Intracellular signaling in proto-eukaryotes evolves to alleviate regulatory conflicts of endosymbiosis.

The complex eukaryotic cell resulted from a merger between simpler prokaryotic cells, yet the role of the mitochondrial endosymbiosis with respect to other eukaryotic innovations has remained under dispute. To investigate how the regulatory challenges associated with the endosymbiotic state impacted genome and network evolution during eukaryogenesis, we study a constructive computational model where two simple cells are forced into an obligate endosymbiosis. Across multiple in silico evolutionary replicates, we observe the emergence of different mechanisms for the coordination of host and symbiont cell cycles, stabilizing the endosymbiotic relationship. In most cases, coordination is implicit, without signaling between host and symbiont. Signaling only evolves when there is leakage of regulatory products between host and symbiont. In the fittest evolutionary replicate, the host has taken full control of the symbiont cell cycle through signaling, mimicking the regulatory dominance of the nucleus over the mitochondrion that evolved during eukaryogenesis.

Obligate endosymbiosis enables genome expansion during eukaryogenesis.

The endosymbiosis of an alpha-proteobacterium that gave rise to mitochondria was one of the key events in eukaryogenesis. One striking outcome of eukaryogenesis was a much more complex cell with a large genome. Despite the existence of many alternative hypotheses for this and other patterns potentially related to endosymbiosis, a constructive evolutionary model in which these hypotheses can be studied is still lacking. Here, we present a theoretical approach in which we focus on the consequences rather than the causes of mitochondrial endosymbiosis. Using a constructive evolutionary model of cell-cycle regulation, we find that genome expansion and genome size asymmetry arise from emergent host-symbiont cell-cycle coordination. We also find that holobionts with large host and small symbiont genomes perform best on long timescales and mimic the outcome of eukaryogenesis. By designing and studying a constructive evolutionary model of obligate endosymbiosis, we uncovered some of the forces that may drive the patterns observed in nature. Our results provide a theoretical foundation for patterns related to mitochondrial endosymbiosis, such as genome size asymmetry, and reveal evolutionary outcomes that have not been considered so far, such as cell-cycle coordination without direct communication.

A social niche breadth score reveals niche range strategies of generalists and specialists.

Generalists can survive in many environments, whereas specialists are restricted to a single environment. Although a classical concept in ecology, niche breadth has remained challenging to quantify for microorganisms because it depends on an objective definition of the environment. Here, by defining the environment of a microorganism as the community it resides in, we integrated information from over 22,000 environmental sequencing samples to derive a quantitative measure of the niche, which we call social niche breadth. At the level of genera, we explored niche range strategies throughout the prokaryotic tree of life. We found that social generalists include opportunists that stochastically dominate local communities, whereas social specialists are stable but low in abundance. Social generalists have a more diverse and open pan-genome than social specialists, but we found no global correlation between social niche breadth and genome size. Instead, we observed two distinct evolutionary strategies, whereby specialists have relatively small genomes in habitats with low local diversity, but relatively large genomes in habitats with high local diversity. Together, our analysis shines data-driven light on microbial niche range strategies.

Modelling the evolution of novelty: a review.

Evolution has been an inventive process since its inception, about 4 billion years ago. It has generated an astounding diversity of novel mechanisms and structures for adaptation to the environment, for competition and cooperation, and for organisation of the internal and external dynamics of the organism. How does this novelty come about? Evolution builds with the tools available, and on top of what it has already built - therefore, much novelty consists in repurposing old functions in a different context. In the process, the tools themselves evolve, allowing yet more novelty to arise. Despite evolutionary novelty being the most striking observable of evolution, it is not accounted for in classical evolutionary theory. Nevertheless, mathematical and computational models that illustrate mechanisms of evolutionary innovation have been developed. In the present review, we present and compare several examples of computational evo-devo models that capture two aspects of novelty: 'between-level novelty' and 'constructive novelty.' Novelty can evolve between predefined levels of organisation to dynamically transcode biological information across these levels - as occurs during development. Constructive novelty instead generates a level of biological organisation by exploiting the lower level as an informational scaffold to open a new space of possibilities - an example being the evolution of multicellularity. We propose that the field of computational evo-devo is well-poised to reveal many more exciting mechanisms for the evolution of novelty. A broader theory of evolutionary novelty may well be attainable in the near future.

