The Underlying Molecular and Network Level Mechanisms in the Evolution of Robustness in Gene Regulatory Networks

Pujato, Mario; MacCarthy, Thomas; Fiser, András; Bergman, Aviv

doi:10.1371/journal.pcbi.1002865

Cited by 17 publications

(15 citation statements)

References 61 publications

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“…Other recent explorations following the same philosophy, such as the deterministic model of gene expression developed by Pujato et al. (), also demonstrate the value of mechanistic models that capture multiple aspects of gene effects. We expect that our ENTWINE model will allow the investigation of many novel questions about the evolution of robustness and phenotypic variability.…”

Section: Discussionmentioning

confidence: 87%

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Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Draghi

Whitlock

2015

Evolution

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Stochastic noise in gene expression causes variation in the development of phenotypes, making such noise a potential target of stabilizing selection. Here, we develop a new simulation model of gene networks to study the adaptive landscape underlying the evolution of robustness to noise. We find that epistatic interactions between the determinants of the expression of a gene and its downstream effect impose significant constraints on evolution, but these interactions do allow the gradual evolution of increased robustness. Despite strong sign epistasis, adaptation rarely proceeds via deleterious intermediate steps, but instead occurs primarily through small beneficial mutations. A simple mathematical model captures the relevant features of the single-gene fitness landscape and explains counterintuitive patterns, such as a correlation between the mean and standard deviation of phenotypes. In more complex networks, mutations in regulatory regions provide evolutionary pathways to increased robustness. These results chart the constraints and possibilities of adaptation to reduce expression noise and demonstrate the potential of a novel modeling framework for gene networks.

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Section: Discussionmentioning

confidence: 87%

“…; Pujato et al. ). However, few evolutionary models have considered noise, and most of the exceptions (Ciliberti et al.…”

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confidence: 96%

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Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Draghi

Whitlock

2015

Evolution

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“…Computational models such as the gene regulatory network evolution model (also known as the Wagner model), that combine a complex genotype-phenotype mapping (describing a gene regulatory network) with evolutionary dynamics have previously been used to address a range of questions concerned with evolution of biological complexity [33, 34]. In previous studies, the gene regulatory network evolution model has been extended to account for different system levels, including transcription factor (TF)-DNA binding interactions [35] and protein-protein interactions (PPI) [36] at the microscopic level, or between two different populations [10] at the macroscopic level. These previous studies [10, 36] showed how robustness and evolvability can evolve to be distributed across different system levels, depending on the model conditions.…”

Section: Introductionmentioning

confidence: 99%

Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution

Shin

MacCarthy

2016

BMC Evol Biol

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BackgroundHost resistance and viral pathogenicity are determined by molecular interactions that are part of the evolutionary arms race between viruses and their hosts. Viruses are obligate intracellular parasites and entry to the host cell is the first step of any virus infection. Commonly, viruses enter host cells by binding cell surface receptors. We adopt a computational modeling approach to study the evolution of the first infection step, where we consider two possible levels of resistance mechanism: at the level of the binding interaction between the host receptor and a virus binding protein, and at the level of receptor protein expression where we use a standard gene regulatory network model. At the population level we adopted the Susceptible-Infected-Susceptible (SIS) model. We used our multi-scale model to understand what conditions might determine the balance between use of resistance mechanisms at the two different levels.ResultsWe explored a range of different conditions (model parameters) that affect host evolutionary dynamics and, in particular, the balance between the use of different resistance mechanisms. These conditions include the complexity of the receptor binding protein-protein interaction, selection pressure on the host population (pathogenicity), and the number of expressed cell-surface receptors. In particular, we found that as the receptor binding complexity (understood as the number of amino acids involved in the interaction between the virus entry protein and the host receptor) increases, viruses tend to become specialists and target one specific receptor. At the same time, on the host side, the potential for resistance shifts from the changes at the level of receptor binding (protein-protein) interaction towards changes at the level of gene regulation, suggesting a mechanism for increased biological complexity.ConclusionsHost resistance and viral pathogenicity depend on quite different evolutionary conditions. Viruses may evolve cell entry strategies that use small receptor binding regions, represented by low complexity binding in our model. Our modeling results suggest that if the virus adopts a strategy based on binding to low complexity sites on the host receptor, the host will select a defense strategy at the protein (receptor) level, rather than at the level of the regulatory network - a virus-host strategy that appears to have been selected most often in nature.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-016-0804-z) contains supplementary material, which is available to authorized users.

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“…The core GRNs that control the developmental fates of diverse cells are composed of specific sets of transcription factors (TFs), epigenetic regulators, signaling mediators, and post‐transcriptional regulators that include noncoding regulatory RNAs. These GRN components dynamically regulate each other in different regulatory loops to confer robustness to cell fate decisions . Thus, rewiring/reprogramming of cellular GRNs is expected to render different cell fates interchangeable.…”

Section: Introductionmentioning

confidence: 99%

Concise Review: Harmonies Played by MicroRNAs in Cell Fate Reprogramming

2014

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It is now well-established that somatic cells can be reprogrammed to alternative cell fates by ectopic coexpression of defined factors. Reprogramming technology has uncovered a huge plasticity besides gene regulatory networks (GRNs) of differentiated cell states. MicroRNAs (miRNAs), which are an integral part of GRNs, have recently emerged as a powerful reprogramming toolbox. They regulate numerous genes, thereby modulating virtually all cellular processes, including somatic cell reprogramming. Not only can miRNAs provide novel opportunities for interrogating mechanisms of induced pluripotency and direct lineage reprogramming but they also offer hope for the efficient creation of safe cell sources for regenerative medicine. In reviewing landmark roles of miRNAs in cell reprogramming, we offer suggestions for evolution of the reprogramming field. STEM CELLS 2014;32:3-15

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The Underlying Molecular and Network Level Mechanisms in the Evolution of Robustness in Gene Regulatory Networks

Cited by 17 publications

References 61 publications

Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution

Concise Review: Harmonies Played by MicroRNAs in Cell Fate Reprogramming

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