Modeling the origin of cells.

Protocells (dividing supramolecular vesicles harboring unlinked genetic replicators) are thought to have played an important role in the origin and early evolution of life. Under what scenario did such reproducers come into play? New work by Babajanyan et al. provides theoretical insight into the symbiosis between replicators and reproducing compartments.

Bayes and Darwin: How replicator populations implement Bayesian computations.

Bayesian learning theory and evolutionary theory both formalize adaptive competition dynamics in possibly high-dimensional, varying, and noisy environments. What do they have in common and how do they differ? In this paper, we discuss structural and dynamical analogies and their limits, both at a computational and an algorithmic-mechanical level. We point out mathematical equivalences between their basic dynamical equations, generalizing the isomorphism between Bayesian update and replicator dynamics. We discuss how these mechanisms provide analogous answers to the challenge of adapting to stochastically changing environments at multiple timescales. We elucidate an algorithmic equivalence between a sampling approximation, particle filters, and the Wright-Fisher model of population genetics. These equivalences suggest that the frequency distribution of types in replicator populations optimally encodes regularities of a stochastic environment to predict future environments, without invoking the known mechanisms of multilevel selection and evolvability. A unified view of the theories of learning and evolution comes in sight.

Directional selection coupled with kin selection favors the establishment of senescence.

BACKGROUND: Conventional wisdom in evolutionary theory considers aging as a non-selected byproduct of natural selection. Based on this, conviction aging was regarded as an inevitable phenomenon. It was also thought that in the wild organisms tend to die from diseases, predation and other accidents before they could reach the time when senescence takes its course. Evidence has accumulated, however, that aging is not inevitable and there are organisms that show negative aging even. Furthermore, old age does play a role in the deaths of many different organisms in the wild also. The hypothesis of programmed aging posits that a limited lifespan can evolve as an adaptation (i.e., positively selected for) in its own right, partly because it can enhance evolvability by eliminating "outdated" genotypes. A major shortcoming of this idea is that non-aging sexual individuals that fail to pay the demographic cost of aging would be able to steal good genes by recombination from aging ones. RESULTS: Here, we show by a spatially explicit, individual-based simulation model that aging can positively be selected for if a sufficient degree of kin selection complements directional selection. Under such conditions, senescence enhances evolvability because the rate of aging and the rate of recombination play complementary roles. The selected aging rate is highest at zero recombination (clonal reproduction). In our model, increasing extrinsic mortality favors evolved aging by making up free space, thereby decreasing competition and increasing drift, even when selection is stabilizing and the level of aging is set by mutation-selection balance. Importantly, higher extrinsic mortality is not a substitute for evolved aging under directional selection either. Reduction of relatedness decreases the evolved level of aging; chance relatedness favors non-aging genotypes. The applicability of our results depends on empirical values of directional and kin selection in the wild. CONCLUSIONS: We found that aging can positively be selected for in a spatially explicit population model when sufficiently strong directional and kin selection prevail, even if reproduction is sexual. The view that there is a conceptual link between giving up clonal reproduction and evolving an aging genotype is supported by computational results.

Evolution of linkage and genome expansion in protocells: The origin of chromosomes.

Chromosomes are likely to have assembled from unlinked genes in early evolution. Genetic linkage reduces the assortment load and intragenomic conflict in reproducing protocell models to the extent that chromosomes can go to fixation even if chromosomes suffer from a replicative disadvantage, relative to unlinked genes, proportional to their length. Here we numerically show that chromosomes spread within protocells even if recurrent deleterious mutations affecting replicating genes (as ribozymes) are considered. Dosage effect selects for optimal genomic composition within protocells that carries over to the genic composition of emerging chromosomes. Lacking an accurate segregation mechanism, protocells continue to benefit from the stochastic corrector principle (group selection of early replicators), but now at the chromosome level. A remarkable feature of this process is the appearance of multigene families (in optimal genic proportions) on chromosomes. An added benefit of chromosome formation is an increase in the selectively maintainable genome size (number of different genes), primarily due to the marked reduction of the assortment load. The establishment of chromosomes is under strong positive selection in protocells harboring unlinked genes. The error threshold of replication is raised to higher genome size by linkage due to the fact that deleterious mutations affecting protocells metabolism (hence fitness) show antagonistic (diminishing return) epistasis. This result strengthens the established benefit conferred by chromosomes on protocells allowing for the fixation of highly specific and efficient enzymes.

