Stochastic parabolic growth promotes coexistence and a relaxed error threshold in RNA-like replicator populations

The RNA world hypothesis proposes that during the early evolution of life, primordial genomes of the first self-propagating evolutionary units existed in the form of RNA-like polymers. Autonomous, non-enzymatic, and sustained replication of such information carriers presents a problem, because product formation and hybridization between template and copy strands reduces replication speed. Kinetics of growth is then parabolic with the benefit of entailing competitive coexistence, thereby maintaining diversity. Here, we test the information-maintaining ability of parabolic growth in stochastic multispecies population models under the constraints of constant total population size and chemostat conditions. We find that large population sizes and small differences in the replication rates favor the stable coexistence of the vast majority of replicator species ('genes'), while the error threshold problem is alleviated relative to exponential amplification. In addition, sequence properties (GC content) and the strength of resource competition mediated by the rate of resource inflow determine the number of coexisting variants, suggesting that fluctuations in building block availability favored repeated cycles of exploration and exploitation. Stochastic parabolic growth could thus have played a pivotal role in preserving viable sequences generated by random abiotic synthesis and providing diverse genetic raw material to the early evolution of functional ribozymes.

A neural network-based model framework for cell-fate decisions and development

Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.

Characteristic processes of human evolution caused the Anthropocene and may obstruct its global solutions

We propose that the global environmental crises of the Anthropocene are the outcome of a ratcheting process in long-term human evolution which has favoured groups of increased size and greater environmental exploitation. To explore this hypothesis, we review the changes in the human ecological niche. Evidence indicates the growth of the human niche has been facilitated by group-level cultural traits for environmental control. Following this logic, sustaining the biosphere under intense human use will probably require global cultural traits, including legal and technical systems. We investigate the conditions for the evolution of global cultural traits. We estimate that our species does not exhibit adequate population structure to evolve these traits. Our analysis suggests that characteristic patterns of human group-level cultural evolution created the Anthropocene and will work against global collective solutions to the environmental challenges it poses. We illustrate the implications of this theory with alternative evolutionary paths for humanity. We conclude that our species must alter longstanding patterns of cultural evolution to avoid environmental disaster and escalating between-group competition. We propose an applied research and policy programme with the goal of avoiding these outcomes. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

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.

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.

Human socio-cultural evolution in light of evolutionary transitions: introduction to the theme issue

Human societies are no doubt complex. They are characterized by division of labour, multiple hierarchies, intricate communication networks and transport systems. These phenomena and others have led scholars to propose that human society may be, or may become, a new hierarchical level that may dominate the individual humans within it, similar to the relations between an organism and its cells, or an ant colony and its members. Recent discussions of the possibility of this major evolutionary transition in individuality (ETI) raise interesting and controversial questions that are explored in the present issue from four different complementary perspectives. (i) The general theory of ETIs. (ii) The unique aspects of cultural evolution. (iii) The evolutionary history and pre-history of humans. (iv) Specific routes of a possible human ETI. Each perspective uses different tools provided by different disciplines: biology, anthropology, cultural evolution, systems theory, psychology, economy, linguistics and philosophy of science. Altogether, this issue provides a broad and rich application of the notion of ETI to human past, present and perhaps also future evolution. It presents important case studies, new theoretical results and novel questions for future research. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

Evolutionary ecology of language origins through confrontational scavenging

A dynamic model and an agent-based simulation model implementing the assumptions of the confrontational scavenging hypothesis on early protolanguage as an adaptive response of Homo erectus to gradual change in their habitat has been developed and studied. The core assumptions of the hypothesis and the model scenario are the pre-adaptation of our ancestors to occupy the ecological niche that they constructed for themselves by having evolved displaced communication and a rudimentary tool manufacture, two features allowing them to use a new, concentrated and abundant resource-megafauna carrion-on the savannahs replacing arboreal habitats owing to the drying climate of East Africa at about 2 Ma. The shift in diet required coordinated cooperation by the hominin scavengers confronted with concurrent predators. Power scavenging compelled displaced symbolic communication featuring a limited semantic range; syntax was not yet required. We show that phenotypic evolution on the accuracy of information transfer between cooperating hominins is a necessary and sufficient condition for the population of agents to survive the diet shift. Both the individual and the group fitness of the hominin horde increased with the accuracy of their protolanguage, with decreasing time allocated to foraging and thus more time left for culture. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

