ArticlePloS one2012
Mutational robustness of gene regulatory networks.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Potential for evolution of complex defense strategies in a multi-scale model of virus-host coevolution.BMC evolutionary biology · 2016Article
- Models of buffering of dosage imbalances in protein complexes.Biology direct · 2015Article
- Genetic code evolution reveals the neutral emergence of mutational robustness, and information as an evolutionary constraint.Life (Basel, Switzerland) · 2015Article
- Evolving robust gene regulatory networks.PloS one · 2015Article
- Biophysical fitness landscapes for transcription factor binding sites.PLoS computational biology · 2014Article
- On the contributions of topological features to transcriptional regulatory network robustness.BMC bioinformatics · 2012Article
- Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.PloS one · 2012Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.