An evolutionary model for the origin of modularity in a complex gene network

Gu, Xun

doi:10.1002/jez.b.21249

Cited by 6 publications

(6 citation statements)

References 30 publications

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“…Therefore, the case m/0 is consistent with a slower rate of loss of interactions though transevolution than the case mZ1. This can be compared with recent work (Gu 2009), suggesting that gene network evolution may be characterized by a 2-2-1 pattern (net gain of two genes and two edges along with loss of one edge). In our model, the ratio of gain of two edges to loss of one edge is more consistent with the case m/0 than with the case mZ1.…”

Section: ð3:2þmentioning

confidence: 75%

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Degree dependence in rates of transcription factor evolution explains the unusual structure of transcription networks

2009

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Transcription networks have an unusual structure. In both prokaryotes and eukaryotes, the number of target genes regulated by each transcription factor, its out-degree, follows a broad tailed distribution. By contrast, the number of transcription factors regulating a target gene, its in-degree, follows a much narrower distribution, which has no broad tail. We constructed a model of transcription network evolution through trans-and cis-mutations, gene duplication and deletion. The effects of these different evolutionary processes on the network structure are enough to produce an asymmetrical in-and out-degree distribution. However, the parameter values required to replicate known in-and out-degree distributions are unrealistic. We then considered variation in the rate of evolution of a gene dependent upon its position in the network. When transcription factors with many regulatory interactions are constrained to evolve more slowly than those with few interactions, the details of the in-and out-degree distributions of transcription networks can be fully reproduced over a range of plausible parameter values. The networks produced by our model depend on the relative rates of the different evolutionary processes. By determining the circumstances under which the networks with the correct degree distributions are produced, we are able to assess the relative importance of the different evolutionary processes in our model during evolution.

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Section: ð3:2þmentioning

confidence: 75%

“…However, it is well known that duplication growth models of networks can produce power-law distributions (Bahn et al 2002;Pastor-Sorras et al 2002;Chung et al 2003;Gu 2009). We have not considered growing networks for two reasons.…”

Section: ð3:2þmentioning

confidence: 99%

Degree dependence in rates of transcription factor evolution explains the unusual structure of transcription networks

2009

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“…The Rhodopsin class is further divided into four groups: alpha, beta, gamma and delta. The sequences of vertebrate GPCR genes were retrieved from the Hovergen database [ 28 ]. In cases where the GRAFS families were further split in Hovergen, the split families were used for age estimation separately.…”

Section: Methodsmentioning

confidence: 99%

“…Families were constructed for each entry in Figure 1 . The vertebrate homologues in the gene families were retrieved from Hovergen [ 28 ], the invertebrate homologues and the vertebrate homolgues missed by Hovergen were identified by homology searching in the Swiss-prot database. Redundant sequences were removed similarly as for GPCRs.…”

Section: Methodsmentioning

confidence: 99%

Differences in duplication age distributions between human GPCRs and their downstream genes from a network prospective

Huang

2009

BMC Genomics

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Background: How gene duplication has influenced the evolution of gene networks is one of the core problems in evolution. Current duplication-divergence theories generally suggested that genes on the periphery of the networks were preferentially retained after gene duplication. However, previous studies were mostly based on gene networks in invertebrate species, and they had the inherent shortcoming of not being able to provide information on how the duplicationdivergence process proceeded along the time axis during major speciation events.

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“…Otherwise, several models (processes) of network evolution have already been proposed to explain the emergence of local structural modules in different networks. These include increase of stability [27], network fluctuations [28], goal variation [29], opinion formation [30], constraint optimization [31] and others [32]. However, the origin of community structure in software networks remains unclear.…”

Section: Related Workmentioning

confidence: 98%

Community structure of complex software systems: Analysis and applications

Šubelj

Bajec

2011

Physica A: Statistical Mechanics and its Applications

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a b s t r a c tDue to notable discoveries in the fast evolving field of complex networks, recent research in software engineering has also focused on representing software systems with networks. Previous work has observed that these networks follow scale-free degree distributions and reveal small-world phenomena, while we here explore another property commonly found in different complex networks, i.e. community structure. We adopt class dependency networks, where nodes represent software classes and edges represent dependencies among them, and show that these networks reveal a significant community structure, characterized by similar properties as observed in other complex networks. However, although intuitive and anticipated by different phenomena, identified communities do not exactly correspond to software packages. We empirically confirm our observations on several networks constructed from Java and various third party libraries, and propose different applications of community detection to software engineering.

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An evolutionary model for the origin of modularity in a complex gene network

Cited by 6 publications

References 30 publications

Degree dependence in rates of transcription factor evolution explains the unusual structure of transcription networks

Degree dependence in rates of transcription factor evolution explains the unusual structure of transcription networks

Differences in duplication age distributions between human GPCRs and their downstream genes from a network prospective

Community structure of complex software systems: Analysis and applications

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