007/978-3-540-75140-3_13
[26] Pradalier, S., Credi, A., Garavelli, M., Laneve, C., Zavattaro, G.: Modelization and simulation of nano devices in the nano-kappa calculus. In: Calder, M., Gilmore, S. (eds.) CMSB 2007. LNCS (LNBI), vol. 4695, pp. 168–183. Springer, Heidelberg (2007) · Zbl 05282353 · doi:10.1007/978-3-540-75140-3_12
[27] Ramsey, S., Orrell, D., Bolouri, H.: Dizzy: stochastic simulation of large-scale genetic regulatory networks. Journal of Bioinformatics and Computational Biology 3(2), 415–436 (2005) · Zbl 02216506 · doi:10.1142/S0219720005001132
[28] Regev, A.: Computational Systems Biology: A Calculus for Biomolecular Knowledge. Tel Aviv University, PhD thesis (2002)
[29] Roesser, J.R., Yanofsky, C.: Ribosome release modulates basal level expression of the trp operon of Escherichia coli. Journal of Biological Chemistry 263(28), 14251–14255 (1988)
[30] Santillan, M., Zeron, E.S.: Dynamic influence of feedback enzyme inhibition and transcription attenuation on the tryptophan operon response to nutritional shifts. Journal of Theoretical Biology 231(2), 287–298 (2004) · doi:10.1016/j.jtbi.2004.06.023
[31] Shieber, S.M.: An Introduction to Unification-Based Approaches to Grammar, vol. 4. CLSI Publications (1986) · Zbl 0770.68008
[32] Simão, E., Remy, E., Thieffry, D., Chaouiya, C.: Qualitative modelling of regulated metabolic pathways: application to the tryptophan biosynthesis in E.coli. In: ECCB/JBI, pp. 190–196 (2005)
[33] Trun, N., Trempy, J.: Gene expression and regulation. In: Fundamental bacterial genetics, pp. 191–212. Blackwell, Malden (2003)
[34] von Heijne, G., Nilsson, L., Blomberg, C.: Translation and messenger RNA secondary structure. Journal of Theoretical Biology 68, 321–329 (1977) · doi:10.1016/0022-5193(77)90063-7
[35] Yang, J., Monine, M.I., Faeder, J.R., Hlavacek, W.S.: Kinetic monte carlo method for rule-based modeling of biochemical networks. Physical Review E 78(3), 7 (2008)
[36] Yanofsky, C.: Attenuation in the control of expression of bacterial operons. Nature 289, 751–758 (1981) · doi:10.1038/289751a0
[37] Yanofsky, C.: Transcription attenuation: once viewed as a novel regulatory strategy. J. Bacteriology 182(1), 1–8 (2000) · doi:10.1128/JB.182.1.1-8.2000
[38] Yanofsky, C.: RNA-based regulation of genes of tryptophan synthesis and degradation, in bacteria. RNA - A publication of the RNA Society 13(8), 1141–1154 (2007) · doi:10.1261/rna.620507
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