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Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions
- Source :
- Scientific Reports, Vol 9, Iss 1, Pp 1-15 (2019), Scientific Reports
- Publication Year :
- 2019
- Publisher :
- Nature Publishing Group, 2019.
-
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 networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random and therefore attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.<br />Comment: 28 pages, 5 figures, 12 pages supplementary information
- Subjects :
- 0301 basic medicine
Computer science
Archaeal Proteins
Molecular Networks (q-bio.MN)
Gene regulatory network
lcsh:Medicine
computer.software_genre
Article
03 medical and health sciences
0302 clinical medicine
Bacterial Proteins
Quantitative Biology - Molecular Networks
Computer Simulation
Gene Regulatory Networks
lcsh:Science
Clustering coefficient
Multidisciplinary
Models, Genetic
Quantitative Biology::Molecular Networks
lcsh:R
Computational Biology
Epistasis, Genetic
Network density
Biological Evolution
030104 developmental biology
Prokaryotic Cells
FOS: Biological sciences
Epistasis
Probability distribution
lcsh:Q
Data mining
Signal transduction
computer
Algorithms
030217 neurology & neurosurgery
Biological network
Signal Transduction
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 9
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....7633740713a14b0e8f67e9552bc814e8