Evolution experiments with an artificial ecosystem

Synthetic model ecosystem of 12 cryopreservable microbial species allowing for a noninvasive approach

Simultaneous understanding of both population and ecosystem dynamics is crucial in an era marked by the degradation of ecosystem services. Experimental ecosystems are a powerful tool for understanding these dynamics; however, they often face technical challenges, typically falling into two categories: "complex but with limited replicability microcosms" and "highly replicable but overly simplistic microcosms." Herein, we present a high-throughput synthetic microcosm system comprising 12 functionally and phylogenetically diverse microbial species. These species are axenically culturable, cryopreservable, and can be measured noninvasively via microscopy, aided by machine learning. This system includes prokaryotic and eukaryotic producers and decomposers, and eukaryotic consumers to ensure functional redundancy. Our model system exhibited key features of a complex ecosystem: (i) various positive and negative interspecific interactions, (ii) higher-order interactions beyond two-species dynamics, (iii) probabilistic dynamics leading to divergent outcomes, and (iv) stable nonlinear transitions. We identified several conditions under which at least one species from each of the three functional groups-producers, consumers, and decomposers-and one functionally redundant species, persisted for over six months. These conditions set the stage for detailed investigations in the future. Given its designability and experimental replicability, our model ecosystem offers a promising platform for deeper insights integrating both population and ecosystem dynamics.

Symbiosis in Digital Evolution: Past, Present, and Future

Symbiosis, the living together of unlike organisms as symbionts, is ubiquitous in the natural world. Symbioses occur within and across all scales of life, from microbial to macro-faunal systems. Further, the interactions between symbionts are multimodal in both strength and type, can span from parasitic to mutualistic within one partnership, and persist over generations. Studying the ecological and evolutionary dynamics of symbiosis in natural or laboratory systems poses a wide range of challenges, including the long time scales at which symbioses evolve de novo, the limited capacity to experimentally control symbiotic interactions, the weak resolution at which we can quantify interactions, and the idiosyncrasies of current model systems. These issues are especially challenging when seeking to understand the ecological effects and evolutionary pressures on and of a symbiosis, such as how a symbiosis may shift between parasitic and mutualistic modes and how that shift impacts the dynamics of the partner population. In digital evolution, populations of computational organisms compete, mutate, and evolve in a virtual environment. Digital evolution features perfect data tracking and allows for experimental manipulations that are impractical or impossible in natural systems. Furthermore, modern computational power allows experimenters to observe thousands of generations of evolution in minutes (as opposed to several months or years), which greatly expands the range of possible studies. As such, digital evolution is poised to become a keystone technique in our methodological repertoire for studying the ecological and evolutionary dynamics of symbioses. Here, we review how digital evolution has been used to study symbiosis, and we propose a series of open questions that digital evolution is well-positioned to answer.

Evolutionary Innovations and Where to Find Them: Routes to Open-Ended Evolution in Natural and Artificial Systems

This article presents a high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems. Drawing upon earlier work by Banzhaf et al. (2016), three different kinds of open-endedness are identified: exploratory, expansive, and transformational. These are characterized in terms of their relationship to the search space of phenotypic behaviors. A formalism is introduced to describe three key processes required for an evolutionary process: the generation of a phenotype from a genetic description, the evaluation of that phenotype, and the reproduction with variation of individuals according to their evaluation. The formalism makes explicit various influences in each of these processes that can easily be overlooked. The distinction is made between intrinsic and extrinsic implementations of these processes. A discussion then investigates how various interactions between these processes, and their modes of implementation, can lead to open-endedness. However, an important contribution of the article is the demonstration that these considerations relate to exploratory open-endedness only. Conditions for the implementation of the more interesting kinds of open-endedness-expansive and transformational-are also discussed, emphasizing factors such as multiple domains of behavior, transdomain bridges, and non-additive compositional systems. In contrast to a traditional Darwinian analysis, these factors relate not to the generic evolutionary properties of individuals and populations, but rather to the nature of the building blocks out of which individual organisms are constructed, and the laws and properties of the environment in which they exist. The article ends with suggestions of how the framework can be used to categorize and compare the open-ended evolutionary potential of different systems, how it might guide the design of systems with greater capacity for open-ended evolution, and how it might be further improved.

