Evolution of digital organisms at high mutation rates leads to survival of the flattest

By Claus O. Wilke, Jia Lan Wang, Charles Ofria, R. Lenski, C. Adami. Nature, 2001

669 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Genetics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/81a21bf23746908724a13719ba2ef55974512592

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Citations in the corpus

A biophysical protein folding model accounts for most mutational fitness effects in viruses (2011, background): “For example, inspired by the results from digital organisms (38), Sanjuán et al.”
A structural model of latent evolutionary potentials underlying neutral networks in proteins (2007, background): “Earlier considerations showed that, under constant fitness pressure, a population on a neutral net tends to concentrate around the genotype with the largest number of neutral mutations (van Nimwegen et al., 1999; Wilke et al., 2001; Bornberg-Bauer and Chan, 1999) which, according to the superfunnel ”
EVOLUTION AND DETECTION OF GENETIC ROBUSTNESS (2003, background): “If robustness comes at a cost, only populations with high mutation rates and substantial back mutation will evolve robustness (Gabriel and Bü rger 2000; Wilke et al. 2001; Hermisson et al. 2002).”
The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing (2014, background): “We expect more prevalent sequences to be more fit, consistent with expectations from simple models of evolution [23] though the precise correspondence between fitness and prevalence may have a more complicated dependence on factors such as the shape of the fitness landscape, as predicted by quasispe”
On the application of statistical physics to evolutionary biology. (2009, background): “, 2003), and the tendency of mutation to drive populations towards flatter parts of the adaptive landscape has been termed "survival of the flattest" (Wilke et al., 2001; Wilke, 2005).”
Quasispecies Theory and the Behavior of RNA Viruses (2010, background): “has been observed in digital organisms, bacteriophages, and VSV [24–28].”
The Evolution and Emergence of RNA Viruses (2009, background): “This effect has cleverly been referred to as the ‘survival of the fl attest’ (Wilke et al. 2001), although it is perhaps more correctly thought of as increased mutational robustness.”
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (2018, positive): “In research focused on understanding how organisms evolve to cope with high-mutation-rate environments [146], Ofria sought to disentangle the beneficial effects of performing tasks (which would allow an organism to execute its code faster and thus replicate faster) from evolved robustness to the…”
Evolvability as a function of purifying selection in TEM-1 β-lactamase. (2015, positive): “, ampicillin) or under conditions of highmutation rate (Wilke et al., 2001).”
The alignment problem from a deep learning perspective (2022, background): “One precursor comes from Wilke et al. [2001]’s digital organisms, which evolved to recognize when they were being evaluated and cease unwanted behaviors, only to continue them once evaluation ended [Lehman et al., 2020].”
Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes (2008, methodology): “We studied the evolution of mutation rates using the Avida digital evolution platform [25–34].”