Abstract
A well-known hypothesis, with far-reaching implications, is that biological evolution should preferentially lead to critical dynamic regimes. Useful information about the dynamical regime of gene regulatory networks can be obtained by studying their responses to small perturbations. The interpretation of these data requires the use of suitable models, where it is usually assumed that the system is homogeneous. On the other hand, it is widely acknowledged that biological networks display some degree of modularity, so it is interesting to ascertain how modularity can affect the estimation of their dynamical properties. In this study we introduce a well-defined degree of modularity and we study how it influences the network dynamics. In particular, we show how the estimate of the Derrida parameter from “avalanche” data may be affected by strong modularity.
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Notes
- 1.
Note that in such a way the activation function of every node is kept unchanged, the only alteration being the source of the input link. In this way the process preserves the contribution of the nodes to the overall dynamic regime.
- 2.
All links are chosen in order to prevent multiple redirections.
- 3.
In any case, the behavior of networks with different number of nodes has been considered without noting qualitatively different behaviors.
- 4.
That is, 100 different networks, each network measured in 10000 initial conditions for the Derrida parameter estimate, and 20000 different networks in order to obtain the avalanche distribution.
References
Kauffman, S.A.: The Origins of Order. Oxford University Press, New York (1993)
Kauffman, S.A.: At Home in the Universe. Oxford University Press, New York (1995)
Benedettini, S., et al.: Dynamical regimes and learning properties of evolved Boolean networks. Neurocomputing 99, 111–123 (2013)
Langton, C.G.: Computation at the edge of chaos: phase transitions and emergent computation. Physica D 42(1–3), 12–37 (1990)
Langton, C.G.: Life at the edge of chaos. In: Langton, C.G., Taylor, C., Farmer, J.D., Rasmussen, S. (eds.) Artificial Life II, pp. 41–91. Addison-Wesley, Reading (1992)
Packard, N.H.: Adaptation toward the edge of chaos. In: Dynamic Patterns in Complex Systems, pp. 293–301. World Scientific (1988)
Shmulevich, I., Kauffman, S.A., Aldana, M.: Eukaryotic cells are dynamically ordered or critical but not chaotic. PNAS 102(38), 13439–13444 (2005)
Bar-Yam, Y.: Dynamics of Complex Systems. Addison-Wesley, Reading (1997)
Nicolis, G., Nicolis, C.: Foundations of Complex Systems: Nonlinear Dynamics, Statistical Physics, Information and Prediction. World Scientific, Singapore (2007)
Aldana, M., Coppersmith, S., Kadanoff, L.P.: Boolean dynamics with random couplings. In: Kaplan, E., Marsden, J.E., Sreenivasan, K.R. (eds.) Perspectives and Problems in Nonlinear Science, pp. 23–89. Springer, Heidelberg (2003). https://doi.org/10.1007/978-0-387-21789-5_2
Kaneko, K.: Life: An Introduction to Complex Systems Biology. Springer, New York (2006)
Babu, M.M., Luscombe, N.M., Aravind, L., Gerstein, M., Teichmann, S.A.: Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol. 14(3), 283–291 (2004)
Bastolla, U., Parisi, G.: The modular structure of Kauffman networks. Physica D 115(3–4), 219–233 (1998a)
Bastolla, U., Parisi, G.: Relevant elements, magnetization and dynamical properties in Kauffman (1998b)
Hughes, T.R., Marton, M.J., Jones, A.R., et al.: Functional discovery via a compendium of expression profiles. Cell 102(1), 109–126 (2000)
Kemmeren, P., Sameith, K., van de Pasch, L.A.L., et al.: Largescale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell 157(3), 740–752 (2014)
Serra, R., Villani, M.: Semeria A Genetic network models and statistical properties of gene expression data in knock-out experiments. J. Theor. Biol. 227, 149–157 (2004)