How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles

We propose an approach to open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three important factors to create novelty and diversity: (a) communication generates combinatorial cooperative dynamics, (b) concurrency allows for separation of time scales, and (c) complexity and size increases push the system towards transitions in innovation. We illustrate these three components in a model computing the continuous evolution of a swarm of agents. The results, divided into three distinct applications, show how emergent structures are capable of filtering information through the bottleneck of their memory, to produce meaningful novelty and diversity within their simulated environment.

Population-reaction model and microbial experimental ecosystems for understanding hierarchical dynamics of ecosystems

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population-reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population-reaction model. We also show that population-reaction models can be applied to various ecological concepts, such as predator-prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms.

Evolving an ecology of mathematical expressions with grammatical evolution

This paper describes the use of grammatical evolution to obtain an ecology of artificial beings associated with mathematical functions, whose fitness is also defined mathematically. The system allows "parasite" species and "parasites of parasites" to develop, and supports the simultaneous evolution of several ecological niches. The use of standard measurements makes it possible to explore the influence of the number of niches or the presence of parasites on "biological" diversity and similar functions. Our results suggest that some of the features of biological evolution depend more on the genetic substrate and natural selection than on the actual phenotypic expression of that substrate.

The evolution of trophic structure

The trophic relationships of an ecological community were represented by digital individuals consuming resources or prey within a simulated ecosystem and producing offspring that may differ from their parents. When individuals meet, a few simple rules are used to decide the outcome of their interaction. Trophically complex systems persist for long periods of time even in finite communities, provided that the strength of predator-prey interaction is sufficient to repay the cost of maintenance. The topology of the food web and important system-level attributes such as overall productivity follow from the rules of engagement: that is, the macroscopic properties of the ecosystem follow from the microscopic attributes of individuals, without the need to invoke the emergence of novel processes at the level of the whole system. Evolutionarily stable webs exist only when the pool of available species is small. If the pool is large, or speciation is allowed, species composition changes continually, while overall community properties are maintained. Ecologically separate and topologically different source webs based on the same pool of resources usually coexist for long periods of time, through negative frequency-dependent selection at the level of the source web as a whole. Thus, the evolved food web of species-rich communities is a highly dynamic structure with continual species turnover. It both imposes selection on each species and itself responds to selection, but selection does not necessarily maximize stability, productivity or any other community property.

The Paton individual-based model legacy

Ray Paton oversaw the creation of a long lineage of Individual-based Models (IbMs) and this paper discusses the five most successful. All of these concern the development of adaptation, covering both evolutionary time and organism lifetime (somatic time). Of the five models discussed here, the first is based on a plant-herbivore model, the other four are based on a substrate-bacteria model, with the option of antibiotics.

A rule-based approach to the modelling of bacterial ecosystems

This paper presents an approach to ecological/evolutionary modelling that is inspired by natural bacterial ecosystems and bacterial evolution. An individual-based artificial ecosystem model is proposed, which is designed to explore the evolvability of adaptive behavioural strategies in artificial bacteria represented by rule-based learning classifier systems. The proposed ecosystem model consists of a n-dimensional environmental grid, which can contain different types of artificial resources in arbitrary arrangements. The resources provide the energy that is necessary for the organisms to sustain life, and can trigger different types of behaviour in the organisms, such as movement towards nutrients and away from toxic substances, growth, and the controlled release of signalling resources. The balance between energy and material is modelled carefully to ensure that the ecosystem is dissipative. Those organisms that are able to efficiently exploit the available resources gradually accumulate enough energy to reproduce (by binary fission) and generate copies of themselves in the environment. Organisms are also able to produce their own resources, which can potentially be used as markers to send signals to other organisms (a behaviour known as quorum sensing). The complex relationships between stimuli and actions in the organisms are stochastically altered by means of mutations, thus enabling the organisms to adapt to their environment and maximise their lifespan and reproductive success. In this paper, the proposed bacterial ecosystem model is defined formally and its structure is discussed in detail. This is followed by results from simulation experiments that illustrate the model's operation and how it can be used in evolutionary modelling/computing scenarios.

