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Thus, on the fitness landscape, the standard code was found to be closer to its local peak than most of the more robust random codes were from theirs.
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By using purely graph theoretical measures – the diameter of the network and the size of the largest connected component – they found that scale-free networks are much more robust against random errors than random networks but more vulnerable against directed attacks.
The work we reported here suggests that viability in the E. coli metabolic network is significantly more robust to random reaction removal than in random viable genotypes for the three environments we studied.
The results strongly suggest that networks with higher efficiency are more robust to random failures.
The existing structures are more robust to random mutations and therefore are more evolutionary stable.
Therefore, we concluded that natural pre-miRNAs are not more robust than random structural analogs.
In all environments we studied, the E. coli metabolic network is significantly more robust than random viable networks.
These curves, representing an average survival behaviour for the defined condition, allow us to compare the strains with higher confidence survival curves, due to the vastly increased number of observations compared with single studies, rendering the estimates more robust to random influences.
However, these relationships were not verified in the statistically more robust longitudinal random-effects models (third set), where all relationships between population achievement and outcomes were negligible.
Taking CSD into account, a scale-free network could turn out to be more robust than a random network in the face of intended attacks.
It is shown that networks that employ this type of multivalued logic exhibit more robust dynamics than random networks, with few attractors and short limit cycles.
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