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Journal of Theoretical Biology
Article . 2007 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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zbMATH Open
Article . 2007
Data sources: zbMATH Open
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Do scale-free regulatory networks allow more expression than random ones?

Authors: Fortuna, Miguel A.; Melián, Carlos J.;

Do scale-free regulatory networks allow more expression than random ones?

Abstract

In this paper, we compile the network of software packages with regulatory interactions (dependences and conflicts) from Debian GNU/Linux operating system and use it as an analogy for a gene regulatory network. Using a trace-back algorithm we assemble networks from the pool of packages with both scale-free (real data) and exponential (null model) topologies. We record the maximum number of packages that can be functionally installed in the system (i.e., the active network size). We show that scale-free regulatory networks allow a larger active network size than random ones. This result might have implications for the number of expressed genes at steady state. Small genomes with scale-free regulatory topologies could allow much more expression than large genomes with exponential topologies. This may have implications for the dynamics, robustness and evolution of genomes.

Keywords

Genome, Models, Genetic, Transcription, Genetic, gene networks, Regulatory interactions, Systems biology, networks, Gene networks, Complex networks, complex networks, Theory of software, transcriptional regulatory networks, Animals, network assembly, regulatory interactions, Gene Regulatory Networks, Genetics and epigenetics, Network assembly, Transcriptional regulatory networks, Algorithms, Software

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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9
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