Search and insight processes in card sorting games

Insight problems are particularly interesting, because problems which require restructuring allow researchers to investigate the underpinnings of the Aha-experience, creativity and out of the box thinking. There is a need for new insight tasks to probe and extend the limits of existing theories and cognitive frameworks. To shed more light on this fascinating issue, we addressed the question: Is it possible to convey a well-known card sorting game into an insight task? We introduced different conditions and tested them via two online experiments (N = 546). Between the conditions we systematically varied the available perceptual features, and the existence of non-obvious rules. We found that our card sorting game elicited insight experience. In the first experiment, our data revealed that solution strategies and insight experience varied by the availability and saliency of perceptual features. The discovery of a non-obvious rule, which is not hinted at by perceptual features, was most difficult. With our new paradigm, we were able to construe ambiguous problems which allowed participants to find more than one solution strategy. Interestingly, we realized interindividual preferences for different strategies. The same problem drove strategies which either relied on feature integration or on more deliberate strategies. The second experiment varied the degree of independence of a sorting rule from the standard rules which were in accordance with prior knowledge. It was shown that the more independent the hidden rule was, the more difficult the task became. In sum, we demonstrated a new insight task which extended the available task domains and shed light on sequential and multi-step rule learning problems. Finally, we provided a first sketch of a cognitive model that should help to integrate the data within the existing literature on cognitive models and speculated about the generalizability of the interplay of prior knowledge modification and variation for problem solving.

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.

Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. Is there any computational domain that is flexible enough to provide solutions to such diverse problems and can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of neural informational patterns, is a promising candidate. Here we implement imperfect information copying through one reservoir computing unit teaching another. Teacher and learner roles are assigned dynamically based on evaluation of the readout signal. We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We introduce a novel analysis method, neural phylogenies, that displays the unfolding of the neural-evolutionary process.

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.

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.

The emergence of chromosomes harboring several genes is a crucial ingredient of the major evolutionary transition from naked replicators to cells. Linkage of replicating genes reduces conflict between them and alleviates the problem of chance loss of genes upon stochastic protocell fission. The emerging organization of protocells maintaining several segregating chromosomes with balanced gene composition also allows for an increase in the number of gene types despite recurrent deleterious mutations. We suggest that this interim genomic organization enabled protocells to evolve specific and efficient enzymes and paved the way toward an accurate mechanism for chromosome segregation later in evolution.

Encoding Temporal Regularities and Information Copying in Hippocampal Circuits

Discriminating, extracting and encoding temporal regularities is a critical requirement in the brain, relevant to sensory-motor processing and learning. However, the cellular mechanisms responsible remain enigmatic; for example, whether such abilities require specific, elaborately organized neural networks or arise from more fundamental, inherent properties of neurons. Here, using multi-electrode array technology, and focusing on interval learning, we demonstrate that sparse reconstituted rat hippocampal neural circuits are intrinsically capable of encoding and storing sub-second-order time intervals for over an hour timescale, represented in changes in the spatial-temporal architecture of firing relationships among populations of neurons. This learning is accompanied by increases in mutual information and transfer entropy, formal measures related to information storage and flow. Moreover, temporal relationships derived from previously trained circuits can act as templates for copying intervals into untrained networks, suggesting the possibility of circuit-to-circuit information transfer. Our findings illustrate that dynamic encoding and stable copying of temporal relationships are fundamental properties of simple in vitro networks, with general significance for understanding elemental principles of information processing, storage and replication.