Phenotypes to remember: Evolutionary developmental memory capacity and robustness.

There is increased awareness of the possibility of developmental memories resulting from evolutionary learning. Genetic regulatory and neural networks can be modelled by analogous formalism raising the important question of productive analogies in principles, processes and performance. We investigate the formation and persistence of various developmental memories of past phenotypes asking how the number of remembered past phenotypes scales with network size, to what extent memories stored form by Hebbian-like rules, and how robust these developmental "devo-engrams" are against networks perturbations (graceful degradation). The analogy between neural and genetic regulatory networks is not superficial in that it allows knowledge transfer between fields that used to be developed separately from each other. Known examples of spectacular phenotypic radiations could partly be accounted for in such terms.

Farming the mitochondrial ancestor as a model of endosymbiotic establishment by natural selection.

The origin of mitochondria was a major evolutionary transition leading to eukaryotes, and is a hotly debated issue. It is unknown whether mitochondria were acquired early or late, and whether it was captured via phagocytosis or syntrophic integration. We present dynamical models to directly simulate the emergence of mitochondria in an ecoevolutionary context. Our results show that regulated farming of prey bacteria and delayed digestion can facilitate the establishment of stable endosymbiosis if prey-rich and prey-poor periods alternate. Stable endosymbiosis emerges without assuming any initial metabolic benefit provided by the engulfed partner, in a wide range of parameters, despite that during good periods farming is costly. Our approach lends support to the appearance of mitochondria before any metabolic coupling has emerged, but after the evolution of primitive phagocytosis by the urkaryote.

Ecological and evolutionary dynamics of interconnectedness and modularity.

In this contribution, we develop a theoretical framework for linking microprocesses (i.e., population dynamics and evolution through natural selection) with macrophenomena (such as interconnectedness and modularity within an ecological system). This is achieved by developing a measure of interconnectedness for population distributions defined on a trait space (generalizing the notion of modularity on graphs), in combination with an evolution equation for the population distribution. With this contribution, we provide a platform for understanding under what environmental, ecological, and evolutionary conditions ecosystems evolve toward being more or less modular. A major contribution of this work is that we are able to decompose the overall driver of changes at the macro level (such as interconnectedness) into three components: (i) ecologically driven change, (ii) evolutionarily driven change, and (iii) environmentally driven change.

Moderate sex between protocells can balance between a decrease in assortment load and an increase in parasite spread.

Sexual reproduction is widespread in nature despite the different kinds of cost that it entails. We do not know exactly when the first sexual process took place and especially why it was beneficial at first. It is clearer why sex is advantageous for the prokaryotes and eukaryotes but the benefit of sex for protocells with individually replicating ribozymes is not yet fully understood. In this context sex is the simple horizontal gene transfer among two protocells that undergo transient fusion. Many authors argue that horizontal gene transfer (HGT) was very common in the early stage of evolution. However, HGT is a risky mechanism considering both the disruption of optimal compositions and the spread of parasites among protocells. In order to test the effects of HGT on the fitness of a protocell population, we explored by numerical simulations those conditions under which fusion might have been beneficial. We investigated multiple conceivable types of fusion in the stochastic corrector model framework and we considered the spread of parasites in every case. Protocells contain up to five species of unlinked, essential ribozymes; if a protocell has the same amount of each, it reaches maximum fitness. Fusion is dangerous not only due to the spread of parasites but also because it can ruin the cells with balanced ribozyme composition. We show that fusion can restore the ribozyme composition of the protocells under certain circumstances (high gene count, intermediate split size and low rate of fusion) and thus it can decrease the effect of the genetic load. Fusion could have been a useful early mechanism in contributing to the reliable coexistence of the different ribozymes before the spread of the chromosomes.

