A Comparison Between Threshold Ergodic Sets and Stochastic Simulation of Boolean Networks for Modelling Cell Differentiation

  • Michele Braccini
  • Andrea Roli
  • Marco Villani
  • Roberto Serra
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 830)

Abstract

Recently a cell differentiation model based on noisy random Boolean networks has been proposed. This mathematical model is able to describe in an elegant way the most relevant features of cell differentiation. Noise plays a key role in this model; the different stages of the differentiation process are emergent dynamical configurations deriving from the control of the intracellular noise level. In this work we compare two approaches to this cell differentiation framework: the first one (already present in the literature) is focused on a network analysis representing the average wandering of the system among its attractors, whereas the second (new) approach takes into consideration the dynamical stories of thousands of individual cells. Results showed that under a particular noise condition the two approaches produce comparable results. Therefore both can be used to model the cell differentiation process in an integrative and complementary manner.

References

  1. 1.
    Bastolla, U., Parisi, G.: A numerical study of the critical line of Kauffman networks. J. Theor. Biol. 187(1), 117–133 (1997)CrossRefGoogle Scholar
  2. 2.
    Benedettini, S., Roli, A., Serra, R., Villani, M.: Automatic design of Boolean networks for modelling cell differentiation. In: Cagnoni, S., Mirolli, M., Villani, M. (eds.) Evolution, Complexity and Artificial Life, vol. 708, pp. 77–89. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-37577-4_5CrossRefGoogle Scholar
  3. 3.
    Braccini, M., Roli, A., Villani, M., Serra, R.: Automatic design of Boolean networks for cell differentiation. In: Rossi, F., Piotto, S., Concilio, S. (eds.) WIVACE 2016. CCIS, vol. 708, pp. 91–102. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57711-1_8CrossRefGoogle Scholar
  4. 4.
    Holland, M.L.: Epigenetic regulation of the protein translation machinery. EBioMedicine 17, 3–4 (2017)CrossRefGoogle Scholar
  5. 5.
    Huang, S.: The molecular and mathematical basis of Waddington’s epigenetic landscape: a framework for post-darwinian biology? Bioessays 34(2), 149–157 (2012)CrossRefGoogle Scholar
  6. 6.
    Kauffman, S.A.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Milo, R., Jorgensen, P., Moran, U., Weber, G., Springer, M.: Bionumbers-the database of key numbers in molecular and cell biology. Nucleic Acids Res. 38(Suppl. 1), D750–D753 (2009)Google Scholar
  8. 8.
    Paroni, A., Graudenzi, A., Caravagna, G., Damiani, C., Mauri, G., Antoniotti, M.: CABeRNET: a cytoscape app for augmented Boolean models of gene regulatory NETworks. BMC Bioinform. 17, 64–75 (2016)CrossRefGoogle Scholar
  9. 9.
    Peláez, N., Gavalda-Miralles, A., Wang, B., Navarro, H.T., Gudjonson, H., Rebay, I., Dinner, A.R., Katsaggelos, A.K., Amaral, L.A., Carthew, R.W.: Dynamics and heterogeneity of a fate determinant during transition towards cell differentiation. Elife 4, e08924 (2015)CrossRefGoogle Scholar
  10. 10.
    Ribeiro, A.S., Kauffman, S.A.: Noisy attractors and ergodic sets in models of gene regulatory networks. J. Theor. Biol. 247(4), 743–755 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Serra, R., Villani, M., Barbieri, A., Kauffman, S., Colacci, A.: On the dynamics of random boolean networks subject to noise: attractors, ergodic sets and cell types. J. Theor. Biol. 265(2), 185–193 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Villani, M., Barbieri, A., Serra, R.: A dynamical model of genetic networks for cell differentiation. PloS ONE 6(3), e17703 (2011)CrossRefGoogle Scholar
  13. 13.
    Villani, M., Serra, R.: On the dynamical properties of a model of cell differentiation. J. Bioinform. Syst. Biol. 2013(1), 4 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAlma Mater Studiorum, Università di BolognaBolognaItaly
  2. 2.Department of Physics, Informatics and MathematicsUniversità di Modena e Reggio EmiliaModenaItaly
  3. 3.European Centre for Living TechnologyVeniceItaly

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