Using digital organisms to study the evolutionary consequences of whole genome duplication and polyploidy

Abstract

The potential role of whole genome duplication (WGD) in evolution is controversial. Whereas some view WGD mainly as detrimental and an evolutionary ‘dead end’, there is growing evidence that the long-term establishment of polyploidy might be linked to environmental change, stressful conditions, or periods of extinction. However, despite much research, the mechanistic underpinnings of why and how polyploids might be able to outcompete non-polyploids at times of environmental upheaval remain indefinable. Here, we improved our recently developed bio-inspired framework, combining an artificial genome with an agent-based system, to form a population of so-called Digital Organisms (DOs), to examine the impact of WGD on evolution under different environmental scenarios mimicking extinction events of varying strength and frequency. We found that, under stable environments, DOs with non-duplicated genomes formed the majority, if not all, of the population, whereas the numbers of DOs with duplicated genomes increased under dramatically challenging environments. After tracking the evolutionary trajectories of individual genomes in terms of sequence and encoded gene regulatory networks (GRNs), we propose that duplicated GRNs might provide polyploids with better chances to acquire the drastic changes necessary to adapt to challenging conditions, thus endowing DOs with increased adaptive potential under extinction events. In contrast, under stable environments, random mutations might easily render the GRN less well adapted to such environments, a phenomenon that is exacerbated in duplicated, more complex GRNs. We believe that our results provide some additional insights into how genome duplication and polyploidy might help organisms to compete for novel niches and survive ecological turmoil, and confirm the usefulness of our computational simulation in studying the role of WGD in evolution and adaptation, helping to overcome some of the traditional limitations of evolution experiments with model organisms.