ArticlePloS one2012
Most networks in Wagner's model are cycling.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Breaking reflection symmetry: evolving long dynamical cycles in Boolean systems.New journal of physics · 2024Article
- Article
- Being noisy in a crowd: Differential selective pressure on gene expression noise in model gene regulatory networks.PLoS computational biology · 2023Article
- Gene loss during a transition to multicellularity.Scientific reports · 2023Article
- Using phenotypic plasticity to understand the structure and evolution of the genotype-phenotype map.Genetica · 2022Review
- The basis of easy controllability in Boolean networks.Nature communications · 2021Article
- Explorability and the origin of network sparsity in living systems.Scientific reports · 2017Article
- Exploratory adaptation in large random networks.Nature communications · 2017Article
- Modeling the evolution of complex genetic systems: the gene network family tree.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2015Article
- Stability depends on positive autoregulation in Boolean gene regulatory networks.PLoS computational biology · 2014Article
- Article
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Authors and funding
3 authors.
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Abstract
In this paper we study a model of gene networks introduced by Andreas Wagner in the 1990s that has been used extensively to study the evolution of mutational robustness. We investigate a range of model features and parameters and evaluate the extent to which they influence the probability that a random gene network will produce a fixed point steady state expression pattern. There are many different types of models used in the literature, (discrete/continuous, sparse/dense, small/large network) and we attempt to put some order into this diversity, motivated by the fact that many properties are qualitatively the same in all the models. Our main result is that random networks in all models give rise to cyclic behavior more often than fixed points. And although periodic orbits seem to dominate network dynamics, they are usually considered unstable and not allowed to survive in previous evolutionary studies. Defining stability as the probability of fixed points, we show that the stability distribution of these networks is highly robust to changes in its parameters. We also find sparser networks to be more stable, which may help to explain why they seem to be favored by evolution. We have unified several disconnected previous studies of this class of models under the framework of stability, in a way that had not been systematically explored before.
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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.