Toward major evolutionary transitions theory 2.0.

The impressive body of work on the major evolutionary transitions in the last 20 y calls for a reconstruction of the theory although a 2D account (evolution of informational systems and transitions in individuality) remains. Significant advances include the concept of fraternal and egalitarian transitions (lower-level units like and unlike, respectively). Multilevel selection, first without, then with, the collectives in focus is an important explanatory mechanism. Transitions are decomposed into phases of origin, maintenance, and transformation (i.e., further evolution) of the higher level units, which helps reduce the number of transitions in the revised list by two so that it is less top-heavy. After the transition, units show strong cooperation and very limited realized conflict. The origins of cells, the emergence of the genetic code and translation, the evolution of the eukaryotic cell, multicellularity, and the origin of human groups with language are reconsidered in some detail in the light of new data and considerations. Arguments are given why sex is not in the revised list as a separate transition. Some of the transitions can be recursive (e.g., plastids, multicellularity) or limited (transitions that share the usual features of major transitions without a massive phylogenetic impact, such as the micro- and macronuclei in ciliates). During transitions, new units of reproduction emerge, and establishment of such units requires high fidelity of reproduction (as opposed to mere replication).

Insight into the ten-penny problem: guiding search by constraints and maximization.

For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Recently, promising hybrid cognitive models attempt to merge both approaches. In this vein, we were interested in the interplay of constraints and heuristic search, when problem solvers were asked to solve a difficult multi-step problem, the ten-penny problem. In three experimental groups and one control group (N = 4 × 30) we aimed at revealing, what constraints drive problem difficulty in this problem, and how relaxing constraints, and providing an efficient search criterion facilitates the solution. We also investigated how the search behavior of successful problem solvers and non-solvers differ. We found that relaxing constraints was necessary but not sufficient to solve the problem. Without efficient heuristics that facilitate the restriction of the search space, and testing the progress of the problem solving process, the relaxation of constraints was not effective. Relaxing constraints and applying the search criterion are both necessary to effectively increase solution rates. We also found that successful solvers showed promising moves earlier and had a higher maximization and variation rate across solution attempts. We propose that this finding sheds light on how different strategies contribute to solving difficult problems. Finally, we speculate about the implications of our findings for insight problem solving.

Founder of systems chemistry and foundational theoretical biologist: Tibor Gánti (1933-2009).

With his chemoton theory theoretical biologist and chemical engineer Tibor Gánti was one of the most outstanding intellects behind systems chemistry and the at the foundations of theoretical biology. A brief review of his oeuvre is presented. This essay introduces a special issue dedicated to his memory.

Metabolically Coupled Replicator Systems: Overview of an RNA-world model concept of prebiotic evolution on mineral surfaces.

Metabolically Coupled Replicator Systems (MCRS) are a family of models implementing a simple, physico-chemically and ecologically feasible scenario for the first steps of chemical evolution towards life. Evolution in an abiotically produced RNA-population sets in as soon as any one of the RNA molecules become autocatalytic by engaging in template directed self-replication from activated monomers, and starts increasing exponentially. Competition for the finite external supply of monomers ignites selection favouring RNA molecules with catalytic activity helping self-replication by any possible means. One way of providing such autocatalytic help is to become a replicase ribozyme. An additional way is through increasing monomer supply by contributing to monomer synthesis from external resources, i.e., by evolving metabolic enzyme activity. Retroevolution may build up an increasingly autotrophic, cooperating community of metabolic ribozymes running an increasingly complicated and ever more efficient metabolism. Maintaining such a cooperating community of metabolic replicators raises two serious ecological problems: one is keeping the system coexistent in spite of the different replicabilities of the cooperating replicators; the other is constraining parasitism, i.e., keeping "cheaters" in check. Surface-bound MCRS provide an automatic solution to both problems: coexistence and parasite resistance are the consequences of assuming the local nature of metabolic interactions. In this review we present an overview of results published in previous articles, showing that these effects are, indeed, robust in different MCRS implementations, by considering different environmental setups and realistic chemical details in a few different models. We argue that the MCRS model framework naturally offers a suitable starting point for the future modelling of membrane evolution and extending the theory to cover the emergence of the first protocell in a self-consistent manner. The coevolution of metabolic, genetic and membrane functions is hypothesized to follow the progressive sequestration scenario, the conceptual blueprint for the earliest steps of protocell evolution.

