Evolving Boolean regulatory networks with epigenetic control

Bull, Larry

doi:10.1016/j.biosystems.2013.12.004

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…Arguably, each model of NGI brings forth a new property, affecting more or less the course of evolution. A single NGI mechanism may have effects on various scales; for instance, the impact of DNA methylation on evolution may be modelled at the network scale (Bull, ) or at the population scale (Slatkin, ), and may (or not) take into account an increase in mutation rate (Klironomos et al ., ) or ability to be reset between generations (Tal et al ., ). Therefore, whereas Mendelian heredity as a general mechanism can be studied with a global approach, the various NGI systems cannot all be modelled at once in a single abstraction.…”

Section: Discussionmentioning

confidence: 99%

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Modelling the influence of parental effects on gene‐network evolution

Odorico

Rünneburger

Rouzic

2018

J of Evolutionary Biology

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Understanding the importance of nongenetic heredity in the evolutionary process is a major topic in modern evolutionary biology. We modified a classical gene-network model by allowing parental transmission of gene expression and studied its evolutionary properties through individual-based simulations. We identified ontogenetic time (i.e. the time gene networks have to stabilize before being submitted to natural selection) as a crucial factor in determining the evolutionary impact of this phenotypic inheritance. Indeed, fast-developing organisms display enhanced adaptation and greater robustness to mutations when evolving in presence of nongenetic inheritance (NGI). In contrast, in our model, long development reduces the influence of the inherited state of the gene network. NGI thus had a negligible effect on the evolution of gene networks when the speed at which transcription levels reach equilibrium is not constrained. Nevertheless, simulations show that intergenerational transmission of the gene-network state negatively affects the evolution of robustness to environmental disturbances for either fast- or slow-developing organisms. Therefore, these results suggest that the evolutionary consequences of NGI might not be sought only in the way species respond to selection, but also on the evolution of emergent properties (such as environmental and genetic canalization) in complex genetic architectures.

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Section: Discussionmentioning

confidence: 99%

“…To this end, various theoretical approaches have been proposed, most of them focusing on DNA-carried modifications (e.g. methylation or chromatin state; Slatkin, 2009;Klironomos et al, 2013;Bull, 2014;Uller et al, 2015). These models thus involve the transmission of genetic-like information along with DNA.…”

Section: Introductionmentioning

confidence: 99%

Modelling the influence of parental effects on gene‐network evolution

Odorico

Rünneburger

Rouzic

2018

J of Evolutionary Biology

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“…The artificial [1] However, elements of the work described within this thesis which has been previously published (Turner et al, 2013b) have been acknowledged as inspiration for new epigenetically inspired networks (Bull, 2013) epigenetic network developed throughout this thesis has been applied to a range of control tasks. The performance of the networks and the analysis of their structure and dynamical properties have resulted in the following conclusions about their functionality…”

Section: Discussionmentioning

confidence: 99%

The artificial epigenetic network

Turner

Lones

Fuente

et al. 2013

2013 IEEE International Conference on Evolvable Systems (ICES)

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The term epigenetics refers to typically heritable biological mechanisms which facilitate stable yet reversible modifications of gene expression or phenotype state, without alteration of the underlying genetic code. More specifically, epigenetic mechanisms allow organisms to control which genes are active at a given time. In eukaryotes, epigenetic mechanisms have essential roles in gene regulation, cellular differentiation and genetic packaging. These epigenetic mechanisms give rise to functionality which DNA alone is generally incapable of providing.This thesis takes inspiration from the fields of genetics and epigenetics, and builds a computational model which captures the beneficial properties of epigenetics in silico. This computational model is referred to as the artificial epigenetic network. The artificial epigenetic network can dynamically control which genes within the network are active at a given time, allowing certain groups of genes to become specialised towards specific aspects of a task.Hence, the artificial epigenetic network can contain many different regulatory circuits, each with specific properties. This gives the networks the ability to more readily express a wider range of dynamical behaviours, which were found to produce a number computational benefits. The artificial epigenetic network is applied to a diverse range of control tasks, each with varying dynamics, to ascertain how the functionality of the artificial epigenetic structures effects the functionality of the network. An emergent property is that the epigenetic structures can partition the network into functional units corresponding to the logical decomposition of the tasks, and control these units with a switch like behaviour. This provides an interface, where a user can gain control over the complex dynamics of the target domain via the activation or deactivation of these switches.3

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“…Several works making use of mathematical models of specific aspects of epigenetic processes are present in the literature (Bull 2014;Miyamoto et al 2015;Turner et al 2017Turner et al , 2013; however, to the best of our knowledge, there is no systematic attempt to study the capabilities of reproducing differentiation dynamics by methylation-like mechanisms in modelling. A noteworthy model of differentiation, based on an entirely different mechanism (i.e.…”

Section: Methylation Modelmentioning

confidence: 99%

Dynamical properties and path dependence in a gene-network model of cell differentiation

et al. 2020

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In this work, we explore the properties of a control mechanism exerted on random Boolean networks that takes inspiration from the methylation mechanisms in cell differentiation and consists in progressively freezing (i.e. clamping to 0) some nodes of the network. We study the main dynamical properties of this mechanism both theoretically and in simulation. In particular, we show that when applied to random Boolean networks, it makes it possible to attain dynamics and path dependence typical of biological cells undergoing differentiation.

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Evolving Boolean regulatory networks with epigenetic control

Cited by 6 publications

References 27 publications

Modelling the influence of parental effects on gene‐network evolution

Modelling the influence of parental effects on gene‐network evolution

The artificial epigenetic network

Dynamical properties and path dependence in a gene-network model of cell differentiation

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