2019
DOI: 10.1038/s41598-019-39866-z
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Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

Abstract: Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network currently being complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete ne… Show more

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Cited by 13 publications
(43 citation statements)
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“…However, assigning the interaction to the coding gene would be misleading and inflates the number of self-loops in the network even when the sRNAs might be transcribed through its own promoter. As previously discussed, the interaction coverage is a better proxy for network completeness than the genomic coverage [6]. Although the authors provide the σ factors required for the transcription of the sRNAs, we did not include these σ-DNA interactions as they were solely supported by DNA-binding motif computational predictions and we have identified a high number of false positives in the search of binding sites for the σ factors, and purely computational prediction is not considered for the Abasy Atlas networks [1].…”
Section: Curation and Network Definitionmentioning
confidence: 98%
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“…However, assigning the interaction to the coding gene would be misleading and inflates the number of self-loops in the network even when the sRNAs might be transcribed through its own promoter. As previously discussed, the interaction coverage is a better proxy for network completeness than the genomic coverage [6]. Although the authors provide the σ factors required for the transcription of the sRNAs, we did not include these σ-DNA interactions as they were solely supported by DNA-binding motif computational predictions and we have identified a high number of false positives in the search of binding sites for the σ factors, and purely computational prediction is not considered for the Abasy Atlas networks [1].…”
Section: Curation and Network Definitionmentioning
confidence: 98%
“…It has been previously debated whether the P(k) of real networks is truly governed by a power-law distribution where a few nodes have most of the interactions [16]. Recently, using several statistic methods we demonstrated that the regulatory networks truly follow a power-law distribution and they would fit other power law-like distributions better than a Poisson distribution regardless of the completeness of the network and that the sole coefficient of determination (R 2 ) is a good proxy to assess the goodness-of-fit of the model [6].…”
Section: Analyzing Regulatory Network: a Primermentioning
confidence: 99%
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“…After this update, C. glutamicum moved from the fourth to the second position as the organism with the most complete regulatory network in Abasy Atlas, according to our recently published model of the total number of interactions a complete regulatory network has [6]. We discuss the global structural properties of the three network models in the context of the previous versions for the transcriptional regulatory models and more than 40 other bacterial networks from Abasy Atlas, the most complete collection of experimentally-validated regulatory networks [1].…”
Section: Introductionmentioning
confidence: 99%