Toward a theory of evolutionary computation

We outline a theory of evolutionary computation using a formal model of evolutionary computation--the Evolutionary Turing Machine--which is introduced as the extension of the Turing Machine model. Evolutionary Turing Machines provide a better and a more complete model for evolutionary computing than conventional Turing Machines, algorithms, and Markov chains. The convergence and convergence rate are defined and investigated in terms of this new model. The sufficient conditions needed for the completeness and optimality of evolutionary search are investigated. In particular, the notion of the total optimality as an instance of the multiobjective optimization of the Universal Evolutionary Turing Machine is introduced. This provides an automatic way to deal with the intractability of evolutionary search by optimizing the quality of solutions and search costs simultaneously. Based on a new model a very flexible classification of optimization problem hardness for the evolutionary techniques is proposed. The expressiveness of evolutionary computation is investigated. We show that the problem of the best evolutionary algorithm is undecidable independently of whether the fitness function is time dependent or fixed. It is demonstrated that the evolutionary computation paradigm is more expressive than Turing Machines, and thus the conventional computer science based on them. We show that an Evolutionary Turing Machine is able to solve nonalgorithmically the halting problem of the Universal Turing Machine and, asymptotically, the best evolutionary algorithm problem. In other words, the best evolutionary algorithm does not exist, but it can be potentially indefinitely approximated using evolutionary techniques.

Mutualism promotes diversity and stability in a simple artificial ecosystem

This work investigates the effect of ecological interactions between organisms on the evolutionary dynamics of a community. A spatially explicit, individual-based model is presented, in which organisms compete for space and resources. We investigated how introducing the potential for mutualistic relationships (where the presence of one type of organism stimulates the growth of another type, and vice versa) affected the evolutionary dynamics of the system. Without this potential, one or a small number of individual types of organisms dominated the simulated community from the onset. When mutualistic relationships were allowed, many persisting types arose, with new types appearing continually. Furthermore, we investigated how the stability of the community differed when mutualistic relationships were allowed and disallowed. Our results suggest that the existence of mutualistic relationships improved community stability.

The physics of symbols: bridging the epistemic cut

Evolution requires the genotype-phenotype distinction, a primeval epistemic cut that separates energy-degenerate, rate-independent genetic symbols from the rate-dependent dynamics of construction that they control. This symbol-matter or subject-object distinction occurs at all higher levels where symbols are related to a referent by an arbitrary code. The converse of control is measurement in which a rate-dependent dynamical state is coded into quiescent symbols. Non-integrable constraints are one necessary condition for bridging the epistemic cut by measurement, control, and coding. Additional properties of heteropolymer constraints are necessary for biological evolution.

Howard Pattee's theoretical biology--a radical epistemological stance to approach life, evolution and complexity

This paper offers a short review of Pattee's main contributions to science and philosophy. With no intention of being exhaustive, an account of Pattee's work is presented which discusses some of his ideas and their reception. This is done through an analysis centered in what is thought to be his main contribution: the elaboration of an internal epistemic stance to better understand life, evolution and complexity. Having introduced this core idea as a sort of a posteriori cohesive element of a complex but highly coherent and complete system of thinking, further specific elements are also reviewed.

