Generic properties of random gene regulatory networks

Quantitative Biology - Tập 1 - Trang 253-260 - 2014
Zhiyuan Li1,2, Simone Bianco2, Zhaoyang Zhang3, Chao Tang1
1Center for Quantitative Biology, School of Physics and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
2Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA
3Department of Physics, Beijing Normal University, Beijing, China

Tóm tắt

Modeling gene regulatory networks (GRNs) is an important topic in systems biology. Although there has been much work focusing on various specific systems, the generic behavior of GRNs with continuous variables is still elusive. In particular, it is not clear typically how attractors partition among the three types of orbits: steady state, periodic and chaotic, and how the dynamical properties change with network’s topological characteristics. In this work, we first investigated these questions in random GRNs with different network sizes, connectivity, fraction of inhibitory links and transcription regulation rules. Then we searched for the core motifs that govern the dynamic behavior of large GRNs. We show that the stability of a random GRN is typically governed by a few embedding motifs of small sizes, and therefore can in general be understood in the context of these short motifs. Our results provide insights for the study and design of genetic networks.

Tài liệu tham khảo

Kauffman, S., Peterson, C., Samuelsson, B. and Troein, C. (2003) Random Boolean network models and the yeast transcriptional network. Proc. Natl. Acad. Sci. U.S.A., 100, 14796–14799 Kapuy, O., He, E., López-Avilés, S., Uhlmann, F., Tyson, J. J. and Novák, B. (2009) System-level feedbacks control cell cycle progression. FEBS Lett., 583, 3992–3998 Perkins, T. J., Hallett, M. and Glass, L. (2006) Dynamical properties of model gene networks and implications for the inverse problem. Biosystems, 84, 115–123 Sneppen, K., Krishna, S. and Semsey, S. (2010) Simplified models of biological networks. Annu. Rev. Biophys., 39, 43–59 Tsai, T. Y., Choi, Y. S., Ma, W., Pomerening, J. R., Tang, C. and Ferrell, J. E. Jr. (2008) Robust, tunable biological oscillations from interlinked positive and negative feedback loops. Science, 321, 126–129 Li, F., Long, T., Lu, Y., Ouyang, Q. and Tang, C. (2004) The yeast cellcycle network is robustly designed. Proc. Natl. Acad. Sci. U.S.A., 101, 4781–4786 Kauffman, S. A. (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol., 22, 437–467 Kadanoff, L., Coppersmith, S. and Aldana, M. (2002) Boolean dynamics with random couplings. Nlin, 0204062 Shmulevich, I., Kauffman, S. A. and Aldana, M. (2005) Eukaryotic cells are dynamically ordered or critical but not chaotic. Proc. Natl. Acad. Sci. U.S.A., 102, 13439–13444 Kappler, K., Edwards, R. and Glass, L. (2003) Dynamics in highdimensional model gene networks. Signal Process., 83, 789–798 Shea, M. A. and Ackers, G. K. (1985) The OR control system of bacteriophage lambda. A physical-chemical model for gene regulation. J. Mol. Biol., 181, 211–230 Buchler, N. E., Gerland, U. and Hwa, T. (2003) On schemes of combinatorial transcription logic. Proc. Natl. Acad. Sci. U.S.A., 100, 5136–5141 Albert, R. and Othmer, H. G. (2003) The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J. Theor. Biol., 223, 1–18 Zhang, Y., Qian, M., Ouyang, Q., Deng, M., Li, F. and Tang, C. (2006) Stochastic model of yeast cell-cycle network. Physica D, 219, 35–39 Ma, W., Trusina, A., El-Samad, H., Lim, W. A. and Tang, C. (2009) Defining network topologies that can achieve biochemical adaptation. Cell, 138, 760–773 Ma, W., Lai, L., Ouyang, Q. and Tang, C. (2006) Robustness and modular design of the Drosophila segment polarity network. Mol. Syst. Biol., 2, 70 Zhang, Z., Ye, W., Qian, Y., Zheng, Z., Huang, X. and Hu, G. (2012) Chaotic motifs in gene regulatory networks. PLoS ONE, 7, e39355. PMID:22792171 Ehud Kaplan, J. E. M. and Katepalli, R. (Sreenivasan Eds).(2003) In Perspectives and Problems in Nonlinear Science. A celebratory volume in honor of Lawrence Sirovich Wang, J., Zhang, K., Xu, L. and Wang, E. (2011) Quantifying the Waddington landscape and biological paths for development and differentiation. Proc. Natl. Acad. Sci. U.S.A., 108, 8257–8262 Alon, U. (2007) An Introduction to Systems Biology: Design Principles of Biological Circuits. CRC Press Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. and Futcher, B. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell, 9, 3273–3297 Ko, C. H. and Takahashi, J. S. (2006) Molecular components of the mammalian circadian clock. Hum. Mol. Genet., 15, R271–R277 Zhang, E. E. and Kay, S. A. (2010) Clocks not winding down: unravelling circadian networks. Nat. Rev. Mol. Cell Biol., 11, 764–776 Tyson, J. J., Novak, B. (2008) Temporal organization of the cell cycle. Current Biology, 18, 759–768 Seshadhri, C., Vorobeychik, Y., Mayo, J. R., Armstrong, R. C. and Ruthruff, J. R. (2011) Influence and dynamic behavior in random boolean networks. Phys. Rev. Lett., 107, 108701 Glass, L. and Hill, C. (1998) Ordered and disordered dynamics in random networks. Europhys. Lett., 41, 599–604 Mestl, T., Bagley, R. J. and Glass, L. (1997) Common chaos in arbitrarily complex feedback networks. Phys. Rev. Lett., 79, 653–656 Wainrib, G. and Touboul, J. (2013) Topological and dynamical complexity of random neural networks. Phys. Rev. Lett., 110, 118101