Multilevel selection as Bayesian inference, major transitions in individuality as structure learning

Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level evolutionary units. Although this process has been likened to the recursive combination of pre-adapted sub-solutions in the framework of learning theory, no general mathematical formalization of this analogy has been provided yet. Here we show, building on former results connecting replicator dynamics and Bayesian update, that (i) evolution of a hierarchical population under multilevel selection is equivalent to Bayesian inference in hierarchical Bayesian models and (ii) evolutionary transitions in individuality, driven by synergistic fitness interactions, is equivalent to learning the structure of hierarchical models via Bayesian model comparison. These correspondences support a learning theory-oriented narrative of evolutionary complexification: the complexity and depth of the hierarchical structure of individuality mirror the amount and complexity of data that have been integrated about the environment through the course of evolutionary history.

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.

The evolutionary dynamics of language

The well-established framework of evolutionary dynamics can be applied to the fascinating open problems how human brains are able to acquire and adapt language and how languages change in a population. Schemas for handling grammatical constructions are the replicating unit. They emerge and multiply with variation in the brains of individuals and undergo selection based on their contribution to needed expressive power, communicative success and the reduction of cognitive effort. Adopting this perspective has two major benefits. (i) It makes a bridge to neurobiological models of the brain that have also adopted an evolutionary dynamics point of view, thus opening a new horizon for studying how human brains achieve the remarkably complex competence for language. And (ii) it suggests a new foundation for studying cultural language change as an evolutionary dynamics process. The paper sketches this novel perspective, provides references to empirical data and computational experiments, and points to open problems.

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.

Breath-giving cooperation: critical review of origin of mitochondria hypotheses : Major unanswered questions point to the importance of early ecology

The origin of mitochondria is a unique and hard evolutionary problem, embedded within the origin of eukaryotes. The puzzle is challenging due to the egalitarian nature of the transition where lower-level units took over energy metabolism. Contending theories widely disagree on ancestral partners, initial conditions and unfolding of events. There are many open questions but there is no comparative examination of hypotheses. We have specified twelve questions about the observable facts and hidden processes leading to the establishment of the endosymbiont that a valid hypothesis must address. We have objectively compared contending hypotheses under these questions to find the most plausible course of events and to draw insight on missing pieces of the puzzle. Since endosymbiosis borders evolution and ecology, and since a realistic theory has to comply with both domains' constraints, the conclusion is that the most important aspect to clarify is the initial ecological relationship of partners. Metabolic benefits are largely irrelevant at this initial phase, where ecological costs could be more disruptive. There is no single theory capable of answering all questions indicating a severe lack of ecological considerations. A new theory, compliant with recent phylogenomic results, should adhere to these criteria.

REVIEWERS

This article was reviewed by Michael W. Gray, William F. Martin and Purificación López-García.

Transient compartmentalization of RNA replicators prevents extinction due to parasites

The appearance of molecular replicators (molecules that can be copied) was probably a critical step in the origin of life. However, parasitic replicators would take over and would have prevented life from taking off unless the replicators were compartmentalized in reproducing protocells. Paradoxically, control of protocell reproduction would seem to require evolved replicators. We show here that a simpler population structure, based on cycles of transient compartmentalization (TC) and mixing of RNA replicators, is sufficient to prevent takeover by parasitic mutants. TC tends to select for ensembles of replicators that replicate at a similar rate, including a diversity of parasites that could serve as a source of opportunistic functionality. Thus, TC in natural, abiological compartments could have allowed life to take hold.