"Synergistic selection": a Darwinian frame for the evolution of complexity.

Non-Darwinian theories about the emergence and evolution of complexity date back at least to Lamarck, and include those of Herbert Spencer and the "emergent evolution" theorists of the later nineteenth and early twentieth centuries. In recent decades, this approach has mostly been espoused by various practitioners in biophysics and complexity theory. However, there is a Darwinian alternative - in essence, an economic theory of complexity - proposing that synergistic effects of various kinds have played an important causal role in the evolution of complexity, especially in the "major transitions". This theory is called the "synergism hypothesis". We posit that otherwise unattainable functional advantages arising from various cooperative phenomena have been favored over time in a dynamic that the late John Maynard Smith characterized and modeled as "synergistic selection". The term highlights the fact that synergistic "wholes" may become interdependent "units" of selection. We provide some historical perspective on this issue, as well as a brief explication of the underlying theory and the concept of synergistic selection, and we describe two relevant models.

Phenotypic plasticity, the Baldwin effect, and the speeding up of evolution: the computational roots of an illusion.

An increasing number of dissident voices claim that the standard neo-Darwinian view of genes as 'leaders' and phenotypes as 'followers' during the process of adaptive evolution should be turned on its head. This idea is older than the rediscovery of Mendel's laws of inheritance, with the turn-of-the-twentieth-century notion eventually labeled as the 'Baldwin effect' as one of the many ways in which the standard neo-Darwinian view can be turned around. A condition for this effect is that environmentally induced variation such as phenotypic plasticity or learning is crucial for the initial establishment of a trait. This gives the additional time for natural selection to act on genetic variation and the adaptive trait can be eventually encoded in the genotype. An influential paper published in the late 1980s claimed the Baldwin effect to happen in computer simulations, and avowed that it was crucial to solve a difficult adaptive task. This generated much excitement among scholars in various disciplines that regard neo-Darwinian accounts to explain the evolutionary emergence of high-order phenotypic traits such as consciousness or language almost hopeless. Here, we use analytical and computational approaches to show that a standard population genetics treatment can easily crack what the scientific community has granted as an unsolvable adaptive problem without learning. Evolutionary psychologists and linguists have invoked the (claimed) Baldwin effect to make wild assertions that should not be taken seriously. What the Baldwin effect needs are plausible case-histories.

Primordial evolvability: Impasses and challenges.

While it is generally agreed that some kind of replicating non-living compounds were the precursors of life, there is much debate over their possible chemical nature. Metabolism-first approaches propose that mutually catalytic sets of simple organic molecules could be capable of self-replication and rudimentary chemical evolution. In particular, the graded autocatalysis replication domain (GARD) model, depicting assemblies of amphiphilic molecules, has received considerable interest. The system propagates compositional information across generations and is suggested to be a target of natural selection. However, evolutionary simulations indicate that the system lacks selectability (i.e. selection has negligible effect on the equilibrium concentrations). We elaborate on the lessons learnt from the example of the GARD model and, more widely, on the issue of evolvability, and discuss the implications for similar metabolism-first scenarios. We found that simple incorporation-type chemistry based on non-covalent bonds, as assumed in GARD, is unlikely to result in alternative autocatalytic cycles when catalytic interactions are randomly distributed. An even more serious problem stems from the lognormal distribution of catalytic factors, causing inherent kinetic instability of such loops, due to the dominance of efficiently catalyzed components that fail to return catalytic aid. Accordingly, the dynamics of the GARD model is dominated by strongly catalytic, but not auto-catalytic, molecules. Without effective autocatalysis, stable hereditary propagation is not possible. Many repetitions and different scaling of the model come to no rescue. Despite all attempts to show the contrary, the GARD model is not evolvable, in contrast to reflexively autocatalytic networks, complemented by rare uncatalyzed reactions and compartmentation. The latter networks, resting on the creation and breakage of chemical bonds, can generate novel ('mutant') autocatalytic loops from a given set of environmentally available compounds. Real chemical reactions that make or break covalent bonds, rather than mere incorporation of components, are necessary for open-ended evolvability. The issue of whether or not several concrete chemical systems (rather than singular curiosities) could realize reflexively autocatalytic macromolecular networks will ultimately determine the relevance of metabolism-first approaches to the origin of life, as stepping stones towards true open-endedness that requires the combination of rich combinatorial chemistry controlled by information stored in template replicators.

