Evolution Of Genetic Networks Research Articles

Review of Wagner’s Artificial Gene Regulatory Networks Model and its Applications for Understanding Complex Biological Systems

In the mid of 1990s, Andreas Wagner proposed a gene regulatory network model where the self-development process was explicitly modelled in the system. The many-to-one mapping mechanism of genotype to phenotype in Wagner’s GRN model enables genes to buffer against and even exploit likely variations in the genome. This mechanism is crucial for evolutionary innovations, because genotypes which control gene-gene interactions can change profoundly without affecting phenotypes which represent gene activities or expression concentrations. Wagner’s GRN model motivates research on the evolution of genetic networks and has been successfully employed to study many fundamental evolutionary and ecological questions. In this paper, I will review Wagner’s GRN model and currently available research papers that fall into its framework

New genes drive the evolution of gene interaction networks in the human and mouse genomes.

BackgroundThe origin of new genes with novel functions creates genetic and phenotypic diversity in organisms. To acquire functional roles, new genes must integrate into ancestral gene-gene interaction (GGI) networks. The mechanisms by which new genes are integrated into ancestral networks, and their evolutionary significance, are yet to be characterized. Herein, we present a study investigating the rates and patterns of new gene-driven evolution of GGI networks in the human and mouse genomes.ResultsWe examine the network topological and functional evolution of new genes that originated at various stages in the human and mouse lineages by constructing and analyzing three different GGI datasets. We find a large number of new genes integrated into GGI networks throughout vertebrate evolution. These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions. We identify a few human lineage-specific hub genes that have evolved brain development-related functions. Finally, we explore the possible underlying mechanisms driving the GGI network evolution and the observed patterns of new gene integration process.ConclusionsOur results unveil a remarkable network topological integration process of new genes: over 5000 new genes were integrated into the ancestral GGI networks of human and mouse; new genes gradually acquire increasing number of gene partners; some human-specific genes evolved into hub structure with critical phenotypic effects. Our data cast new conceptual insights into the evolution of genetic networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0772-4) contains supplementary material, which is available to authorized users.

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

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.

Control Flow Analysis of Generalised Boolean Networks

Generalised Boolean Networks are a well known qualitative model used to analyse the evolution of genetic networks as well as generic biological pathways. Despite the qualitative abstraction due to the few threshold concentration values considered for each biological element in the model, the complexity of the execution of a Generalised Boolean model could be non trivial. In this paper, we propose a tailored process algebra, called Sim-πn, reminiscent of the π-calculus to model GBNs. We further apply the Control Flow Analysis methodology to the resulting computational model for making static (and therefore less computationally expensive) predictions on the dynamical evolution of the investigated networks. The scope is twofold: helping in the setting up of the model, for checking its completeness, and checking the evolution of the model, in terms of the possibility to reach particular threshold values of the biological elements in the model, when varying the initial conditions.

Evolution of Genetic Networks Underlying the Emergence of Thymopoiesis in Vertebrates

Evolution of Genetic Networks Underlying the Emergence of Thymopoiesis in Vertebrates

Candidate Genes and Adaptive Radiation: Insights from Transcriptional Adaptation to the Limnetic Niche among Coregonine Fishes (Coregonus spp., Salmonidae)

In the past 40 years, there has been increasing acceptance that variation in levels of gene expression represents a major source of evolutionary novelty. Gene expression divergence is therefore likely to be involved in the emergence of incipient species, namely, in a context of adaptive radiation. In the lake whitefish species complex (Coregonus clupeaformis), previous microarray experiments have led to the identification of candidate genes potentially implicated in the parallel evolution of the limnetic dwarf lake whitefish, which is highly distinct from the benthic normal lake whitefish in life history, morphology, metabolism, and behavior, and yet diverged from it only approximately 15,000 years before present. The aim of the present study was to address transcriptional divergence for six candidate genes among lake whitefish and European whitefish (Coregonus lavaretus) species pairs, as well as lake cisco (Coregonus artedi) and vendace (Coregonus albula). The main goal was to test the hypothesis that parallel phenotypic adaptation toward the use of the limnetic niche in coregonine fishes is accompanied by parallelism in candidate gene transcription as measured by quantitative real-time polymerase chain reaction. Results obtained for three candidate genes, whereby parallelism in expression was observed across all whitefish species pairs, provide strong support for the hypothesis that divergent natural selection plays an important role in the adaptive radiation of whitefish species. However, this parallelism in expression did not extend to cisco and vendace, thereby infirming transcriptional convergence between limnetic whitefish species and their limnetic congeners for these genes. As recently proposed (Lynch 2007a. The evolution of genetic networks by non-adaptive processes. Nat Rev Genet. 8:803-813), these results may suggest that convergent phenotypic evolution can result from nonadaptive shaping of genome architecture in independently evolved coregonine lineages.

Development of the carapacial ridge: Implications for the evolution of genetic networks in turtle shell development

Development of the carapacial ridge: Implications for the evolution of genetic networks in turtle shell development

The evolution of genetic networks by non-adaptive processes

Although numerous investigators assume that the global features of genetic networks are moulded by natural selection, there has been no formal demonstration of the adaptive origin of any genetic network. This Analysis shows that many of the qualitative features of known transcriptional networks can arise readily through the non-adaptive processes of genetic drift, mutation and recombination, raising questions about whether natural selection is necessary or even sufficient for the origin of many aspects of gene-network topologies. The widespread reliance on computational procedures that are devoid of population-genetic details to generate hypotheses for the evolution of network configurations seems to be unjustified.

The Opportunity for Canalization and the Evolution of Genetic Networks

There has been a recent revival of interest in how genetic interactions evolve, spurred on by an increase in our knowledge of genetic interactions at the molecular level. Empirical work on genetic networks has revealed a surprising amount of robustness to perturbations, suggesting that robustness is an evolved feature of genetic networks. Here, we derive a general model for the evolution of canalization that can incorporate any form of perturbation. We establish an upper bound to the strength of selection on canalization that is approximately equal to the fitness load in the system. This method makes it possible to compare different forms of perturbation, including genetic, developmental, and environmental effects. In general, load that arises from mutational processes is low because the mutation rate is itself low. Mutation load can create selection for canalization in a small network that can be achieved through dominance evolution or gene duplication, and in each case selection for canalization is weak at best. In larger genetic networks, selection on genetic canalization can be reasonably strong because larger networks have higher mutational load. Because load induced through migration, segregation, developmental noise, and environmental variance is not mutation limited, each can cause strong selection for canalization.

The Opportunity for Canalization and the Evolution of Genetic Networks

There has been a recent revival of interest in how genetic interactions evolve, spurred on by an increase in our knowledge of genetic interactions at the molecular level. Empirical work on genetic networks has revealed a surprising amount of robustness to perturbations, suggesting that robustness is an evolved feature of genetic networks. Here, we derive a general model for the evolution of canalization that can incorporate any form of perturbation. We establish an upper bound to the strength of selection on canalization that is approximately equal to the fitness load in the system. This method makes it possible to compare different forms of perturbation, including genetic, developmental, and environmental effects. In general, load that arises from mutational processes is low because the mutation rate is itself low. Mutation load can create selection for canalization in a small network that can be achieved through dominance evolution or gene duplication, and in each case selection for canalization is weak at best. In larger genetic networks, selection on genetic canalization can be reasonably strong because larger networks have higher mutational load. Because load induced through migration, segregation, developmental noise, and environmental variance is not mutation limited, each can cause strong selection for canalization.