Artificial chemistries--a review

This article reviews the growing body of scientific work in artificial chemistry. First, common motivations and fundamental concepts are introduced. Second, current research activities are discussed along three application dimensions: modeling, information processing, and optimization. Finally, common phenomena among the different systems are summarized. It is argued here that artificial chemistries are "the right stuff" for the study of prebiotic and biochemical evolution, and they provide a productive framework for questions regarding the origin and evolution of organizations in general. Furthermore, artificial chemistries have a broad application range of practical problems, as shown in this review.

Learning synergy in a multilevel neuronal architecture

An artificial worlds model of the brain has been developed that integrates memory, intraneuronal dynamics and multilevel evolutionary learning. The model includes two major subsystems. The first is a memory-manipulation scheme, called the reference neuron system, that serves to orchestrate a repertoire of neurons with different input-output capabilities. Signals impinging on these neurons are integrated by a cytoskeletal structure that is simulated as a cellular automation. The second subsystem is an evolutionary learning scheme, called the selection circuits system, that serves to train the neurons in the repertoire by varying the cytoskeletal proteins that control signal flow or readouts. The integrated system comprises two layers of cytoskeletally controlled neurons and two layers of reference neurons. Evolution can occur at the level of readout enzymes in neurons, at the level of proteins that control the flow of signals in the cytoskeleton and at the level of reference neurons that orchestrate the repertoire. The integrated system controls the motion of a modeled organism that is embedded in an artificial environment consisting of barriers, food and a target. The organism effectively learns to use patterns of barriers in its local environment to find the target, using food as a reward. Experiments with the model show that the integrated system enjoys significant computational synergies that make it more powerful than the component systems standing alone, that interactions between different levels at which variation can occur exert significant control over the tempo of evolution, that the synergies between different components and levels becomes more important as the environment becomes more complex and that mutation strategies that significantly slow down the rate of learning significantly decrease the degrading effects of environmental noise on performance.

The artificial worlds approach to emergent evolution

Artificial worlds models of evolutionary systems are computer models that map the essential logical structure of ecological systems, defined as self-sustaining biological organizations. The artificial world comprises an artificial environment, with mass components, energy input, and physical states. It also comprises artificial organisms, including a genome, a phenome, and a (developmental) map that connects the genome to the phenome. Mass components are cycled and space is limited. The evolution process results, as in nature, from genetic variation combined with natural selection imposed by the finiteness of the environment. The selection criteria (fitness values) are not imposed, but rather emerge from the interactions of the organisms with each other and with the environment. The dynamics at the population level also emerges from these basic interactions. In this paper we describe the comparative properties of the EVOLVE family of artificial worlds models.

Simulated evolution and artificial selection

A highly simplified evolving system was investigated by computer simulation. The genetic complement of each simulated organism in the population was represented by a single chromosome that consisted of a string of symbols. Individual fitness was measured as the number of symbols that corresponded to a specified rule. Reproduction was simulated with a non-breeding algorithm and two variants of a breeding algorithm, and was subject to random point mutations. In each generation, selection was effected by replacing the less fit members of the population with offspring of the more fit. The size of the population and the fraction replaced, though under experimental control, were constant for each simulation run. It was found that even such a simplified system is able to mimic a variety of properties observed in natural systems. In addition, the effect of the simulation parameters on the course of fitness increase provides a basis for using a genetic algorithm as an optimization technique.

Evolve II: a computer model of an evolving ecosystem

The Evolve II program is a model of an ecosystem in which organisms are allowed to evolve. Organisms are subject to a changeable environment and competition from other organisms for a limited food supply. The gene structure may change through mutation. A feature of Evolve II is that the magnitude of phenotypic change resulting from mutation is itself a property of the gene. The system was studied under a number of environmental variation schemes. We report three significant findings. Two species (lineages with distinctly different survival strategies) evolved and coexisted in the same environmental conditions. Organisms developed a resistance to phenotypic change in response to mutation in slowly varying environments. However, traits which favor survival of the individual at the expense of reproduction could in some cases undergo phenotypic change in response to mutation despite the fact that this did not favor the survival of the offspring. This demonstrates that gene structures can evolve which are advantageous from the standpoint of the lineage, but not advantageous from the standpoint of individual offspring.