Evolution of the division of labor between genes and enzymes in the RNA world.

The RNA world is a very likely interim stage of the evolution after the first replicators and before the advent of the genetic code and translated proteins. Ribozymes are known to be able to catalyze many reaction types, including cofactor-aided metabolic transformations. In a metabolically complex RNA world, early division of labor between genes and enzymes could have evolved, where the ribozymes would have been transcribed from the genes more often than the other way round, benefiting the encapsulating cells through this dosage effect. Here we show, by computer simulations of protocells harboring unlinked RNA replicators, that the origin of replicational asymmetry producing more ribozymes from a gene template than gene strands from a ribozyme template is feasible and robust. Enzymatic activities of the two modeled ribozymes are in trade-off with their replication rates, and the relative replication rates compared to those of complementary strands are evolvable traits of the ribozymes. The degree of trade-off is shown to have the strongest effect in favor of the division of labor. Although some asymmetry between gene and enzymatic strands could have evolved even in earlier, surface-bound systems, the shown mechanism in protocells seems inevitable and under strong positive selection. This could have preadapted the genetic system for transcription after the subsequent origin of chromosomes and DNA.

Gause's principle and the effect of resource partitioning on the dynamical coexistence of replicating templates.

Models of competitive template replication, although basic for replicator dynamics and primordial evolution, have not yet taken different sequences explicitly into account, neither have they analyzed the effect of resource partitioning (feeding on different resources) on coexistence. Here we show by analytical and numerical calculations that Gause's principle of competitive exclusion holds for template replicators if resources (nucleotides) affect growth linearly and coexistence is at fixed point attractors. Cases of complementary or homologous pairing between building blocks with parallel or antiparallel strands show no deviation from the rule that the nucleotide compositions of stably coexisting species must be different and there cannot be more coexisting replicator species than nucleotide types. Besides this overlooked mechanism of template coexistence we show also that interesting sequence effects prevail as parts of sequences that are copied earlier affect coexistence more strongly due to the higher concentration of the corresponding replication intermediates. Template and copy always count as one species due their constraint of strict stoichiometric coupling. Stability of fixed-point coexistence tends to decrease with the length of sequences, although this effect is unlikely to be detrimental for sequences below 100 nucleotides. In sum, resource partitioning (niche differentiation) is the default form of competitive coexistence for replicating templates feeding on a cocktail of different nucleotides, as it may have been the case in the RNA world. Our analysis of different pairing and strand orientation schemes is relevant for artificial and potentially astrobiological genetics.

Real ribozymes suggest a relaxed error threshold.

The error threshold for replication, the critical copying fidelity below which the fittest genotype deterministically disappears, limits the length of the genome that can be maintained by selection. Primordial replication must have been error-prone, and so early replicators are thought to have been necessarily short. The error threshold also depends on the fitness landscape. In an RNA world, many neutral and compensatory mutations can raise the threshold, below which the functional phenotype, rather than a particular sequence, is still present. Here we show, on the basis of comparative analysis of two extensively mutagenized ribozymes, that with a copying fidelity of 0.999 per digit per replication the phenotypic error threshold rises well above 7,000 nucleotides, which permits the selective maintenance of a functionally rich riboorganism with a genome of more than 100 different genes, the size of a tRNA. This requires an order of magnitude of improvement in the accuracy of in vitro-generated polymerase ribozymes. Incidentally, this genome size coincides with that estimated for a minimal cell achieved by top-down analysis, omitting the genes dealing with translation.