Phenotype–genotype relation in Wagner's canalization model

Cunff, Yann Le; Pakdaman, Khashayar

doi:10.1016/j.jtbi.2012.08.020

Cited by 20 publications

(23 citation statements)

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“…For instance, the sensitivity to mutations can decrease (i.e. genetic canalization can increase) as a by-product of stabilizing selection on gene expression at the end of the development [15, 18]. Using a similar framework, Siegal & Bergman [16] suggested that selection on the stability of development also leads to higher canalization, even in the absence of stabilizing selection for a phenotypic optimum.…”

Section: Introductionmentioning

confidence: 99%

Why and how genetic canalization evolves in gene regulatory networks

Rünneburger

Rouzic

2016

BMC Evol Biol

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BackgroundGenetic canalization reflects the capacity of an organism’s phenotype to remain unchanged in spite of mutations. As selection on genetic canalization is weak and indirect, whether or not genetic canalization can reasonably evolve in complex genetic architectures is still an open question. In this paper, we use a quantitative model of gene regulatory network to describe the conditions in which substantial canalization is expected to emerge in a stable environment.ResultsThrough an individual-based simulation framework, we confirmed that most parameters associated with the network topology (complexity and size of the network) have less influence than mutational parameters (rate and size of mutations) on the evolution of genetic canalization. We also established that selecting for extreme phenotypic optima (nil or full gene expression) leads to much higher canalization levels than selecting for intermediate expression levels. Overall, constrained networks evolve less canalization than networks in which some genes could evolve freely (i.e. without direct stabilizing selection pressure on gene expression).ConclusionsTaken together, these results lead us to propose a two-fold mechanism involved in the evolution of genetic canalization in gene regulatory networks: the shrinkage of mutational target (useless genes are virtually removed from the network) and redundancy in gene regulation (so that some regulatory factors can be lost without affecting gene expression).Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-016-0801-2) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Why and how genetic canalization evolves in gene regulatory networks

Rünneburger

Rouzic

2016

BMC Evol Biol

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“…This resulted in individuals that rapidly attained developmental stability, and had lower sensitivity to mutation. Implementing this model in population simulations has been a popular way to study epistasis (non linear interactions between the alleles at different loci) and complex genetic interactions and it has been used to explore the effects of mutation, recombination, genetic drift, and environmental selection in a network context (e.g., Bergman and Siegal, 2003; Masel, 2004; Azevedo et al, 2006; MacCarthy and Bergman, 2007; Borenstein et al, 2008; Draghi and Wagner, 2009; Espinosa Soto and Wagner, 2010; Espinosa Soto et al, 2011; Fierst, 2011a,b; Le Cunff and Pakdaman, 2012). Several groups (Huerta Sanchez and Durrett, 2007; Sevim and Rikvold, 2008; Pinho et al, 2012) have analyzed the model to identify how networks change when they transition from oscillatory dynamics to developmental stability.…”

Section: The Wagner Gene Networkmentioning

confidence: 99%

Modeling the evolution of complex genetic systems: The gene network family tree

Fierst

Phillips

2014

J. Exp. Zool. (Mol. Dev. Evol.)

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In 1994 and 1996, Andreas Wagner introduced a novel model in two papers addressing the evolution of genetic regulatory networks. This work, and a suite of papers that followed using similar models, helped integrate network thinking into biology and motivate research focused on the evolution of genetic networks. The Wagner network has its mathematical roots in the Ising model, a statistical physics model describing the activity of atoms on a lattice, and in neural networks. These models have given rise to two branches of applications, one in physics and biology and one in artificial intelligence and machine learning. Here, we review development along these branches, outline similarities and differences between biological models of genetic regulatory circuits and neural circuits models used in machine learning, and identify ways in which these models can provide novel insights into biological systems.

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“…We have previously described the standard WM in detail, with explanatory diagrams for its various components and stages (Le Cunff and Pakdaman, 2012). In the interest of readability we summarize key ingredients of that model here.…”

Section: The Wagner Model and Reproduction Costmentioning

confidence: 99%

“…This model has been the foundation of a broad range of studies (Wagner, 1994(Wagner, , 1996Siegal and Bergman, 2002;Bergman and Siegal, 2003;Masel, 2004;Azevedo et al, 2006;Ciliberti et al, 2007aCiliberti et al, , 2007bMacCarthy and Bergman, 2007;Draghi and Wagner, 2009;Martin and Wagner, 2009;Lohaus et al, 2010;Espinosa-Soto et al, 2011a, 2011bLe Cunff and Pakdaman, 2012), including the evolution of robustness and reproduction, two key factors that influence demographic stochasticity. Yet, in the previous versions of the Wagner Model (WM), population size was fixed, which implies that the number of reproductive attempts was not limited.…”

Section: Introductionmentioning

confidence: 99%