Multilevel selection has been indicated as an essential factor for the evolution of complexity in interacting RNA-like replicator systems. There are two types of multilevel selection mechanisms: implicit and explicit. For implicit multilevel selection, spatial self-organization of replicator populations has been suggested, which leads to higher level selection among emergent mesoscopic spatial patterns (traveling waves). For explicit multilevel selection, compartmentalization of replicators by vesicles has been suggested, which leads to higher level evolutionary dynamics among explicitly imposed mesoscopic entities (protocells). Historically, these mechanisms have been given separate consideration for the interests on its own. Here, we make a direct comparison between spatial self-organization and compartmentalization in simulated RNA-like replicator systems. Firstly, we show that both mechanisms achieve the macroscopic stability of a replicator system through the evolutionary dynamics on mesoscopic entities that counteract that of microscopic entities. Secondly, we show that a striking difference exists between the two mechanisms regarding their possible influence on the long-term evolutionary dynamics, which happens under an emergent trade-off situation arising from the multilevel selection. The difference is explained in terms of the difference in the stability between self-organized mesoscopic entities and externally imposed mesoscopic entities. Thirdly, we show that a sharp transition happens in the long-term evolutionary dynamics of the compartmentalized system as a function of replicator mutation rate. Fourthly, the results imply that spatial self-organization can allow the evolution of stable folding in parasitic replicators without any specific functionality in the folding itself. Finally, the results are discussed in relation to the experimental synthesis of chemical Darwinian systems and to the multilevel selection theory of evolutionary biology in general. To conclude, novel evolutionary directions can emerge through interactions between the evolutionary dynamics on multiple levels of organization. Different multilevel selection mechanisms can produce a difference in the long-term evolutionary trend of identical microscopic entities.
Consideration of selection acting on multiple levels of biotic organization is important for understanding of biological evolution in general [1]–[6]. In the studies of prebiotic evolution, it has been shown that some form of multilevel selection is even necessary for the maintenance and evolution of complexity in the interacting RNA replicator system [7]–[12]. The RNA-like replicator system is considered one of the simplest chemical systems that can undergo Darwinian evolution in a self-sustained manner [13], [14]. Hence, RNA replicators have been suggested as the central player of prebiotic evolution in the RNA world hypothesis [15]–[18]. Besides whether or not such replicators—or its analogues [19]—can actually exist (see [20]–[25], for recent progress on this), an interesting question is whether such a chemical replicator system can increase its complexity through evolution and approach the biotic system as we know it. The importance of multilevel selection in prebiotic evolution is based on two problems that arise in the evolution of replicator systems. Firstly, there is a fundamental problem about the accumulation of information in exponentially growing replicators. That is, the maximal length of sequence patterns that can be maintained under the mutation-selection process in a single replicator quasi-species is severely limited by high mutation rates, which are supposed in primordial replication processes based on RNA molecules [13], [26] (see also Text S1 note 1). Hence, a solution to information accumulation has been sought in the symbiosis of multiple species of replicators. As such a solution, a classical study suggested the so-called hypercycle, in which every replicator species catalyzes replication of another species, forming a cyclic network of cooperative interactions [27]. However, there is an inherent problem in such a cooperatively interacting replicator system, in that selection acting on the level of individual replicators favors only the evolution of better templates, but does not favor—even disfavor [28]—the evolution of better catalysts [7], [8]. To overcome this problem, spatial structure in a population has been suggested. Generally speaking, spatial population structure can be classified according to whether it is implicit or explicit: Implicit population structure arises from the birth-death-migration process of individuals themselves, whereas explicit population structure is imposed to a population by some external boundaries (of course, the external factors can depend on the activity of individuals). In the context of prebiotic evolution, both kinds of population structure have been investigated: (i) spatial self-organization of populations in a diffusion-limited, surface-bound replicator system as implicit structure [4]; (ii) compartmentalization of replicators by vesicles as explicit structure [9], [29]. Historically, the two kinds of population structure were given separate consideration for the interests on its own. In both