Showing papers in "Artificial Life in 2004"
TL;DR: This work explains the general principles on which Avida is built, as well as its main components and their interactions, and how experiments are set up, carried out, and analyzed.
Abstract: Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over experimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed.
415 citations
TL;DR: A mathematical model of a 3D tesselation automaton, considered as a minimal example of autopoiesis, is presented, and a thesis T1 is proposed: An autopoietic system can be described as a random dynamical system, which is defined only within its organized autopOietic domain.
Abstract: This article revisits the concept of autopoiesis and examines its relation to cognition and life. We present a mathematical model of a 3D tesselation automaton, considered as a minimal example of autopoiesis. This leads us to a thesis T1: "An autopoietic system can be described as a random dynamical system, which is defined only within its organized autopoietic domain." We propose a modified definition of autopoiesis: "An autopoietic system is a network of processes that produces the components that reproduce the network, and that also regulates the boundary conditions necessary for its ongoing existence as a network." We also propose a definition of cognition: "A system is cognitive if and only if sensory inputs serve to trigger actions in a specific way, so as to satisfy a viability constraint." It follows from these definitions that the concepts of autopoiesis and cognition, although deeply related in their connection with the regulation of the boundary conditions of the system, are not immediately identical: a system can be autopoietic without being cognitive, and cognitive without being autopoietic. Finally, we propose a thesis T2: "A system that is both autopoietic and cognitive is a living system."
398 citations
TL;DR: This account gives a bottom-up account of the origins of self-production (or self-construction, as it is proposed to call it), pointing out which are the minimal material and energetic requirements for the constitution of basic autonomous systems.
Abstract: In the search for the primary roots of autonomy (a pivotal concept in Varela's comprehensive understanding of living beings), the theory of autopoiesis provided an explicit criterion to define minimal life in universal terms, and was taken as a guideline in the research program for the artificial synthesis of biological systems. Acknowledging the invaluable contribution of the autopoietic school to present biological thinking, we offer an alternative way of conceiving the most basic forms of autonomy. We give a bottom-up account of the origins of "self-production" (or self-construction, as we propose to call it), pointing out which are the minimal material and energetic requirements for the constitution of basic autonomous systems. This account is, indeed, committed to the project of developing a general theory of biology, but well grounded in the universal laws of physics and chemistry. We consider that the autopoietic theory was formulated in highly abstract terms and, in order to advance in the implementation of minimal autonomous systems (and, at the same time, make major progress in exploring the origins of life), a more specific characterization of minimal autonomous systems is required. Such a characterization will not be drawn from a review of the autopoietic criteria and terminology (a la Fleischaker) but demands a whole reformulation of the question: a proper naturalization of the concept of autonomy. Finally, we also discuss why basic autonomy, according to our account, is necessary but not sufficient for life, in contrast with Varela's idea that autopoiesis was a necessary and sufficient condition for it.
188 citations
TL;DR: The relationship between an autopoietic perspective on cognition and recent work on dynamical approaches to the behavior and cognition of situated, embodied agents is examined.
Abstract: Maturana and Varela's notion of autopoiesis has the potential to transform the conceptual foundation of biology as well as the cognitive, behavioral, and brain sciences. In order to fully realize this potential, however, the concept of autopoiesis and its many consequences require significant further theoretical and empirical development. A crucial step in this direction is the formulation and analysis of models of autopoietic systems. This article sketches the beginnings of such a project by examining a glider from Conway's game of life in autopoietic terms. Such analyses can clarify some of the key ideas underlying autopoiesis and draw attention to some of the central open issues. This article also examines the relationship between an autopoietic perspective on cognition and recent work on dynamical approaches to the behavior and cognition of situated, embodied agents.
122 citations
TL;DR: It is argued that computational autopoiesis continues to provide an effective framework for addressing key open problems in artificial life.
Abstract: Computational autopoiesis—the realization of autopoietic entities in computational media—holds an important and distinctive role within the field of artificial life. Its earliest formulation by Francisco Varela, Humberto Maturana, and Ricardo Uribe was seminal in demonstrating the use of an artificial, computational medium to explore the most basic question of the abstract nature of living systems—over a decade in advance of the first Santa Fe Workshop on Artificial Life. The research program it originated has generated substantive demonstrations of progressively richer, lifelike phenomena. It has also sharply illuminated both conceptual and methodological problems in the field. This article provides an integrative overview of the sometimes disparate work in this area, and argues that computational autopoiesis continues to provide an effective framework for addressing key open problems in artificial life.
100 citations
TL;DR: The matrix effect is re-elaborated in the perspective of the origin of life, and in particular in terms of the prebiotic mechanisms that might permit the growth and reproduction of vesicles.
