The simulation of gene knock-out in scale-free random Boolean models of genetic networks
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Dipartimento di scienze sociali, cognitive e quantitative, Università di Modena e Reggio Emilia, Via Allegri 9, 42100 Reggio Emilia
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Excellence Environmental Carcinogenesis, Environmental Protection and Health Prevention Agency, Emilia-Romagna, viale Filopanti 22, Bologna
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Institute for Biocomplexity and Informatics, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4
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Received:
01 August 2007
Revised:
01 February 2008
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Primary: 92D10, 37B99; Secondary: 92C40, 94C15.
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This paper describes the effects of perturbations, which simulate the knock-out of single genes, one at a time, in random Boolean models of genetic networks (RBN). The analysis concentrates on the probability distribution of so-called avalanches (defined in the text) in gene expression. The topology of the random Boolean networks considered here is of the scale-free type, with a power-law distribution of outgoing connectivities. The results for these scale-free random Boolean networks (SFRBN) are compared with those of classical RBNs, which had been previously analyzed, and with experimental data on S. cerevisiae. It is shown that, while both models approximate the main features of the distribution of experimental data, SFRBNs tend to overestimate the number of large avalanches.
Citation: Roberto Serra, Marco Villani, Alex Graudenzi, Annamaria Colacci, Stuart A. Kauffman. The simulation of gene knock-out in scale-free random Boolean models of genetic networks[J]. Networks and Heterogeneous Media, 2008, 3(2): 333-343. doi: 10.3934/nhm.2008.3.333
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Abstract
This paper describes the effects of perturbations, which simulate the knock-out of single genes, one at a time, in random Boolean models of genetic networks (RBN). The analysis concentrates on the probability distribution of so-called avalanches (defined in the text) in gene expression. The topology of the random Boolean networks considered here is of the scale-free type, with a power-law distribution of outgoing connectivities. The results for these scale-free random Boolean networks (SFRBN) are compared with those of classical RBNs, which had been previously analyzed, and with experimental data on S. cerevisiae. It is shown that, while both models approximate the main features of the distribution of experimental data, SFRBNs tend to overestimate the number of large avalanches.
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