Evolution of Complex Regulation for Cell-Cycle Control.

Many questions remain about the interplay between adaptive and neutral processes leading to genome expansion and the evolution of cellular complexity. Genome size appears to be tightly linked to the size of the regulatory repertoire of cells (van Nimwegen E. 2003. Scaling laws in the functional content of genomes. Trends Gen. 19(9):479-484). In the context of gene regulation, we here study the interplay between adaptive and nonadaptive forces on genome and regulatory network in a computational model of cell-cycle adaptation to different environments. Starting from the well-known Caulobacter crescentus network, we report on ten replicate in silico evolution experiments where cells evolve cell-cycle control by adapting to increasingly harsh spatial habitats. We find adaptive expansion of the regulatory repertoire of cells. Having a large genome is inherently costly, but also allows for improved cell-cycle behavior. Replicates traverse different evolutionary trajectories leading to distinct eco-evolutionary strategies. In four replicates, cells evolve a generalist strategy to cope with a variety of nutrient levels; in two replicates, different specialist cells evolve for specific nutrient levels; in the remaining four replicates, an intermediate strategy evolves. These diverse evolutionary outcomes reveal the role of contingency in a system under strong selective forces. This study shows that functionality of cells depends on the combination of regulatory network topology and genome organization. For example, the positions of dosage-sensitive genes are exploited to signal to the regulatory network when replication is completed, forming a de novo evolved cell cycle checkpoint. Our results underline the importance of the integration of multiple organizational levels to understand complex gene regulation and the evolution thereof.

Nothing in evolution makes sense except in the light of parasitism: evolution of complex replication strategies.

Parasitism emerges readily in models and laboratory experiments of RNA world and would lead to extinction unless prevented by compartmentalization or spatial patterning. Modelling replication as an active computational process opens up many degrees of freedom that are exploited to meet environmental challenges, and to modify the evolutionary process itself. Here, we use automata chemistry models and spatial RNA-world models to study the emergence of parasitism and the complexity that evolves in response. The system is initialized with a hand-designed replicator that copies other replicators with a small chance of point mutation. Almost immediately, short parasites arise; these are copied more quickly, and so have an evolutionary advantage. The replicators also become shorter, and so are replicated faster; they evolve a mechanism to slow down replication, which reduces the difference of replication rate of replicators and parasites. They also evolve explicit mechanisms to discriminate copies of self from parasites; these mechanisms become increasingly complex. New parasite species continually arise from mutated replicators, rather than from evolving parasite lineages. Evolution itself evolves, e.g. by effectively increasing point mutation rates, and by generating novel emergent mutational operators. Thus, parasitism drives the evolution of complex replicators and complex ecosystems.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics.

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

Contingent evolution of alternative metabolic network topologies determines whether cross-feeding evolves.

Metabolic exchange is widespread in natural microbial communities and an important driver of ecosystem structure and diversity, yet it remains unclear what determines whether microbes evolve division of labor or maintain metabolic autonomy. Here we use a mechanistic model to study how metabolic strategies evolve in a constant, one resource environment, when metabolic networks are allowed to freely evolve. We find that initially identical ancestral communities of digital organisms follow different evolutionary trajectories, as some communities become dominated by a single, autonomous lineage, while others are formed by stably coexisting lineages that cross-feed on essential building blocks. Our results show how without presupposed cellular trade-offs or external drivers such as temporal niches, diverse metabolic strategies spontaneously emerge from the interplay between ecology, spatial structure, and metabolic constraints that arise during the evolution of metabolic networks. Thus, in the long term, whether microbes remain autonomous or evolve metabolic division of labour is an evolutionary contingency.

Slightly beneficial genes are retained by bacteria evolving DNA uptake despite selfish elements.

Horizontal gene transfer (HGT) and gene loss result in rapid changes in the gene content of bacteria. While HGT aids bacteria to adapt to new environments, it also carries risks such as selfish genetic elements (SGEs). Here, we use modelling to study how HGT of slightly beneficial genes impacts growth rates of bacterial populations, and if bacterial collectives can evolve to take up DNA despite selfish elements. We find four classes of slightly beneficial genes: indispensable, enrichable, rescuable, and unrescuable genes. Rescuable genes - genes with small fitness benefits that are lost from the population without HGT - can be collectively retained by a community that engages in costly HGT. While this 'gene-sharing' cannot evolve in well-mixed cultures, it does evolve in a spatial population like a biofilm. Despite enabling infection by harmful SGEs, the uptake of foreign DNA is evolutionarily maintained by the hosts, explaining the coexistence of bacteria and SGEs.

Most animals, including humans, inherit genes from their parents. However, bacteria and other microorganisms can also acquire genes from members of the same generation. This process, called horizontal gene transfer (HGT for short), allows bacteria to quickly adapt to new environments. For example, rather than waiting for rare mutations to arise, bacteria can pick up 'tried and true' genes from their neighbours which allow them to exploit new resources or become resistant to antibiotics. But gene sharing comes at a cost. For instance, taking up DNA is an energetically costly process and exposes bacteria to so-called selfish genes which replicate at the expense of other more useful genes in the genome. Given the costs and the threat of selfish genes, it remained unclear whether HGT is still beneficial in a stable environment where no new resources or antibiotics are present. Here, van Dijk et al. used mathematical modelling to examine how gene sharing affects the growth rate of bacterial colonies living in a stable environment. The experiments showed that bacteria are able to take up new sequences of DNA even in the presence of selfish genes. This allows communities of bacteria to retain genes that provide a small benefit that would otherwise be lost from the population, even when taking up DNA imposes a cost upon the individual. van Dijk et al. found that this collective behaviour cannot evolve in well-mixed bacterial populations, but readily emerged in more structured populations, such as biofilms. This work demonstrates how HGT, a key component of bacterial evolution, has allowed bacteria to coexist with harmful selfish genes. It also provides insights into how genes persist and spread through bacterial communities, which has implications for our understanding of antibiotic resistance.

Trusting the hand that feeds: microbes evolve to anticipate a serial transfer protocol as individuals or collectives.

BACKGROUND: Experimental evolution of microbes often involves a serial transfer protocol, where microbes are repeatedly diluted by transfer to a fresh medium, starting a new growth cycle. This has revealed that evolution can be remarkably reproducible, where microbes show parallel adaptations both on the level of the phenotype as well as the genotype. However, these studies also reveal a strong potential for divergent evolution, leading to diversity both between and within replicate populations. We here study how in silico evolved Virtual Microbe "wild types" (WTs) adapt to a serial transfer protocol to investigate generic evolutionary adaptations, and how these adaptations can be manifested by a variety of different mechanisms. RESULTS: We show that all WTs evolve to anticipate the regularity of the serial transfer protocol by adopting a fine-tuned balance of growth and survival. This anticipation is done by evolving either a high yield mode, or a high growth rate mode. We find that both modes of anticipation can be achieved by individual lineages and by collectives of microbes. Moreover, these different outcomes can be achieved with or without regulation, although the individual-based anticipation without regulation is less well adapted in the high growth rate mode. CONCLUSIONS: All our in silico WTs evolve to trust the hand that feeds by evolving to anticipate the periodicity of a serial transfer protocol, but can do so by evolving two distinct growth strategies. Furthermore, both these growth strategies can be accomplished by gene regulation, a variety of different polymorphisms, and combinations thereof. Our work reveals that, even under controlled conditions like those in the lab, it may not be possible to predict individual evolutionary trajectories, but repeated experiments may well result in only a limited number of possible outcomes.

Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure.

BACKGROUND: Mutators are common in bacterial populations, both in natural isolates and in the lab. The fate of these lineages, which mutation rate is increased up to 100 ×, has long been studied using population genetics models, showing that they can spread in a population following an environmental change. However in stable conditions, they suffer from the increased mutational load, hence being overcome by non-mutators. However, these results don't take into account the fact that an elevated mutation rate can impact the genetic structure, hence changing the sensitivity of the population to mutations. Here we used Aevol, an in silico experimental evolution platform in which genomic structures are free to evolve, in order to study the fate of mutator populations evolving for a long time in constant conditions. RESULTS: Starting from wild-types that were pre-evolved for 300,000 generations, we let 100 mutator populations (point mutation rate ×100) evolve for 100,000 further generations in constant conditions. As expected all populations initially undergo a fitness loss. However, after that the mutator populations started to recover. Most populations ultimately recovered their ancestors fitness, and a significant fraction became even fitter than the non-mutator control clones that evolved in parallel. By analyzing the genomes of the mutators, we show that the fitness recovery is due to two mechanisms: i. an increase in robustness through compaction of the coding part of the mutator genomes, ii. an increase of the selection coefficient that decreases the mean-fitness of the population. Strikingly the latter is due to the accumulation of non-coding sequences in the mutators genomes. CONCLUSION: Our results show that the mutational burden that is classically thought to be associated with mutator phenotype is escapable. On the long run mutators adapted their genomes and reshaped the distribution of mutation effects. Therewith the lineage is able to recover fitness even though the population still suffers the elevated mutation rate. Overall these results change our view of mutator dynamics: by being able to reduce the deleterious effect of the elevated mutation rate, mutator populations may be able to last for a very long time; A situation commonly observed in nature.

Transcriptional Mutagenesis Prevents Ribosomal DNA Deterioration: The Role of Duplications and Deletions.

Clashes between transcription and replication complexes can cause point mutations and chromosome rearrangements on heavily transcribed genes. In eukaryotic ribosomal RNA genes, the system that prevents transcription-replication conflicts also causes frequent copy number variation. Such fast mutational dynamics do not alter growth rates in yeast and are thus selectively near neutral. It was recently found that yeast regulates these mutations by means of a signaling cascade that depends on the availability of nutrients. Here, we investigate the long-term evolutionary effect of the mutational dynamics observed in yeast. We developed an in silico model of single-cell organisms whose genomes mutate more frequently when transcriptional load is larger. We show that mutations induced by high transcriptional load are beneficial when biased toward gene duplications and deletions: they decrease mutational load even though they increase the overall mutation rates. In contrast, genome stability is compromised when mutations are not biased toward gene duplications and deletions, even when mutations occur much less frequently. Taken together, our results show that the mutational dynamics observed in yeast are beneficial for the long-term stability of the genome and pave the way for a theory of evolution where genetic operators are themselves cause and outcome of the evolutionary dynamics.

Around the clock: gradient shape and noise impact the evolution of oscillatory segmentation dynamics.

BACKGROUND: Segmentation, the subdivision of the major body axis into repeated elements, is considered one of the major evolutionary innovations in bilaterian animals. In all three segmented animal clades, the predominant segmentation mechanism is sequential segmentation, where segments are generated one by one in anterior-posterior order from a posterior undifferentiated zone. In vertebrates and arthropods, sequential segmentation is thought to arise from a clock-and-wavefront-type mechanism, where oscillations in the posterior growth zone are transformed into a segmental prepattern in the anterior by a receding wavefront. Previous evo-devo simulation studies have demonstrated that this segmentation type repeatedly arises, supporting the idea of parallel evolutionary origins in these animal clades. Sequential segmentation has been studied most extensively in vertebrates, where travelling waves have been observed that reflect the slowing down of oscillations prior to their cessation and where these oscillations involve a highly complex regulatory network. It is currently unclear under which conditions this oscillator complexity and slowing should be expected to evolve, how they are related and to what extent similar properties should be expected for sequential segmentation in other animal species. RESULTS: To investigate these questions, we extend a previously developed computational model for the evolution of segmentation. We vary the slope of the posterior morphogen gradient and the strength of gene expression noise. We find that compared to a shallow gradient, a steep morphogen gradient allows for faster evolution and evolved oscillator networks are simpler. Furthermore, under steep gradients, damped oscillators often evolve, whereas shallow gradients appear to require persistent oscillators which are regularly accompanied by travelling waves, indicative of a frequency gradient. We show that gene expression noise increases the likelihood of evolving persistent oscillators under steep gradients and of evolving frequency gradients under shallow gradients. Surprisingly, we find that the evolutions of oscillator complexity and travelling waves are not correlated, suggesting that these properties may have evolved separately. CONCLUSIONS: Based on our findings, we suggest that travelling waves may have evolved in response to shallow morphogen gradients and gene expression noise. These two factors may thus also be responsible for the observed differences between different species within both the arthropod and chordate phyla.

Evolutionary Conflict Leads to Innovation: Symmetry Breaking in a Spatial Model of RNA-Like Replicators.

Molecules that replicate in trans are vulnerable to evolutionary extinction because they decrease the catalysis of replication to become more available as a template for replication. This problem can be alleviated with higher-level selection that clusters molecules of the same phenotype, favouring those groups that contain more catalysis. Here, we study a simple replicator model with implicit higher-level selection through space. We ask whether the functionality of such system can be enhanced when molecules reproduce through complementary replication, representing RNA-like replicators. For high diffusion, symmetry breaking between complementary strands occurs: one strand becomes a specialised catalyst and the other a specialised template. In ensemble, such replicators can modulate their catalytic activity depending on their environment, thereby mitigating the conflict between levels of selection. In addition, these replicators are more evolvable, facilitating survival in extreme conditions (i.e., for higher diffusion rates). Our model highlights that evolution with implicit higher-level selection-i.e., as a result of local interactions and spatial patterning-is very flexible. For different diffusion rates, different solutions to the selective conflict arise. Our results support an RNA World by showing that complementary replicators may have various ways to evolve more complexity.

The origin of a primordial genome through spontaneous symmetry breaking.

The heredity of a cell is provided by a small number of non-catalytic templates-the genome. How did genomes originate? Here, we demonstrate the possibility that genome-like molecules arise from symmetry breaking between complementary strands of self-replicating molecules. Our model assumes a population of protocells, each containing a population of self-replicating catalytic molecules. The protocells evolve towards maximising the catalytic activities of the molecules to increase their growth rates. Conversely, the molecules evolve towards minimising their catalytic activities to increase their intracellular relative fitness. These conflicting tendencies induce the symmetry breaking, whereby one strand of the molecules remains catalytic and increases its copy number (enzyme-like molecules), whereas the other becomes non-catalytic and decreases its copy number (genome-like molecules). This asymmetry increases the equilibrium cellular fitness by decreasing mutation pressure and increasing intracellular genetic drift. These results implicate conflicting multilevel evolution as a key cause of the origin of genetic complexity.Early molecules of life likely served both as templates and catalysts, raising the question of how functionally distinct genomes and enzymes arose. Here, the authors show that conflict between evolution at the molecular and cellular levels can drive functional differentiation of the two strands of self-replicating molecules and lead to copy number differences between the two.

Evolution of evolvability and phenotypic plasticity in virtual cells.

BACKGROUND: Changing environmental conditions pose a challenge for the survival of species. To meet this challenge organisms adapt their phenotype by physiological regulation (phenotypic plasticity) or by evolving. Regulatory mechanisms that ensure a constant internal environment in the face of continuous external fluctuations (homeostasis) are ubiquitous and essential for survival. However, more drastic and enduring environmental change, often requires lineages to adapt by mutating. In vitro evolutionary experiments with microbes show that adaptive, large phenotypic changes occur remarkably quickly, requiring only a few mutations. It has been proposed that the high evolvability demonstrated by these microbes, is an evolved property. If both regulation (phenotypic plasticity) and evolvability can evolve as strategies to adapt to change, what are the conditions that favour the emergence of either of these strategy? Does evolution of one strategy hinder or facilitate evolution of the other strategy? RESULTS: Here we investigate this with computational evolutionary modelling in populations of Virtual Cells. During a preparatory evolutionary phase, Virtual Cells evolved homeostasis regulation for internal metabolite concentrations in a fluctuating environment. The resulting wild-type Virtual Cell strains (WT-VCS) were then exposed to periodic, drastic environmental changes, while maintaining selection on homeostasis regulation. In different sets of simulations the nature and frequencies of environmental change were varied. Pre-evolved WT-VCS were highly evolvable, showing rapid evolutionary adaptation after novel environmental change. Moreover, continued low frequency changes resulted in evolutionary restructuring of the genome that enables even faster adaptation with very few mutations. In contrast, when change frequency is high, lineages evolve phenotypic plasticity that allows them to be fit in different environments without mutations. Yet, evolving phenotypic plasticity is a comparatively slow process. Under intermediate change frequencies, both strategies occur. CONCLUSIONS: We conclude that evolving a homeostasis mechanisms predisposes lineage to be evolvable to novel environmental conditions. Moreover, after continued evolution, evolvability can be a viable alternative with comparable fitness to regulated phenotypic plasticity in all but the most rapidly changing environments.

