In a previous study it was shown that a simple random Boolean network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1. © 2007 Elsevier Ltd. All rights reserved

Serra, R., Villani, M., Graudenzi, A., Kauffman, S. (2007). Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. JOURNAL OF THEORETICAL BIOLOGY, 246(3), 449-460 [10.1016/j.jtbi.2007.01.012].

Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data

Graudenzi, A
;
2007

Abstract

In a previous study it was shown that a simple random Boolean network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1. © 2007 Elsevier Ltd. All rights reserved
Articolo in rivista - Articolo scientifico
Avalanches; Cell criticality; Gene knock-out; Genetic network model; Random Boolean network; S. cerevisiae; Animals; Gene Expression Profiling; Gene Silencing; Humans; Oligonucleotide Array Sequence Analysis; Organisms, Genetically Modified; Saccharomyces cerevisiae; Gene Expression Regulation; Genes, Regulator; Models, Genetic; Neural Networks (Computer); Statistics and Probability; Modeling and Simulation; Biochemistry, Genetics and Molecular Biology (all); Immunology and Microbiology (all); Agricultural and Biological Sciences (all); Applied Mathematics; Computer Science Applications
English
449
460
12
Serra, R., Villani, M., Graudenzi, A., Kauffman, S. (2007). Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. JOURNAL OF THEORETICAL BIOLOGY, 246(3), 449-460 [10.1016/j.jtbi.2007.01.012].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/186567
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