An evolutionary perspective on the systems of adaptive immunity

We propose an evolutionary perspective to classify and characterize the diverse systems of adaptive immunity that have been discovered across all major domains of life. We put forward a new function-based classification according to the way information is acquired by the immune systems: Darwinian immunity (currently known from, but not necessarily limited to, vertebrates) relies on the Darwinian process of clonal selection to 'learn' by cumulative trial-and-error feedback; Lamarckian immunity uses templated targeting (guided adaptation) to internalize heritable information on potential threats; finally, shotgun immunity operates through somatic mechanisms of variable targeting without feedback. We argue that the origin of Darwinian (but not Lamarckian or shotgun) immunity represents a radical innovation in the evolution of individuality and complexity, and propose to add it to the list of major evolutionary transitions. While transitions to higher-level units entail the suppression of selection at lower levels, Darwinian immunity re-opens cell-level selection within the multicellular organism, under the control of mechanisms that direct, rather than suppress, cell-level evolution for the benefit of the individual. From a conceptual point of view, the origin of Darwinian immunity can be regarded as the most radical transition in the history of life, in which evolution by natural selection has literally re-invented itself. Furthermore, the combination of clonal selection and somatic receptor diversity enabled a transition from limited to practically unlimited capacity to store information about the antigenic environment. The origin of Darwinian immunity therefore comprises both a transition in individuality and the emergence of a new information system - the two hallmarks of major evolutionary transitions. Finally, we present an evolutionary scenario for the origin of Darwinian immunity in vertebrates. We propose a revival of the concept of the 'Big Bang' of vertebrate immunity, arguing that its origin involved a 'difficult' (i.e. low-probability) evolutionary transition that might have occurred only once, in a common ancestor of all vertebrates. In contrast to the original concept, we argue that the limiting innovation was not the generation of somatic diversity, but the regulatory circuitry needed for the safe operation of amplifiable immune responses with somatically acquired targeting. Regulatory complexity increased abruptly by genomic duplications at the root of the vertebrate lineage, creating a rare opportunity to establish such circuitry. We discuss the selection forces that might have acted at the origin of the transition, and in the subsequent stepwise evolution leading to the modern immune systems of extant vertebrates.

Cognitive Architecture with Evolutionary Dynamics Solves Insight Problem

In this paper, we show that a neurally implemented a cognitive architecture with evolutionary dynamics can solve the four-tree problem. Our model, called Darwinian Neurodynamics, assumes that the unconscious mechanism of problem solving during insight tasks is a Darwinian process. It is based on the evolution of patterns that represent candidate solutions to a problem, and are stored and reproduced by a population of attractor networks. In our first experiment, we used human data as a benchmark and showed that the model behaves comparably to humans: it shows an improvement in performance if it is pretrained and primed appropriately, just like human participants in Kershaw et al. (2013)'s experiment. In the second experiment, we further investigated the effects of pretraining and priming in a two-by-two design and found a beginner's luck type of effect: solution rate was highest in the condition that was primed, but not pretrained with patterns relevant for the task. In the third experiment, we showed that deficits in computational capacity and learning abilities decreased the performance of the model, as expected. We conclude that Darwinian Neurodynamics is a promising model of human problem solving that deserves further investigation.

Breeding novel solutions in the brain: a model of Darwinian neurodynamics

Background: The fact that surplus connections and neurons are pruned during development is well established. We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. We present a model for Darwinian evolutionary search for candidate solutions in the brain.

Methods: We combine known components of the brain – recurrent neural networks (acting as attractors), the action selection loop and implicit working memory – to provide the appropriate Darwinian architecture. We employ a population of attractor networks with palimpsest memory. The action selection loop is employed with winners-share-all dynamics to select for candidate solutions that are transiently stored in implicit working memory.

Results: We document two processes: selection of stored solutions and evolutionary search for novel solutions. During the replication of candidate solutions attractor networks occasionally produce recombinant patterns, increasing variation on which selection can act. Combinatorial search acts on multiplying units (activity patterns) with hereditary variation and novel variants appear due to (i) noisy recall of patterns from the attractor networks, (ii) noise during transmission of candidate solutions as messages between networks, and, (iii) spontaneously generated, untrained patterns in spurious attractors.

Conclusions: Attractor dynamics of recurrent neural networks can be used to model Darwinian search. The proposed architecture can be used for fast search among stored solutions (by selection) and for evolutionary search when novel candidate solutions are generated in successive iterations. Since all the suggested components are present in advanced nervous systems, we hypothesize that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants.

