Abstract
In the living cells, molecules including proteins, DNAs, RNAs and so on, with interactions between them cooperate as networks that govern various cellular functions. In this paper, a stochastic model with trigger mechanism is proposed based on what are known about the genes and proteins controlling the cell cycle of budding yeast. With respect to the biological observations, it looks more natural and understandable than deterministic dynamical model and our former stochastic model. Our model vividly describes that the protein interaction network goes through the biological pathway and forms an endless loop.
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References
Zweiger, G.: Knowledge Discovery in Gene-Expression-Microarray Data: Mining the Information Output of the Genome. Trends Biotechnol. 17(11), 429–436 (1999)
Ito, T., Chiba, T., Ozawa, R., et al.: A Comprehensive Two-hybrid Analysis to Explore the Yeast Protein Interactome. Proc. Natl. Acad. Sci. USA 98(8), 4569–4574 (2001)
Chen, K.C., Csikasz-Nagy, A., Gyorffy, B., et al.: Kinetic Analysis of a Molecular Model of the Budding Yeast Cell Cycle. Mol. Biol. Cell 11(1), 369–391 (2000)
Cross, F.R., Archambault, V., Miller, M., et al.: Testing a Mathematical Model of the Yeast Cell Cycle. Mol. Biol. Cell 13(1), 52–70 (2002)
Li, F., Long, T., Lu, Y., et al.: The Yeast Cell-Cycle Network is Robustly Designed. Proc. Natl. Acad. Sci. USA 101(14), 4781–4786 (2004)
Chen, H.C., Lee, H.C., Lin, T.Y., et al.: Quantitative Characterization of the Transcriptional Regulatory Network in the Yeast Cell Cycle. Bioinformatics 20(12), 1914–1927 (2004)
Chen, K.C., Calzone, L., Csikasz-Nagy, A., et al.: Integrative Analysis of Cell Cycle Control in Budding Yeast. Mol. Biol. Cell 15(8), 3841–3862 (2004)
Cross, F.R., Schroeder, L., Kruse, M., et al.: Quantitative Characterization of a Mitotic Cyclin Threshold Regulating Exit from Mitosis. Mol. Biol. Cell 16(5), 2129–2138 (2005)
Futcher, B.: Transcriptional Regulatory Networks and the Yeast Cell Cycle. Curr. Opin. Cell Biol. 14(6), 676–683 (2002)
Murray, A.W.: Recycling the Cell Cycle: Cyclins Revisited. Cell 116(2), 221–234 (2004)
Ingolia, N.T., Murray, A.W.: The Ups and Downs of Modeling the Cell Cycle. Curr. Biol. 14(18), R771–R777 (2004)
Tyers, M.: Cell Cycle Goes Global. Curr. Opin. Cell Biol. 16(6), 602–613 (2004)
Zhang, Y., Qian, M., Ouyang, Q., et al.: Stochastic Model of Yeast Cell Cycle Netwok (in appear)
Ao, P.: Potential in Stochastic Differential Equations: Novel Construction. Journal of Physics A: Mathematical and General (3), L25–L30 (2004)
Zhu, X.M., Yin, L., Hood, L., et al.: Calculating Biological Behaviors of Epigenetic States in the Phage λ Life Cycle. Funct. Integr. Genomics 4(3), 188–195 (2004)
Albeverio, S., Feng, J., Qian, M.: Role of Noises in Neural Networks. Physical Review. E. Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 52(6), 6593–6606 (1995)
Jiang, D., Qian, M.: Mathematical Theory of Nonequilibrium Steady States. Springer, Heidelberg (2004)
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Zhang, Y., Yu, H., Deng, M., Qian, M. (2006). Nonequilibrium Model for Yeast Cell Cycle. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_84
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DOI: https://doi.org/10.1007/11816102_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
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