Biological gene networks appear to be dynamically robust to mutation, stochasticity, and changes in the environment and also appear to be sparsely connected. Studies with computational models, however, have suggested that denser gene networks evolve to be more dynamically robust than sparser networks. We resolve this discrepancy by showing that misassumptions about how to measure robustness in artificial networks have inadvertently discounted the costs of network complexity. We show that when the costs of complexity are taken into account, that robustness implies a parsimonious network structure that is sparsely connected and not unnecessarily complex; and that selection will favor sparse networks when network topology is free to evolve. Because a robust system of heredity is necessary for the adaptive evolution of complex phenotypes, the maintenance of frugal network complexity is likely a crucial design constraint that underlies biological organization.
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http://dx.doi.org/10.1038/msb.2008.52 | DOI Listing |
J Med Virol
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Institute of Virology, Technical University of Munich/Helmholtz Munich, Munich, Germany.
SARS-CoV-2 infection is accompanied by elevated liver enzymes, and patients with pre-existing liver conditions experience more severe disease. While it was known that SARS-CoV-2 infects human hepatocytes, our study determines the mechanism of infection, demonstrates viral replication and spread, and highlights direct hepatocyte damage. Viral replication was readily detectable upon infection of primary human hepatocytes and hepatoma cells with the ancestral SARS-CoV-2, Delta, and Omicron variants.
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School of Food Science & Biotechnology, Kyungpook National University, Daegu 41566, Korea.
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Digital Technologies Research Center, National Research Council Canada, Ottawa, ON, K1A 0R6, Canada.
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