cases, however, the essential process that makes the difference from a well-mixed system is the formation of multilevel selection, i. e. selection operating on the level of microscopic entities (i. e. individual replicators) and on the (various) levels of mesoscopic entities, such as (spiral) waves and protocells, that arise from spatial population structuring. In a previous study, we constructed a computational model that can simulate a surface-bound replicator system and compartmentalized replicator system in a unified framework. We therewith investigated the two different types of spatial population structure—explicit versus implicit—with respect to their influence on the macroscopic stability of different evolving replicator systems [30]. The current study aims to extend the previous study: It makes a direct comparison between the spatial self-organization and compartmentalization with respect to their effects on the eco-evolutionary dynamics of a simple interacting replicator system, particularly, by focusing on the interactions between the dynamics of microscopic and mesoscopic entities. For this sake, we adopt the following research strategies. Firstly, we consider an identical model of interacting replicators in the surface-bound system and in the compartmentalized system so that, while the two systems differ in mesoscopic entities, they share identical microscopic entities. Secondly, we design vesicle-level processes such that they do not introduce an extra burden for survival that is independent of the replicator dynamics itself; e. g. , we neglect the problem of substrate uptake through membranes (see [31], [32] for experimental studies on this issue). These considerations allow us to focus on the effect of spatial population structure itself. Thereby we study how different types of spatial population structure achieve the macroscopic stability of a replicator system, and how the dynamics of different mesoscopic entities influence the evolutionary dynamics of microscopic entities and vice versa.
The replicator model investigated here consists of two types of molecules: replicase and parasite. The replicase can catalyze replication of other molecules, whereas the parasite cannot. The parasite switches between two conformations, viz. folded state and template state. When a parasite is in the folded state, it can facilitate the growth of the vesicle in which it resides (explained later), but cannot be replicated by the replicase; when in the template state, it can be replicated, but cannot facilitate the vesicle growth. We assume the conformation switching is so fast that it is always in equilibrium (see also Text S1 note 2). Hence, the concentration of parasites in the folded state () and that in the template state () can be expressed as and respectively, where is the total concentration, and, and is the equilibrium constant of. Thus, the current replicator system can be represented as follows: (1) where and denote a replicase and parasite molecule respectively; and denotes a complex molecule between and and that between and respectively; represents the generalized resource for replication. In Reaction 1: The consideration of complex formation is to take into account the fact that replication takes finite amount of time, during which the replicase cannot be replicated. Complex formation thus considerably disadvantages the replicase over the parasite (see [28] for more details; see also [33]). The replicator dynamics was modeled in the framework of stochastic cellular automata (CA). The model consists of a two-dimensional grid of squares, where one square can contain at most one molecule. Empty squares are considered to represent the generalized resource (), which limits the maximum number of molecules the system can sustain globally and locally. The model' s dynamics are run by randomly choosing one grid square and then locally applying the algorithm that simulates Reaction 1 and diffusion (both prohibited to occur across grid boundaries). The reaction and diffusion algorithm employed here simulates the chemical reaction dynamics in such a way that the result approaches that of the Gillespie algorithm in the limit of with being constant, where denotes the rate of diffusion. To investigate the evolution of replicators, we introduced “mutations” in and. A newly produced parasite inherits the values of and from its template, but mutation can modify either or by adding to it a number uniformly distributed in. Moreover, and are bounded in (see also Text S1 note 3). The mutation of and occur with a probability and respectively, and they are mutually exclusive. Vesicle-level processes were modeled by using the so-called Cellular Potts Model (CPM) [34], [35]. The CPM is a two-scale stochastic CA: It explicitly defines a mesoscopic entity, “vesicle”, by a set of grid squares having an identical state; at microscopic scale, the updating rules tend to minimize the interface between different vesicles, while at mesoscopic scale the updating rules tend to keep the volume of a vesicle (i. e. the number of the squares constituting a vesicle) at the target volume. We implemented a two-dimensional CPM of squares. We then superimposed it onto the replicator CA plane and coupled the dynamics of the two as follows. Firstly, the