Serra, R., Villani, M., Graudenzi, A., Kauffman, S.A.: Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. J. Theor. Biol. 246(3), 449–460 (2007)
Serra, R., Villani, M., Graudenzi, A., Colacci, A., Kauffman, S.A.: The simulation of gene knock-out in scale-free random boolean models of genetic networks. Netw. Heterogen. Media 3(2), 333–343 (2008)
Di Stefano, M.L., Villani, M., La Rocca, L., Kauffman, S.A., Serra, R.: Dynamically critical systems and power-law distributions: avalanches revisited. In: Rossi, F., Mavelli, F., Stano, P., Caivano, D. (eds.) WIVACE 2015. CCIS, vol. 587, pp. 29–39. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32695-5_3
Villani, M., La Rocca, L., Kauffman, S.A., Serra, R.: Dynamical criticality in gene regulatory networks. Complexity 2018, 14 pages, Article ID 5980636 (2018)
Derrida, B., Pomeau, Y.: Random networks of automata: a simple annealed approximation. Europhys. Lett. 1(2), 45–49 (1986)
Derrida, B., Flyvbjerg, H.: The random map model: a disordered model with deterministic dynamics. J. Phys. 48(6), 971–978 (1987)
Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabasi, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)
Shen-Orr, S.S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31, 64–68 (2002)
Gyorgy, A., Del Vecchio, D.: Modular composition of gene transcription networks. PLoS Comput. Biol. 10(3), e1003486 (2014)
Damiani, C., Kauffman, S.A., Serra, R., Villani, M., Colacci, A.: Information transfer among coupled random boolean networks. In: Bandini, S., Manzoni, S., Umeo, H., Vizzari, G. (eds.) ACRI 2010. LNCS, vol. 6350, pp. 1–11. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15979-4_1
Serra, R., Villani, M., Barbieri, B., Kauffman, S.A., Colacci, A.: On the dynamics of random boolean networks subject to noise: attractors, ergodic sets and cell types. J. Theoret. Biol. 265, 185–193 (2010)
Villani, M, Barbieri, A, Serra, R.: A dynamical model of genetic networks for cell differentiation. PLoS ONE 6(3), e17703 (2011). https://doi.org/10.1371/journal.pone.0017703
Glass, L., Kauffman, S.A.: The logical analysis of continuous, non-linear biochemical control networks. J. Theoret. Biol. 39(1), 103–129 (1973)
Serra, R., Villani, M., Salvemini, A.: Continuous genetic networks. Parallel Comput. 27, 663–683 (2001)
Graudenzi, A., Serra, R., Villani, M., Damiani, C., Colacci, A., Kauffman, S.A.: Dynamical properties of a Boolean model of gene regulatory network with memory. J. Comput. Biol. 18, 1291–1303 (2011)
Graudenzi, A., Serra, R., Villani, M., Colacci, A., Kauffman, S.A.: Robustness analysis of a Boolean model of gene regulatory network with memory. J. Comput. Biol. 18(4), 559–577 (2011)
Sapienza, D., Villani, M., Serra, R.: Dynamical properties of a gene-protein model. In: Pelillo, M., Poli, I., Roli, A., Serra, R., Slanzi, D., Villani, M. (eds.) WIVACE 2017. CCIS, vol. 830, pp. 142–152. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78658-2_11
Guido, N.J., Wang, X., Adalsteinsson, D., McMillen, D., Hasty, J., et al.: A bottom-up approach to gene regulation. Nature 439, 856–860 (2006)
Purnick, P.E.M.: Weiss R The second wave of synthetic biology: from modules to systems. Nat. Rev. Mol. Cell Biol. 10, 410–422 (2009)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
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Vezzani, A., Villani, M., Serra, R. (2020). Avalanches of Perturbations in Modular Gene Regulatory Networks. In: Cicirelli, F., Guerrieri, A., Pizzuti, C., Socievole, A., Spezzano, G., Vinci, A. (eds) Artificial Life and Evolutionary Computation. WIVACE 2019. Communications in Computer and Information Science, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-45016-8_3
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