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After an ancestral population splits into two allopatric populations, different mutations may fix in each. When pairs of mutations are brought together in a hybrid offspring, epistasis may cause reduced fitness. Such pairs are known as Bateson–Dobzhansky–Muller (BDM) incompatibilities. A well-known model of BDM incompatibility due to Orr suggests that the fitness load on hybrids should initially accelerate, and continue to increase as the number of potentially incompatible substitutions increases (the “snowball effect”). In the gene networks model, which violates a key assumption of Orr's model (independence of fixation probabilities), the snowball effect often does not occur. Instead, we describe three possible dynamics in a constant environment: (1) Stabilizing selection can constrain two allopatric populations to remain near-perfectly compatible. (2) Despite constancy of environment, punctuated evolution may obtain; populations may experience rare adaptations asynchronously, permitting incompatibility. (3) Despite stabilizing selection, developmental system drift may permit genetic change, allowing two populations to drift in and out of compatibility. We reinterpret Orr's model in terms of genetic distance. We extend Orr's model to the finite loci case, which can limit incompatibility. Finally, we suggest that neutral evolution of gene regulation in nature, to the point of speciation, is a distinct possibility. [ABSTRACT FROM AUTHOR]

It is often assumed that the efficiency of selection for mutational robustness would be proportional to mutation rate and population size, thus being inefficient in small populations. However, Krakauer and Plotkin (2002) hypothesized that selection in small populations would favor robustness mechanisms, such as redundancy, that mask the effect of deleterious mutations. In large populations, by contrast, selection is more effective at removing deleterious mutants and fitness would be improved by eliminating mechanisms that mask the effect of deleterious mutations and thus impede their removal. Here, we test whether these predictions are supported in experiments with evolving populations of digital organisms. Digital organisms are self-replicating programs that inhabit a virtual world inside a computer. Like their organic counterparts, digital organisms mutate, compete, evolve, and adapt by natural selection to their environment. In this study, 160 populations evolved at different combinations of mutation rate and population size. After 104 generations, we measured the mutational robustness of the most abundant genotype in each population. Mutational robustness tended to increase with mutation rate and to decline with population size, although the dependence with population size was in part mediated by a negative relationship between fitness and robustness. These results are independent of whether genomes were constrained to their original length or allowed to change in size. [ABSTRACT FROM AUTHOR]

Genetic assimilation occurs when an acquired trait loses dependency on its environmental trigger and becomes an inherited trait. According to the standard quantitative genetic model for genetic assimilation, the trait is determined by the contributions of multiple genes. Trait expression occurs at a lower threshold with the trigger. Selection for the trait in the presence of the trigger increases the frequency of the trait-determining alleles. Eventually these alleles become frequent enough to breach the higher threshold for expression in the absence of the trigger. This loss of dependence on the trigger signifies genetic assimilation. Here I show that genetic assimilation can occur in the absence of selection for the trait in an evolutionary simulation of a gene network model. This contradicts the prediction of the standard quantitative genetic model, but is consistent with an explanation in terms of the canalization heuristic. [ABSTRACT FROM AUTHOR]