Breeding novel solutions in the brain: a model of Darwinian neurodynamics

Background: The fact that surplus connections and neurons are pruned during development is well established. We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. We present a model for Darwinian evolutionary search for candidate solutions in the brain. Methods: We combine known components of the brain - recurrent neural networks (acting as attractors), the action selection loop and implicit working memory - to provide the appropriate Darwinian architecture. We employ a population of attractor networks with palimpsest memory. The action selection loop is employed with winners-share-all dynamics to select for candidate solutions that are transiently stored in implicit working memory. Results: We document two processes: selection of stored solutions and evolutionary search for novel solutions. During the replication of candidate solutions attractor networks occasionally produce recombinant patterns, increasing variation on which selection can act. Combinatorial search acts on multiplying units (activity patterns) with hereditary variation and novel variants appear due to (i) noisy recall of patterns from the attractor networks, (ii) noise during transmission of candidate solutions as messages between networks, and, (iii) spontaneously generated, untrained patterns in spurious attractors. Conclusions: Attractor dynamics of recurrent neural networks can be used to model Darwinian search. The proposed architecture can be used for fast search among stored solutions (by selection) and for evolutionary search when novel candidate solutions are generated in successive iterations. Since all the suggested components are present in advanced nervous systems, we hypothesize that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants.

What can ecosystems learn? Expanding evolutionary ecology with learning theory

Background

The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole?

Results

Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, ‘unsupervised learning’, well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community’s response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts.

Conclusions

This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions.

Reviewers

This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Neuronal boost to evolutionary dynamics

Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild.

Problem solving stages in the five square problem

According to the restructuring hypothesis, insight problem solving typically progresses through consecutive stages of search, impasse, insight, and search again for someone, who solves the task. The order of these stages was determined through self-reports of problem solvers and has never been verified behaviorally. We asked whether individual analysis of problem solving attempts of participants revealed the same order of problem solving stages as defined by the theory and whether their subjective feelings corresponded to the problem solving stages they were in. Our participants tried to solve the Five-Square problem in an online task, while we recorded the time and trajectory of their stick movements. After the task they were asked about their feelings related to insight and some of them also had the possibility of reporting impasse while working on the task. We found that the majority of participants did not follow the classic four-stage model of insight, but had more complex sequences of problem solving stages, with search and impasse recurring several times. This means that the classic four-stage model is not sufficient to describe variability on the individual level. We revised the classic model and we provide a new model that can generate all sequences found. Solvers reported insight more often than non-solvers and non-solvers reported impasse more often than solvers, as expected; but participants did not report impasse more often during behaviorally defined impasse stages than during other stages. This shows that impasse reports might be unreliable indicators of impasse. Our study highlights the importance of individual analysis of problem solving behavior to verify insight theory.

Fitness Landscapes of Functional RNAs

The notion of fitness landscapes, a map between genotype and fitness, was proposed more than 80 years ago. For most of this time data was only available for a few alleles, and thus we had only a restricted view of the whole fitness landscape. Recently, advances in genetics and molecular biology allow a more detailed view of them. Here we review experimental and theoretical studies of fitness landscapes of functional RNAs, especially aptamers and ribozymes. We find that RNA structures can be divided into critical structures, connecting structures, neutral structures and forbidden structures. Such characterisation, coupled with theoretical sequence-to-structure predictions, allows us to construct the whole fitness landscape. Fitness landscapes then can be used to study evolution, and in our case the development of the RNA world.

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).

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.

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.

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.