molecules in the replicator CA are forbidden to permeate though the vesicle boundaries in the CPM (i. e. , replicators cannot diffuse across vesicle boundaries, and vesicle boundaries cannot move over molecules). Secondly, the dynamics of the target volume of vesicles are governed by the increase due to the occurrence of Reaction 1 (c) and the decrease due to spontaneous decay. That is, if Reaction 1 (c) happens inside a vesicle, its target volume is increased by 1 (if it happens outside vesicles, it is ignored); hence, Reaction 1 (c) can be considered to represent membrane production. Moreover, the target volume decays with rate (the decay rate of target volume). It is worth noting that plays a crucial role for the competition among vesicles because the greater is, the greater chance a vesicle has to expand its actual volume and grow. Thirdly, a vesicle divides when its actual volume exceeds a threshold (“reproduction”): A vesicle is divided along the line of the second principle component; the internal replicators are distributed (or “inherited”) between the two daughter vesicles according to the location (if a complex is divided between the two vesicles, it is dissociated); and the target volume is also distributed between them in proportion to their volume after division. It is also worth noting that determines the size of vesicles and, thus, the number of replicators in vesicles, which is a crucial parameter of compartmentalization. Additionally, the death of vesicles is not simulated as an explicit process, for it happens implicitly through the dynamics already specified (see also Discussion). Importantly, our compartmentalized replicator model ignores the transport process across vesicle boundaries and the resource for the target volume growth. This simplification is to avoid introducing an extra constraint for survival that is not considered by the replicator model per se (cf. the surface model), which allows us to directly compare spatial self-organization and compartmentalization with respect to their effects on the replicator dynamics. Also notable is that vesicle-level selection is nearly at optimal efficiency, for a difference in, which is unbounded, is always reflected in the competence in volume expansion.
The dynamics of the replicator system without population sturcutre can be considered the point of reference for the dynamics with population structure. A simple ordinary differential equation (ODE) model was constructed that describes the well-mixed system of one replicase and one parasite species according to Reaction 1: (2) where, , and denote the concentration of R, L, and respectively; and the dots denote time derivative; and. We study the behavior of Eqn. 2 as a function of and since we later investigate the evolution of these parameters in the systems with spatial population structure. We numerically calculated the equilibrium of Eqn 2 as shown in Fig. 1. The result shows that the values of that allow the stable coexistence of and shift to higher values as increases. The result can be explained with ease: Increasing gives an advantage to the parasite for replication; hence, in order to allow the coexistence, this must be compensated by increasing, which reduces the fraction of time the parasite spends as template. From this argument, one can also expect that if a parasite species with a greater and/or smaller value is introduced to the system, it will out-compete the original parasite; i. e. , there is selection pressure to increase and to decrease. Indeed, this was numerically confirmed by extending Eqn. 2 to include another parasite species (data not shown; the extended version of Eqn. 2 is shown in Text S1). Therefore, if the evolution of and/or is allowed, a well-mixed system will eventually go extinct due to the evolution of too harmful parasites. This was also confirmed by the CA model simulating a well-mixed system (data not shown). In summary, the well-mixed replicator system is evolutionarily unstable, so that some sort of spatial population structure is necessary for the feasibility of the evolving interacting replicator system (see [12] on this point discussed with the model explicitly considering the genotype-phenotype-interaction mapping of replicators; see [28] for more detailed analysis on a similar ODE model). In this section, we examine the evolutionary dynamics of the replicator system with the two types of spatial population structure, viz. compartmentalization and spatial self-organization. We will examine whether the replicator system can survive despite the evolution of parasites and what kind of evolutionary dynamics the system will display. The surface model and compartment model were initialized by inoculating the system with small populations of the replicase and parasite of an equal size. and were allowed to mutate (the initial population was homogeneous), while other parameters were fixed. In the compartment model, molecules were randomly placed inside one large vesicle. In the surface model, replicase molecules were placed in a half circle, and parasite molecules were placed in the other half circle. In the compartment model, the value of (diffusion) was set so great as to remove the effect of spatial self-organization within vesicles in order to simplify the comparison with the surface model (see also Text S1 note 4); otherwise, the two models had identical values in the shared parameters. To obtain the visual image of our models, snapshots of the simulations are shown in Fig. 2. Moreover, Videos S1 and Video S2 depict the spatio-temporal dynamics of the compartment model and that of the surface model respectively (for visibility, Video S1 depicts a smaller scale simulation than that shown in Fig. 2). As Video S2 (surface model) shows, mesoscopic patterns—namely, traveling waves—emerge through the spatial self-organization of the replicator population, which contrasts with the compartment model where mesoscopic patterns—i. e. vesicles—were externally imposed (see [28] for more description on the spatio-temporal dynamics of such waves). In the compartment model, a vesicle expands its volume as the internal replicators multiply, extending into an empty area or pushing other vesicles away (i. e. inter-vesicle competition), and it divides when its volume exceeds the threshold. Once a while, a vesicle also shrinks—or gets squeezed—and disappears from the system (i. e. dies) in concurrence of the extinction of the internal replicators. The long-term behavior of the simulations is depicted as the evolutionary trajectories of the population average of and in Fig. 3 (black and red lines). The trajectories can be separated into two phases: short-term evolution and long-term evolution. In the former, the trajectories go to a contour that gives a (mathematical) functional relationship between and, which indicates the emergence of a trade-off situation in parasites regarding the affinity towards the replicase () and the availability of templates (). In the latter, the trajectories go along the contour, increasing and. These results show that both the surface model and the compartment model allowed the stable coexistence of the replicase and parasite despite the evolutionary instability of the replicator system explained before. Moreover, the two models exhibited a qualitatively identical evolutionary trend such that the parasite, through evolution, increased the fraction of time it spent in the folded state while it also increased the affinity towards the replicase. This result is surprising, given that the folded state has no predefined functionality in the surface model (in fact, it prevents the replication), whereas it does have a predefined functionality in the compartment model (i. e. to facilitate the vesicle growth). To understand these results, we next delve into each model. In this section, we show that the population dynamics of traveling waves exhibit the property of multiplication, variation and inheritance, and therby it ensures the macroscopic stability of the replicator system. Moreover, we analyze what kind of selection pressure exists among waves, which turns out to be qualitatively different from the vesicle-level selection that arises by default. • The two multilevel selection models are quite similar in how they achieve the macroscopic stability of the replicator system: the evolutionary dynamics on the microscopic entities (i. e. replicators) are counteracted by the evolutionary dynamics on the mesoscopic entities (i. e. vesicles or traveling waves). - In the compartment models, the vesicle-level selection operates on the variability in internal replicator systems generated by the stochastic evolutionary dynamics of replicators. - In the surface model, selection operates on the level of traveling waves, which have the feature of multiplication, variation and inheritance. • However, the two types of mesoscopic entities differ in their stability in isolation. - In the compartment model, a vesicle is an externally imposed mesoscopic entity (explicit multilevel selection), and it is less persistent. - In the surface model, a traveling wave pattern is a self-organized mesoscopic entity (implicit multilevel selection), and it is thus more persistent than a vesicle (if it is too unstable, it would not self-organize). • The difference in the stability of mesoscopic entities results in the difference in the focus of mesoscopic selection. - The vesicle-level selection, by default, operates for the longevity of vesicles due to its greater instability. - The wave-level selection operates for the fecundity of waves (i. e. the generation of new traveling waves). • Because multilevel selection keeps the evolution of too severe parasitism at bay, parasites have a trade-off situation between (i. e. affinity to replicase) and (i. e. template availability). Under this trade-off, parasites can adopt two kinds of strategies in the association-dissociation reaction with replicase: (A) increasing the effective rate of association—smaller and —and (B) biasing the equilibrium towards association—greater and. Strategy A weakens the deterministic flow of the replicator dynamics while prohibiting the transient growth of a population consisting of a small number of replicases and parasites. Strategy B strengthens the deterministic flow while enhancing such transient growth. These strategies gain selective differences through the interactions between the dynamics of microscopic entities and those of mesoscopic entities. This produces a novel trend in the long-term evolution of the replicator system, which can differ between the two multilevel selection models. - In the compartment models, the death rate of vesicles depends on the stability of the coexistence between the replicase and parasite in the internal replicator system. If the coexistence is deterministically stable, strengthening the deterministic flow of the internal replicator dynamics is favored. If the coexistence is deterministically unstable, weakening the deterministic flow is favored. The evolutionary dynamics of internal replicator systems are fast when the mutation rate of replicators is high and the population size of internal replicator systems is large. In this case, the coexistence is likely to be deterministically unstable; therefore, weakening the deterministic flow of the replicator dynamics (i. e. Strategy A) is favored. (Similarly, if the internal replicator evolutionary dynamics are slow, Strategy B is favored.) However, this default direction of the vesicle-level selection can be overruled by an additional selection pressure arising from the (predefined) functionality of the folded state to facilitate the vesicle growth. - In the surface model, the establishment of new waves depends on the (transient) growth of a population consisting of a small number of replicases and parasites. Therefore, Strategy B is favored. In this section, we compare the surface model and the compartment model with respect to how the macroscopic stability responds to the change of either the diffusion rate () in the surface model or the threshold volume for division () in the compartment model, whereby we illustrate an interesting difference between the two models. Our choice to focus on and is based on two reasons. Firstly, previous studies have suggested that these parameters significantly affect the macroscopic stability of the replicator system [11], [28]–[30], [33], [42], [43]. Secondly, and play a similar role in how they limit the macroscopic stability; i. e. , increasing and increasing increase the number of replicators involved in local replicator systems that can be considered independent of each other due either to vesicle boundaries or to spatial distance (one traveling wave can be considered to consist of multiple such local replicator systems as seen from the heterogeneity within a wave shown in Fig. 8). Firstly, we investigated what might be called the “ecological” stability of the system, i. e. the range of and for which the replicator system exhibits the macroscopically stable coexistence of the replicase and parasite with no mutation (i. e.). As seen from Fig. 11, while the survival regions from the two models are similar in topology, they significantly differ in its response to the change of or. That is, the survival area in the surface model greatly varies as changes whereas that of the compartment model varies relatively little as changes (the modified compartment model showed qualitatively the same result; see Fig. S1). Additionally, we mention that the compartmentalized system was not viable for, which was most likely because of too great assortment load [29], [42]. Also, the spatial pattern in the surface model appeared different for large diffusion rates as shown in Video S5 (see Text S1 for more on this point). Secondly, we investigated the “evolutionary” stability of the system, i. e. the maximal tolerable value of () for which the system exhibits the macroscopically stable coexistence of the replicase and parasite (with and) as shown in Fig. 12 (the modified compartment model showed qualitatively the same result; see Fig. S2). The result shows that significantly changes as the parameter varies in both models, and the curves of are very similar to each other. These results are in stark contrast to the response of the survival regions seen above. These results indicate that there are two different aspects in the macroscopic stability of replicator systems. One is the range of rate constants for which a system displays the macroscopic stability—“ecological stability”. The other is the degree of perturbation to rate constants (i. e. mutation in the sense used here) for which a system displays the macroscopic stability—“evolutionary stability”. Interestingly, these two aspects do not necessarily correspond to one another. Consequently, the two models showing different degrees of ecological stability can show similar degrees of evolutionary stability. Fig. 11 and 12 depict the general tendency that the macroscopic stability decreases as either or increases. This is explained by the fact that increasing the number of replicators involved in the local replicator systems reduces the stability of the whole system in two ways. Firstly, it decreases the stochasticity in the dynamics—both ecological and evolutionary—of the local replicator systems, which diminishes the relative impact of higher-level selection through reducing the variability among the local systems [9] (in the surface model, the local variability referred here also includes the heterogeneity within each wave). Secondly, it increases the frequency at which stronger parasites appear through mutation in a local replicator system per unit time [29], [44], which speeds up the evolutionary dynamics of local replicator systems (see also the caption to Fig. 6; note that this is irrelevant to the ecological stability by definition). It is worth mentioning that the expansion of the survival region by decreasing decreases as increases as shown in Fig. 11B and S1B (see the survival range of as a function of). This is nicely explained by the first effect explained above and the fact that the deterministic flow of the replicator dynamics strengthens as increases. Additionally, we note that the fraction of replicases within vesicles increases as decreases. A similar observation has been made in other models [30], [45], [46] (esp. [45]) However, as we saw above, the survival area of the surface model is far more sensitive to than that of the compartment model is to (Fig. 11). This can be explained by another effect of changing; that is, decreasing makes it more difficult for a parasite molecule to be in contact with replicase molecules than for a replicase molecule (see [43] for more quantitative investigation on this). This means that decreasing directly disadvantages the parasite. Therefore, the survival area substantially changes as a function of. However, despite this additional effect, the dependency of of the surface model is not necessarily more sensitive to than that of the compartment model is to (Fig. 12). This is explained by the fact that the ecological stability is “offset” by the evolutionary dynamics; i. e. , for greater mutation rates the replicator system tends to go to the lower boundary of the survival region through evolution (it is assumed that the ecologically stable region does not cover the entire span of the parameter range; see the case of in Fig. 12A). To summarize, the macroscopic stability of the interacting replicator system has two different aspects: ecological stability and evolutionary stability. In the surface model, decreasing substantially enhances the ecological stability because decreasing directly decreases the chance of parasites' being in contact with replicases. Contrastingly, in the compartment model, decreasing does not have such a direct effect; hence, its enhancement of the ecological stability is more moderate. However, this apparent advantage of the surface model with respect to the ecological stability is offset by the tendency of the evolutionary dynamics to bring the system to an edge of the ecologically stable parameter region, which indicates the greater importance of the evolutionary stability relative to the ecological stability. Consequently, the two models display a similar response in the evolutionary stability as a function of or.
The current study compared two multilevel selection models of replicator systems that had identical microscopic entities, but had qualitatively different mesoscopic entities. Despite the difference in the mesoscopic entities, we found that the two models were quite similar in how they achieved the macroscopic stability of the replicator system. Moreover, we also discovered an emergent trade-off situation in microscopic entities, which arose due to the multilevel selection (we note that a similar trade-off situation was previously discovered by van Ballegooijen and Boerlijst [41]). Interestingly, under this trade-off situation, microscopic entities displayed novel long-term evolutionary trends, which originated from the interactions between the dynamics of microscopic entities and mesoscopic entities. Furthermore, in contrast to the similarity mentioned above, the two models could sharply differ in the direction of this evolutionary trend, which was explained in terms of the difference in the stability between self-organized mesoscopic entities and externally imposed mesoscopic entities. The surface model showed that the parasite, through long-term evolution, increased the time it spent in the state in which it could not function as template, despite the fact that no functionality was predefined for this state. Since the folding of an RNA molecule is likely to reduce the template activity, this result can be interpreted as the implication that in the diffusion-limited surface-bound system the parasite can evolve stable folding “for free”, i. e. without any specific functionality in the folding. The evolution of stable folding might be used as substrate for the further evolution of new functionality. Hence, the current study revealed a novel advantage of spatial self-organization for the evolution of complexity in RNA-like replicator systems. In the compartment model, we found a simple relationship between the persistency (i. e. longevity) of a vesicle and the dynamical property of the replicator system inside the vesicle. That is, if the evolutionary dynamics of internal replicator systems are fast, the coexistence of the replicase and parasite in internal replicator systems is deterministically unstable; hence, weakening the deterministic flow of the internal replicator dynamics would increase the longevity of vesicles. Similarly, if the evolutionary dynamics of internal replicator systems are slow, the internal replicator coexistence is deterministically stable; hence, strengthening the deterministic flow of the internal replicator dynamics would increase the longevity of vesicles. This point seems to be generally relevant in compartmentalized interacting replicator systems (i. e. the