Historical and contingent factors affect re-evolution of a complex feature lost during mass extinction in communities of digital organisms.

Re-evolution of complex biological features following the extinction of taxa bearing them remains one of evolution's most interesting phenomena, but is not amenable to study in fossil taxa. We used communities of digital organisms (computer programs that self-replicate, mutate and evolve), subjected to periods of low resource availability, to study the evolution, loss and re-evolution of a complex computational trait, the function EQU (bit-wise logical equals). We focused our analysis on cases where the pre-extinction EQU clade had surviving descendents at the end of the extinction episode. To see if these clades retained the capacity to re-evolve EQU, we seeded one set of multiple subreplicate 'replay' populations using the most abundant survivor of the pre-extinction EQU clade, and another set with the actual end-extinction ancestor of the organism in which EQU re-evolved following the extinction episode. Our results demonstrate that stochastic, historical, genomic and ecological factors can lead to constraints on further adaptation, and facilitate or hinder re-evolution of a complex feature.

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

Darwinian evolution favours genotypes with high replication rates, a process called 'survival of the fittest'. However, knowing the replication rate of each individual genotype may not suffice to predict the eventual survivor, even in an asexual population. According to quasi-species theory, selection favours the cloud of genotypes, interconnected by mutation, whose average replication rate is highest. Here we confirm this prediction using digital organisms that self-replicate, mutate and evolve. Forty pairs of populations were derived from 40 different ancestors in identical selective environments, except that one of each pair experienced a 4-fold higher mutation rate. In 12 cases, the dominant genotype that evolved at the lower mutation rate achieved a replication rate >1.5-fold faster than its counterpart. We allowed each of these disparate pairs to compete across a range of mutation rates. In each case, as mutation rate was increased, the outcome of competition switched to favour the genotype with the lower replication rate. These genotypes, although they occupied lower fitness peaks, were located in flatter regions of the fitness surface and were therefore more robust with respect to mutations.

Evolution of biological complexity.

To make a case for or against a trend in the evolution of complexity in biological evolution, complexity needs to be both rigorously defined and measurable. A recent information-theoretic (but intuitively evident) definition identifies genomic complexity with the amount of information a sequence stores about its environment. We investigate the evolution of genomic complexity in populations of digital organisms and monitor in detail the evolutionary transitions that increase complexity. We show that, because natural selection forces genomes to behave as a natural "Maxwell Demon," within a fixed environment, genomic complexity is forced to increase.

Genome complexity, robustness and genetic interactions in digital organisms.

Digital organisms are computer programs that self-replicate, mutate and adapt by natural selection. They offer an opportunity to test generalizations about living systems that may extend beyond the organic life that biologists usually study. Here we have generated two classes of digital organism: simple programs selected solely for rapid replication, and complex programs selected to perform mathematical operations that accelerate replication through a set of defined 'metabolic' rewards. To examine the differences in their genetic architecture, we introduced millions of single and multiple mutations into each organism and measured the effects on the organism's fitness. The complex organisms are more robust than the simple ones with respect to the average effects of single mutations. Interactions among mutations are common and usually yield higher fitness than predicted from the component mutations assuming multiplicative effects; such interactions are especially important in the complex organisms. Frequent interactions among mutations have also been seen in bacteria, fungi and fruitflies. Our findings support the view that interactions are a general feature of genetic systems.