The dynamics of the RNA world: insights and challenges

The RNA world hypothesis of the origin of life, in which RNA emerged as both enzyme and information carrier, is receiving solid experimental support. The prebiotic synthesis of biomolecules, the catalytic aid offered by mineral surfaces, and the vast enzymatic repertoire of ribozymes are only pieces of the origin of life puzzle; the full picture can only emerge if the pieces fit together by either following from one another or coexisting with each other. Here, we review the theory of the origin, maintenance, and enhancement of the RNA world as an evolving population of dynamical systems. The dynamical view of the origin of life allows us to pinpoint the missing and the not fitting pieces: (1) How can the first self-replicating ribozyme emerge in the absence of template-directed information replication? (2) How can nucleotide replicators avoid competitive exclusion despite utilizing the very same resources (nucleobases)? (3) How can the information catastrophe be avoided? (4) How can enough genes integrate into a cohesive system in order to transition to a cellular stage? (5) How can the way information is stored and metabolic complexity coevolve to pave to road leading out of the RNA world to the present protein-DNA world?

"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.

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.

Local neutral networks help maintain inaccurately replicating ribozymes

The error threshold of replication limits the selectively maintainable genome size against recurrent deleterious mutations for most fitness landscapes. In the context of RNA replication a distinction between the genotypic and the phenotypic error threshold has been made; where the latter concerns the maintenance of secondary structure rather than sequence. RNA secondary structure is treated as a proxy for function. The phenotypic error threshold allows higher per digit mutation rates than its genotypic counterpart, and is known to increase with the frequency of neutral mutations in sequence space. Here we show that the degree of neutrality, i.e. the frequency of nearest-neighbour (one-step) neutral mutants is a remarkably accurate proxy for the overall frequency of such mutants in an experimentally verifiable formula for the phenotypic error threshold; this we achieve by the full numerical solution for the concentration of all sequences in mutation-selection balance up to length 16. We reinforce our previous result that currently known ribozymes could be selectively maintained by the accuracy known from the best available polymerase ribozymes. Furthermore, we show that in silico stabilizing selection can increase the mutational robustness of ribozymes due to the fact that they were produced by artificial directional selection in the first place. Our finding offers a better understanding of the error threshold and provides further insight into the plausibility of an ancient RNA world.

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.

Early evolution of efficient enzymes and genome organization

Background

Cellular life with complex metabolism probably evolved during the reign of RNA, when it served as both information carrier and enzyme. Jensen proposed that enzymes of primordial cells possessed broad specificities: they were generalist. When and under what conditions could primordial metabolism run by generalist enzymes evolve to contemporary-type metabolism run by specific enzymes?

Results

Here we show by numerical simulation of an enzyme-catalyzed reaction chain that specialist enzymes spread after the invention of the chromosome because protocells harbouring unlinked genes maintain largely non-specific enzymes to reduce their assortment load. When genes are linked on chromosomes, high enzyme specificity evolves because it increases biomass production, also by reducing taxation by side reactions.

Conclusion

The constitution of the genetic system has a profound influence on the limits of metabolic efficiency. The major evolutionary transition to chromosomes is thus proven to be a prerequisite for a complex metabolism. Furthermore, the appearance of specific enzymes opens the door for the evolution of their regulation.

Reviewers

This article was reviewed by Sándor Pongor, Gáspár Jékely, and Rob Knight.

The evolution of information storage and heredity

Many important transitions in evolution are associated with novel ways of storing and transmitting information. The storage of information in DNA sequence, and its transmission through DNA replication, is a fundamental hereditary system in all extant organisms, but it is not the only way of storing and transmitting information, and has itself replaced, and evolved from, other systems. A system that transmits information can have limited heredity or indefinite heredity. With limited heredity, the number of different possible types is commensurate with, or below, that of the individuals. With indefinite heredity, the number of possible types greatly exceeds the number of individuals in any realistic system. Recent findings suggest that the emergence and subsequent evolution of very different hereditary systems, from autocatalytic chemical cycles to natural language, accompanied the major evolutionary transitions in the history of life.

Selectionist and evolutionary approaches to brain function: a critical appraisal

We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman’s theory of neuronal group selection, Changeux’s theory of synaptic selection and selective stabilization of pre-representations, Seung’s Darwinian synapse, Loewenstein’s synaptic melioration, Adam’s selfish synapse, and Calvin’s replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price’s covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Examples are given of cases where parallel competitive search with information transfer among the units is more efficient than search without information transfer between units. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise.