Luis Zaman

Coevolution drives the emergence of complex traits and promotes evolvability.

Experiments using a digital host-parasite model system show that coevolution can drive the emergence of complex traits and more evolvable genomes. Homepage Title: Parasitism Drives the Evolution of Complexity

Sustained fitness gains and variability in fitness trajectories in the long-term evolution experiment with Escherichia coli

Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving populations mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50 000 generations were better described by a power-law model than by a hyperbolic model. According to the power-law model, the rate of fitness gain declines over time but fitness has no upper limit, whereas the hyperbolic model implies a hard limit. Here, we examine whether the previously estimated power-law model predicts the fitness trajectory for an additional 10 000 generations. To that end, we conducted more than 1100 new competitive fitness assays. Consistent with the previous study, the power-law model fits the new data better than the hyperbolic model. We also analysed the variability in fitness among populations, finding subtle, but significant, heterogeneity in mean fitness. Some, but not all, of this variation reflects differences in mutation rate that evolved over time. Taken together, our results imply that both adaptation and divergence can continue indefinitely—or at least for a long time—even in a constant environment.

Evolving digital ecological networks.

“It is hard to realize that the living world as we know it is just one among many possibilities” [1]. Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs (i.e., digital organisms) that experience the same major ecological interactions as biological organisms (e.g., competition, predation, parasitism, and mutualism). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations (hereafter tasks) that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology (e.g., the extent to which the architecture of multispecies networks shape coevolutionary outcomes, and the processes involved).

Rapid host-parasite coevolution drives the production and maintenance of diversity in digital organisms

Accumulating evidence suggests evolution and ecology can happen on similar time scales. Coevolution between hosts and parasites is a practical example of interacting ecological and evolutionary dynamics. Antagonistic interactions theoretically and experimentally increase host diversity, but the contribution of novel variation to diversity is not well understood. In laboratory or natural settings it is infeasible to prohibit novel mutations in communities while still allowing frequencies of extant organisms to change. We turn to digital organisms to investigate the effects of rapid evolution on host-parasite community diversity in the presence and absence of novel variation. We remove the source of variation in coevolved digital host-parasite communities and allow them to reach an equilibrium. We find that coevolved host-parasite communities are surprisingly stable in the absence of new variation. However, the communities at equilibrium are less diverse than those that continued to experience mutations. In either case, hosts coevolving with parasites are significantly more diverse than hosts evolving alone. Harnessing an advantage of in silico evolution, we show that novel variation increases host diversity in communities with parasites further than the trivial increase expected from new mutations.

On the intrinsic sterility of 3D printing

3D printers that build objects using extruded thermoplastic are quickly becoming commonplace tools in laboratories. We demonstrate that with appropriate handling, these devices are capable of producing sterile components from a non-sterile feedstock of thermoplastic without any treatment after fabrication. The fabrication process itself results in sterilization of the material. The resulting 3D printed components are suitable for a wide variety of applications, including experiments with bacteria and cell culture.

The genotype-phenotype map of an evolving digital organism

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

Modeling the evolutionary dynamics of plasmids in spatial populations

One of the processes by which microorganisms are able to rapidly adapt to changing conditions is horizontal gene transfer, whereby an organism incorporates additional genetic material from sources other than its parent. These genetic elements may encode a wide variety of beneficial traits. Under certain conditions, many computational models capture the evolutionary dynamics of adaptive behaviors such as toxin production, quorum sensing, and biofilm formation, and have even provided new insights into otherwise unknown or misunderstood phenomena. However, such models rarely incorporate horizontal gene transfer, so they may be incapable of fully representing the vast repertoire of behaviors exhibited by natural populations. Although models of horizontal gene transfer exist, they rarely account for the spatial structure of populations, which is often critical to adaptive behaviors. In this work we develop a spatial model to examine how conjugation, one mechanism of horizontal gene transfer, can be maintained in populations. We investigate how both the costs of transfer and the benefits conferred affect evolutionary outcomes. Further, we examine how rates of transmission evolve, allowing this system to adapt to different environments. Through spatial models such as these, we can gain a greater understanding of the conditions under which horizontally-acquired behaviors are evolved and are maintained.

Non-adaptive origins of evolutionary innovations increase network complexity in interacting digital organisms

The origin of evolutionary innovations is a central problem in evolutionary biology. To what extent such innovations have adaptive or non-adaptive origins is hard to assess in real organisms. This limitation, however, can be overcome using digital organisms, i.e. self-replicating computer programs that mutate, evolve and coevolve within a user-defined computational environment. Here, we quantify the role of the non-adaptive origins of host resistance traits in determining the evolution of ecological interactions among host and parasite digital organisms. We find that host resistance traits arising spontaneously as exaptations increase the complexity of antagonistic host–parasite networks. Specifically, they lead to higher host phenotypic diversification, a larger number of ecological interactions and higher heterogeneity in interaction strengths. Given the potential of network architecture to affect network dynamics, such exaptations may increase the persistence of entire communities. Our in silico approach, therefore, may complement current theoretical advances aimed at disentangling the ecological and evolutionary mechanisms shaping species interaction networks. This article is part of the themed issue ‘Process and pattern in innovations from cells to societies’.

Finger-painting Fitness Landscapes: An Interactive Tool for Exploring Complex Evolutionary Dynamics.

Evolution involves only a few simple processes, yet the resulting dynamics are surprisingly rich and complex. Sewall Wright developed the metaphor of fitness landscapes to provide deeper insight into the complex workings of evolution. Here we extend that metaphor by visualizing in real time the dynamic processes that drive evolution. We allow viewers to construct fitness landscapes interactively while also varying key parameters including population size, mutation effect size, mode of reproduction (asexual or sexual), and densitydependent selection. This application is both mechanistic and visual, and it thereby allows the active exploration of evolutionary processes. We walk the reader through several exercises including both simple activities potentially suitable for education and examples of deeply conceptual topics that remain the focus of current research in evolutionary biology.

Improved adaptation in exogenously and endogenously changing environments.

Fitness landscapes are visual metaphors that appeal to our intuition for real-world landscapes to help us understand how populations evolve. The object inspiring the metaphor is better described as...