Distinct evolutionary trajectories in asexual populations through an interplay of their size, resource availability and mutation rates

Abstract

Asexually reproducing populations of single cells evolve through mutation, natural selection, and genetic drift to enhance their reproductive fitness. The environment provides the contexts that allow and regulate their fitness dynamics. In this work, we used Avida - a digital evolution framework - to uncover the effect of mutation rates, maximum size of the population, and the relative abundance of resources, on evolutionary outcomes in asexually reproducing populations of digital organisms. We observed that over extended simulations, the population evolved predominantly to one of several discrete fitness classes, each with distinct sequence motifs and/or phenotypes. For a low mutation rate, the organisms acquired either of four fitness values through an enhancement in the rate of genomic replication. Evolution at a relatively higher mutation rate presented a more complex picture. While the highest fitness values at a high mutation rate were achieved through enhanced genome replication rates, a suboptimal one was achieved through organisms sharing information relevant to metabolic tasks with each other. The information sharing capacity was vital to fitness acquisition and frequency of the genotype associated with it increased with greater resource levels and maximum population size. In addition, populations optimizing their fitness through such means exhibited a greater degree of genotypic heterogeneity and metabolic activity than those that improved replication rates. Our results reveal a minimal set of conditions for the emergence of interdependence within evolving populations with significant implications for biological systems in appropriate environmental contexts.