Abstract
Kauffman’s random Boolean networks are abstract, high level models for dynamical behavior of gene regulatory networks. They simulate the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by its simplicity, suffered from fundamental shortcomings unveiled by the recent advances in genetics and biology. Using these new discoveries, the model can be improved to reflect current knowledge. Artificial topologies, such as scale-free or hierarchical, are now believed to be closer to that of gene regulatory networks. We have studied actual biological organisms and used parts of their genetic regulatory networks in our models. We also have addressed the improbable full synchronicity of the event taking place on Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.
Errata to this chapter can be found at http://dx.doi.org/10.1007/978-3-642-37577-4_1810.1007/978-3-642-37577-4_18
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References
Pearson, H.: Genetics: what is a gene? Nature 441(7092), 398–401 (2006)
Pennisi, E.: GENOMICS: DNA study forces rethink of what it means to be a gene. Science 316(5831), 1556–1557 (2007)
Nowak, MA.: Evolutionary Dynamics: Exploring the Equations of Life. Harvard University Press, Cambridge, MA (2006)
Albert, R.: Boolean modeling of genetic regulatory networks. In: Ben-Naim, E., Frauenfelder, H., Toroczkai, Z. (eds.) Complex Networks. Lecture Notes in Physics, vol. 650, pp. 459–479. Springer, Berlin (2004)
Bower, J.M., Bolouri, H. (eds.): Computational Modeling of Genetic and Biochemical Networks. MIT, Cambridge, MA (2001)
Edwards, R., Glass, L.: A calculus for relating the dynamics and structure of complex biological networks. In: Berry, R.S., Jortner, J. (eds.) Advances in Chemical Physics, vol. 132, pp. 151–178. Wiley, New York (2006)
Vohradsky, J.: Neural model of the genetic network. J. Biol. Chem. 276(39), 36168–36173 (2001)
Blake, W.J., Kaern, M., Cantor, C.R., Collins, J.J.: Noise in eukaryotic gene expression. Nature 422(6932), 633–637 (2003)
Elowitz, M.B., Levine, A.J., Siggia, E.D., Swain, P.S.: Stochastic gene expression in a single cell. Science 297(5584), 1183–1186 (2002)
Arkin, A., Ross, J., McAdams, H.H.: Stochastic kinetic analysis of developmental pathway bifurcation in phage {lambda}-infected escherichia coli cells. Genetics 149(4), 1633–1648 (1998)
Gardner, T.S., Cantor, C.R., Collins, J.J.: Construction of a genetic toggle switch in escherichia coli. Nature 403(6767), 339–342 (2000)
Elowitz, M.B., Leibler, S.: A synthetic oscillatory network of transcriptional regulators. Nature 403(6767), 335–338 (2000)
Raser, J.M., O’Shea, E.K.: Noise in gene expression: origins, consequences, and control. Science 309(5743), 2010–2013 (2005)
Leclerc, R.D.: Survival of the sparsest: robust gene networks are parsimonious. Mol. Syst. Biol. 4, 214–218 (2008)
Kauffman, S.A.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437–467 (1969)
Aldana, M.: Boolean dynamics of networks with scale-free topology. Phys. D. 185, 45–66 (2003)
Roussel, M.R., Zhu, R.: Validation of an algorithm for delay stochastic simulation of transcription and translation in prokaryotic gene expression. Phys. Biol. 3(4), 274–284 (2006)
Drossel, B.: Random boolean networks. Rev. Nonlinear Dynam. Complex. 1, 69–110 (2008)
Kauffman, S.A.: Investigations. Oxford University Press, New York (2000)
Serra, R., Villani, M., Agostini, L.: On the dynamics of random boolean networks with scale-free outgoing connections. Phys. A Stat. Mech. Appl. 339(3–4), 665–673 (2004)
Aldana, M., Cluzel, P.: A natural class of robust networks. Proc. Natl. Acad. Sci. USA 100(15), 8710–8714 (2003)
Darabos, C., Di Cunto, F., Tomassini, M., Moore, J.H., Provero, P., Giacobini, M.: Additive functions in boolean models of gene regulatory network modules. PLoS One 6(11), e25110 (2011)
Mesot, B., Teuscher, C.: Critical values in asynchronous random boolean networks. In: Banzhaf, W. (ed.) Advances in Artificial Life, ECAL2003. Lecture Notes in Artificial Intelligence, vol. 2801, pp. 367–376. Springer, Berlin (2003)
Gershenson, C.: Updating schemes in random Boolean networks: do they really matter? In: Pollack, J. (ed.) Artificial Life IX Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems, pp. 238–243. MIT, Cambridge, MA (2004)
Darabos, C., Tomassini, M., Giacobini, M.: Dynamics of unperturbed and noisy generalized boolean networks. J. Theor. Biol. 260(4), 531–544 (2009)
Li, F., Long, T., Lu, Y., Ouyang, Q., Tang, C.: The yeast cell-cycle network is robustly designed. Proc. Natl. Acad. Sci. USA 101(14), 4781–4786 (2004)
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, 449–460 (2007)
Aldana, M., Balleza, E., Kauffman, S.A., Resendiz, O.: Robustness and evolvability in genetic regulatory networks. J. Theor. Biol. 245, 433–448 (2007)
Kauffman, S.A.: The Origins of Order. Oxford University Press, Oxford (1993)
Acknowledgments
This work was partially supported by NIH grants LM009012, LM010098, AI59694, by the Swiss National Science Foundation grant PBLAP3-136923, and by Neuroscience Program of the Compagnia di San Paolo in Torino. The authors are grateful to Luca Ferreri for his precious help with statistical elaborations and the corresponding figures, and to Joshua L. Payne for his invaluable contribution to the discussions.
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Darabos, C., Giacobini, M., Moore, J.H., Tomassini, M. (2014). Models of Gene Regulation: Integrating Modern Knowledge into the Random Boolean Network Framework. In: Cagnoni, S., Mirolli, M., Villani, M. (eds) Evolution, Complexity and Artificial Life. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37577-4_3
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