Evolve III: a discrete events model of an evolutionary ecosystem

Evolve III is a discrete events model of an evolutionary ecosystem. The model includes three levels of organization: population, organism and genetic structure. Each of these components was modeled independently, so that selective replacement of subsystems can be used to create families of models capable of testing alternative hypotheses about the real system. To demonstrate the use of the model we describe an experiment on the relationship between adaptability of populations and the variability of the environment. Populations cultured in a constant environment usually dominated those cultured in a variable environment when both were placed in a variable environment at an early stage of development, whereas the opposite is the case at later stages of development. This agrees with experiments on laboratory microcosms and lends credence to the potential predictive value of the model.

Algorithmic specification as a technique for computing with informal biological models

Conceptual biological models can sometimes be usefully expressed in algorithmic form. Models expressed in this way are often capable of capturing more aspects of the complete system than could be captured by other modeling approaches, though in general each aspect is captured in a highly simplified way. Two examples are given. The first involves algorithmic specification of the theory of evolution. The second involves a recently implemented computational model of the brain. Work with such models has, to some extent, the style of experimental work. It often suggests interesting problems or exposes subtle features of the system whose importance has been overlooked.

An automaton analogue of unicellularity

There is presented in outline form an abstract model of a cell in an evolutionary context. The design is based on an elaboration of John Holland's one-dimensional, abstract universe recently posed for the study of the emergence of self-replicating systems. Eight new ingredients constitute the elaboration. They suggest how compartmentation of a set of "molecules" in a one-dimensional universe can be achieved and how a suitable, compartmentalized set of molecules can replicate in a manner analogous to real cell replication, i.e., there is segregation and semi-conservative replication of the genetic material, and there is division of the compartment through the construction of an "inner membrane". The approach to self-replication of a spatially delimited entity exemplified by this design differs from the abstract models of replication of the von Neumann or Laing type. In these the replicating entity constructs a copy external to itself and does not undergo any essential replacement of any of its parts. Also, while in these models the concern is primarily with questions of the "logical" type, our design has in mind features identifiable with both energy and information considerations. Thus, the rules which define the underlying "physics and chemistry" imply that the self-replicating entity is a dissipative structure. A constant flux of energy is required to maintain the system far from thermodynamic equilibrium in order to account for multiple steady states, and hence dynamic structure.

The evolution of sexual reproduction as a repair mechanism. Part I. A model for self-repair and its biological implications

The theory is presented that the sexual process is a repair mechanism which maintains redundancy within the sub-structure of hierarchical, self-reproducing organisms. In order to keep the problems within mathematically tractable limits (see Part II), a simple model is introduced: a wheel with 6 spokes, 3 of them vital and 3 redundant, symbolizes the individual (cell or organism). Random accidents destroy spokes; the wheels replicate at regular cycles and engage periodically in pairing and repair phases during which missing spokes are copy-reproduced along the intact spokes of the partner wheel. The hierarchical structure of such a system is analysed and an 'autonomous unit' is defined: this is the unit of minimal hierarchical complexity which is capable of perpetuating autonomously all higher and all lower levels of the hierarchy; this is the central unit of selection. Four basic, physical parameters are isolated which determine the essential features of any eucaryotic life cycle: 1. The number of levels of the hierarchy (unicellular, multicellular, colonial, etc.); 2. the relation between the phases of replication (asexual generations) and repair (sexual generations); 3. the duration of potential repair (haplo-diplo-phase); 4. the position of the sexual partners within the hierarchy (selfing, monecy, dioecy, reproductive individuals within colonies, etc.). The evaluation of fitness components is considered in relation to trends of reproductive patterns in evolution.