systems where replicases catalyze the replication of templates). The crucial difference between this study and our previous study [30] lies in the type of replicator systems considered: the current study considered a compartmentalized interacting replicator system, whereas the previous study considered a compartmentalized non-interacting replicator system (i. e. the systems where templates self-replicate). Since the evolutionary dynamics of the non-interacting replicator system is, in principle, stable under well-mixed conditions [13], [26], [27], the death of vesicles hardly happened unless externally introduced in the previous study. Therein we reported that it was essential to set the death rate of vesicles—which was one of the parameters—sufficiently high for the vesicle-level selection to be effective. In contrast, vesicle death in the current model was not only an internal—or spontaneous—process due to parasites (i. e. no need to externally introduce vesicle death), but also more frequent to those that contain more severe parasites, which reinforces the vesicle-level selection. Interestingly, therefore, parasites, which were the very reason the higher-level selection had to be considered, actually made the vesicle-level selection more effective. We add that we deliberately avoided making a quantitative comparison between the models with respect to the area of the survival region in the parameter space and the maximum tolerable mutation rate for the following reasons. Firstly, the models have qualitatively different kinds of selection pressure because of the functionality of the folded state of parasites. Secondly, the result of a quantitative comparison depends on the parameters (e. g. one model can have a greater or smaller value of depending on the value of). Thirdly, the models do not have a completely identical set of parameters (most prominently, it is unclear how to scale and). Fourthly, there are two different kinds of population size, i. e. that of microscopic entities and that of mesoscopic entities, which can change through evolution (e. g. Fig. 9). These points make the definition of fairness in quantitative comparison impracticable. Therefore, we concentrated on the qualitative comparison. We should also mention an important simplification made in the current models; i. e. , mutations of replicators were restricted to the perturbation of the two parameters of parasites. Other types of mutation processes can have significant impacts on the eco-evolutionary dynamics of replicator systems. The diversity in the replicase population [30] and/or deleterious mutations [28] can disadvantage parasites by effectively “diluting” the replicase population. Moreover, the explicit consideration of genotype-phenotype-interaction mapping allows a positive feedback in the evolution of these three levels, which stabilizes the whole system [35]. Hence, subjecting a greater degree of freedom to evolution seems to have positive effects on the stability of the replicator systems. In the current study, however, we restricted these processes for a clear-cut elucidation of the effects of different multilevel selection mechanisms. Finally, let us comment on an interesting difference between the modern cell and the protocell conceived in this study (i. e. the vesicle containing replicators). The difference lies in the concept of genotype, which, we commonly assume in evolutionary biology, is a static state of an individual. Such an assumption can be justified for a modern cell because of the small rate of somatic mutation relative to the lifetime of the cell. However, it is clearly invalid for the protocell in the current study because the internal replicator system—of which population composition can be considered as the “genome” of the protocell—greatly changes its state over time comparable to the lifetime of a vesicle (Fig. 4). Stated differently, one cannot separate from each other the timescale of the eco-evolutionary dynamics of the microscopic entities and the population dynamics of the mesoscopic entities. It would be an interesting future project to investigate how these two timescales can be split apart through evolution (see also []). There are ongoing efforts to synthesize chemical systems that can undergo self-sustained Darwinian evolution in the laboratory. In particular, Szostak et al. have been making steady progress towards the laboratory synthesis of model protocells[31], [32], [49]–[53]. In these studies, the diameter of vesicles ranged from to. Assuming that the vesicle dynamics can keep pace with the replicator dynamics, the number of replicators inside the vesicle should be about 100 to counter the evolutionary instability of the kind of replicator systems investigated in this study (Fig. 12B). Thus, the concentration of polynucleotide inside the vesicle should be for and for, where it is assumed that vesicles are spherical and unilamellar (for multilamellar vesicles, the greater concentration would be allowed). Next, there are experimental techniques to amplify polynucleotide in diffusion limited media, the so-called “molecular colony” or “polony” technology [54], [55]; and its possible use for chemical Darwinian systems has been suggested [56]. Under the condition in which this technique is normally practiced, it can be calculated that within a molecular colony a volume of contains about 4000 polynucleotide molecules of about 100 bases and that it takes about 10 sec [which we assume is a generation time () of replicators] for diffusion to displace a molecule of a similar length by (see also Text S1 note 6). The corresponding number of molecules is about 10 for (100 for) in the current surface model. Thus, to obtain a similar situation under the molecular colony technology, either the concentration within a colony must be reduced to 0. 