Robustness in biological regulatory networks II: Application to genetic threshold Boolean random regulatory networks (getBren)

Demongeot, Jacques; Waku, Jules

doi:10.1016/j.crma.2012.01.019

Cited by 11 publications

(17 citation statements)

References 14 publications

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“…More systematic studies have to be performed in order to confirm the dominant influence of boundary negative interactions, thanks to which the Hopfield-like regulatory interaction networks seem to become more robust [21][22], and also to make more precise their influence on the number of attractors, which is conjectured to diminish, when microRNAs are multiple on the boundary of the interaction graph of the network. …”

Section: Discussionmentioning

confidence: 95%

“…We will use in the following the notion of genetic threshold Boolean random regulatory network (getBren), which is a set N of n random automata defined as follows [13][14][15][16][17][18][19][20][21][22]: 1) any random automaton i of the getBren N owns at time t a state x i (t) valued in {0,1}, 0 (resp. 1) meaning that gene i is inactivated (resp.…”

Section: Micrornas Chromatine Clock and Geneticmentioning

confidence: 99%

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MitomiRs and Energetic Regulation

Demongeot

Cohen

Doncescu

et al. 2013

2013 27th International Conference on Advanced Information Networking and Applications Workshops

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MicroRNAs are parts of nuclear and mitochondrial genomes which pertain to the not coding genome. They have a partly unspecific role of inhibition, preventing the weakest part of the genetic regulatory networks to be expressed and preventing the appearance of a too large number of attractors in these networks, i.e., forcing the network to have only one or two possible dynamic behaviours for fulfilling a precise function. They have also a great influence on the chromatin clock, which ensures the real mode of updating of genetic regulatory networks. We analyze this influence as well as their impact on important functions like p53 network control.

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Section: Discussionmentioning

confidence: 95%

Section: Micrornas Chromatine Clock and Geneticmentioning

confidence: 99%

MitomiRs and Energetic Regulation

Demongeot

Cohen

Doncescu

et al. 2013

2013 27th International Conference on Advanced Information Networking and Applications Workshops

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“…in which we found the quadratic term of the classical models of contagion. This quadratic term is also present in the interaction potential of Hopfield like networks in which the study of the robustness with respect to the contagion parameter changes has been performed [59][60][61][62][63][64][65][66][67][68][69] as well as in recent studies taking into account the spatial character of the disease spread. [70][71][72][73][74][75][76][77][78] Confinement and Saturation The localization of contamination has been treated by different authors.…”

Section: The Dynamics Of Contactsmentioning

confidence: 99%

Discrete dynamics of contagious social diseases: Example of obesity

2015

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Modeling contagious diseases needs to incorporate information about social networks through which the disease spreads as well as data about demographic and genetic changes in the susceptible population. In this paper, we propose a theoretical framework (conceptualization and formalization) which seeks to model obesity as a process of transformation of one's own body determined by individual (physical and psychological), inter-individual (relational, i.e., relative to the relationship between the individual and others) and socio-cultural (environmental, i.e., relative to the relationship between the individual and his milieu) factors. Individual and inter-individual factors are tied to each other in a socio-cultural context whose impact is notably related to the visibility of anybody being exposed on the public stage in a non-contingent way. The question we are dealing with in this article is whether such kind of social diseases, i.e., depending upon socio-environmental exposure, can be considered as "contagious". In other words, can obesity be propagated from individual to individual or from environmental sources throughout an entire population?

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“…Let μ = (μ β ) = (μ({β})) be the stationary distribution or invariant measure (over the space of configurations Ω = {0, 1} n , if the network has n genes) of the Markov probability transition matrix P = (P β γ ) γ ,β∈Ω of a getBren observed asymptotically from an initial measure uniform on Ω. Then, the evolutionary entropy H serving as robustness index of the getBrens can be explicitly calculated [10]:…”

Section: Entropy and Robustness In Getbrensmentioning

confidence: 99%

“…doi:10.1016/j.crma.2012.01.002 entropy of the network [9]. We will in this paper apply the main concepts developed in [9,10], underlying the relationship between complexity and stability in the context of a particular genetic threshold Boolean regulatory network (getBren), the network controlling the feather morphogenesis in chicken.…”

Section: Introductionmentioning

confidence: 99%