In silico evo-devo: reconstructing stages in the evolution of animal segmentation.

BACKGROUND: The evolution of animal segmentation is a major research focus within the field of evolutionary-developmental biology. Most studied segmented animals generate their segments in a repetitive, anterior-to-posterior fashion coordinated with the extension of the body axis from a posterior growth zone. In the current study we ask which selection pressures and ordering of evolutionary events may have contributed to the evolution of this specific segmentation mode. RESULTS: To answer this question we extend a previous in silico simulation model of the evolution of segmentation by allowing the tissue growth pattern to freely evolve. We then determine the likelihood of evolving oscillatory sequential segmentation combined with posterior growth under various conditions, such as the presence or absence of a posterior morphogen gradient or selection for determinate growth. We find that posterior growth with sequential segmentation is the predominant outcome of our simulations only if a posterior morphogen gradient is assumed to have already evolved and selection for determinate growth occurs secondarily. Otherwise, an alternative segmentation mechanism dominates, in which divisions occur in large bursts through the entire tissue and all segments are created simultaneously. CONCLUSIONS: Our study suggests that the ancestry of a posterior signalling centre has played an important role in the evolution of sequential segmentation. In addition, it suggests that determinate growth evolved secondarily, after the evolution of posterior growth. More generally, we demonstrate the potential of evo-devo simulation models that allow us to vary conditions as well as the onset of selection pressures to infer a likely order of evolutionary innovations.

Evolutionarily stable disequilibrium: endless dynamics of evolution in a stationary population.

Evolution is often conceived as changes in the properties of a population over generations. Does this notion exhaust the possible dynamics of evolution? Life is hierarchically organized, and evolution can operate at multiple levels with conflicting tendencies. Using a minimal model of such conflicting multilevel evolution, we demonstrate the possibility of a novel mode of evolution that challenges the above notion: individuals ceaselessly modify their genetically inherited phenotype and fitness along their lines of descent, without involving apparent changes in the properties of the population. The model assumes a population of primitive cells (protocells, for short), each containing a population of replicating catalytic molecules. Protocells are selected towards maximizing the catalytic activity of internal molecules, whereas molecules tend to evolve towards minimizing it in order to maximize their relative fitness within a protocell. These conflicting evolutionary tendencies at different levels and genetic drift drive the lineages of protocells to oscillate endlessly between high and low intracellular catalytic activity, i.e. high and low fitness, along their lines of descent. This oscillation, however, occurs independently in different lineages, so that the population as a whole appears stationary. Therefore, ongoing evolution can be hidden behind an apparently stationary population owing to conflicting multilevel evolution.

Parasites Sustain and Enhance RNA-Like Replicators through Spatial Self-Organisation.

In a prebiotic RNA world, parasitic behaviour may be favoured because template dependent replication happens in trans, thus being altruistic. Spatially extended systems are known to reduce harmful effects of parasites. Here we present a spatial system to show that evolution of replication is (indirectly) enhanced by strong parasites, and we characterise the phase transition that leads to this mode of evolution. Building on the insights of this analysis, we identify two scenarios, namely periodic disruptions and longer replication time-span, in which speciation occurs and an evolved parasite-like lineage enables the evolutionary increase of replication rates in replicators. Finally, we show that parasites co-evolving with replicators are selected to become weaker, i.e. worse templates for replication when the duration of replication is increased. We conclude that parasites may not be considered a problem for evolution in a prebiotic system, but a degree of freedom that can be exploited by evolution to enhance the evolvability of replicators, by means of emergent levels of selection.