1 nM or the diffusion constant must be reduced to (but see Text S1 note 7). Unfortunately, these figures are not applicable to the best currently available RNA-directed RNA polymerase ribozyme (a candidate of the replicators investigated here), because it is at best three order of magnitude slower polymerase than the protein polymerases used for PCR [57]. Moreover, mineral surfaces have been suggested to have various chemical advantages for the origin of life (e. g. [49], [58], [59], [60]). Besides chemical aspects, it appears to us that mineral surfaces also have an advantage in the current context by confining the replicator in two-dimensional space. However, to our knowledge, experimental data necessary for the type of calculation made above seem to be not yet available. Finally, Koonin and Martin recently discussed a system compartmentalized by inorganic boundaries which are static relative to the internal replicator dynamics [61] (it is reported that related experimental work is in progress [62]). The current model can easily be extended to simulate a simplified version of such a system by disabling the growth and division of vesicles and by allowing the small diffusion of replicators across compartment boundaries. Our preliminary investigation showed that a model with static compartments displayed the formation of the large traveling waves typically spanning more than 10 compartments (where was fixed either to 100 or to 1000, and inside compartments, and across compartment boundaries), which gives rise to the evolutionary dynamics on the level of waves. Thus, interestingly, it is spatial self-organization that plays an important role for the macroscopic stability of the static compartment model, despite the fact that the system is explicitly compartmentalized. Traditionally, multilevel selection has been investigated in the context of altruism-egoism dichotomy. In this context, models are constructed by defining the traits (or strategies) of individuals directly with respect to its fitness contributions at different levels of biological organizations either through a priori conception or through inference from observation as such (e. g. [63], for review; see also Text S1 note 8). By using these models, the classical theory asserts that a trait that disadvantages an individual can still evolve if it advantages certain higher-level biological organizations. By contrast, the models in the current study were constructed without preconceiving what is costly or beneficial on what level of organization (see also Text S1 note 9). Therewith, this study—while concordant with the classical theory—gives two novel implications. Firstly, interactions between the dynamics of microscopic and mesoscopic entities can generate novel evolutionary directions (or strategies) not conceived in the altruism-egoism dichotomy. Secondly, difference in mesoscopic entities can lead to difference in the long-term evolutionary trend of otherwise identical microscopic entities. Hence, we suggest that it is necessary to go beyond the classical modeling framework in order to explore a possible plethora of novel evolutionary directions—beyond that found here and in [41], [43]—that can be generated through multilevel evolution.
The origin of life has ever been attracting scientific inquiries. The RNA world hypothesis suggests that, before the evolution of DNA and protein, primordial life was based on RNA-like molecules both for information storage and chemical catalysis. In the simplest form, an RNA world consists of RNA molecules that can catalyze the replication of their own copies. Thus, an interesting question is whether a system of RNA-like replicators can increase its complexity through Darwinian evolution and approach the modern form of life. It is, however, known that simple natural selection acting on individual replicators is insufficient to account for the evolution of complexity due to the evolution of parasite-like templates. Two solutions have been suggested: compartmentalization of replicators by membranes (i. e. , protocells) and spatial self-organization of a replicator population. Here, we make a direct comparison of the two suggestions by computer simulations. Our results show that the two suggestions can lead to unanticipated and contrasting consequences in the long-term evolution of replicating molecules. The results also imply a novel advantage in the spatial self-organization for the evolution of complexity in RNA-like replicator systems.
computational biology/evolutionary modeling
evolutionary biology
chemical biology/directed molecular evolution
Multilevel Selection in Models of Prebiotic Evolution II: A Direct Comparison of Compartmentalization and Spatial Self-Organization