Abstract: Spherical bounded structures such as those formed by surfactant aggregates (mostly micelles and vesicles), with an inside that is chemically and physically different from the outside medium, can be seen as primitive cell models. As such, they are fundamental structures for the theory of autopoiesis as originally formulated by Varela and Maturana. In particular, since self-reproduction is a very important feature of minimal cellular life, the study of self-reproduction of micelles and vesicles represents a quite challenging bio-mimetic approach. Our laboratory has put much effort in recent years into implementing self-reproduction of vesicles as models for self-reproduction of cellular bounded structures, and this article is a further contribution in this direction. In particular, we deal with the so-called matrix effect of vesicles, related to the fact that when fresh surfactant is added to an aqueous solution containing preformed vesicles of a very narrow size distribution, the newly formed vesicles (instead of being polydisperse, as is usually the case) have dimensions very close to those of the preformed ones. In practice, this corresponds to a mechanism of reproduction of vesicles of the same size. In this article, the matrix effect is re-elaborated in the perspective of the origin of life, and in particular in terms of the prebiotic mechanisms that might permit the growth and reproduction of vesicles. The data are analyzed by dynamic light scattering with a new program that permits the calculation of the number-weighted size distribution. It is shown that, on adding a stoichiometric amount of oleate micelles to preformed oleate vesicles extruded at 50 and 100 nm, the final distribution contains about twice the initial number of particles, centered around 50 and 100 nm. The same holds when oleate is added to preformed phospholipid liposomes. By contrast, when the same amount of oleate is added to an aqueous solution (as a control experiment), a very broad distribution ranging between 20 and 1000 nm is obtained. The data can then be seen as a kind of reproduction of the same size vesicles, and the argument is advanced that this may correspond to a simple prebiotic mechanism of vesicle multiplication in prebiotic times, when only physical forces might be responsible for the basic mechanisms of early protocell growth and division. Preliminary data also show that repeated addition of oleate maintains the same basic initial features, and that surfactants other than oleate also respect the reproductive mode of the matrix effect.
74 citations
TL;DR: Turn-taking behavior is simulated in a coupled-agents system as mentioned in this paper, where each agent is modeled as a mobile robot with two wheels and a recurrent neural network is used to produce the motor outputs and hold the internal dynamics.
Abstract: Turn-taking behavior is simulated in a coupled-agents system. Each agent is modeled as a mobile robot with two wheels. A recurrent neural network is used to produce the motor outputs and to hold the internal dynamics. Agents are developed to take turns on a two-dimensional arena by causing the network structures to evolve. Turn taking is established using either regular or chaotic behavior of the agents. It is found that chaotic turn takers are more sensitive in response to inputs from the other agent. Conversely, regular turn takers are comparatively robust against noisy inputs, owing to their restricted dynamics. From many observations, including turn taking with virtual agents, we claim that there is a complementary relationship between robustness and adaptability. Furthermore, by investigating the recoupling of agents from different GA generations, we report the emergence of a new turn-taking behavior. Potential for synthesizing a new form of interaction is another characteristic of chaotic turn takers.
41 citations
TL;DR: RGGs' ability to represent genes, regulatory networks of metabolites, and morphologically structured organisms, as well as developmental aspects of these entities, in a common formal framework is demonstrated.
Abstract: We present the high-level language of relational growth grammars (RGGs) as a formalism designed for the specification of ALife models. RGGs can be seen as an extension of the well-known parametric Lindenmayer systems and contain rule-based, procedural, and object-oriented features. They are defined as rewriting systems operating on graphs with the edges coming from a set of user-defined relations, whereas the nodes can be associated with objects. We demonstrate their ability to represent genes, regulatory networks of metabolites, and morphologically structured organisms, as well as developmental aspects of these entities, in a common formal framework. Mutation, crossing over, selection, anti the dynamics of a network of gene regulation can all be represented with simple graph rewriting rules. This is demonstrated in some detail on the classical example of Dawkins' biomorphs and the ABC model of flower morphogenesis: other applications are briefly sketched. An interactive program was implemented, enabling the execution of the formalism and the visualization of the results.
38 citations
TL;DR: Researchers study the evolution of robustness in digital organisms adapting to a high mutation rate and find that increased robustness is achieved by moving from antagonistic epistasis between mutations towards codes where mutations are, on average, independent.
Abstract: We study the evolution of robustness in digital organisms adapting to a high mutation rate. As genomes adjust to the harsh mutational environment, the mean effect of single mutations decreases, up until the point where a sizable fraction (up to 30% in many cases) of the mutations are neutral. We correlate the changes in robustness along the line of descent to changes in directional epistasis, and find that increased robustness is achieved by moving from antagonistic epistasis between mutations towards codes where mutations are, on average, independent. We interpret this recoding as a breakup of linkage between vital sections of the genome, up to the point where instructions are maximally independent of each other. While such a recoding often requires sacrificing some replication speed, it is the best strategy for withstanding high rates of mutation.
38 citations
TL;DR: This work shows that the reflexive agents of contemporary particle systems can readily be extended to support goal-directed problem solving while retaining their collective movement behaviors.
Abstract: Self-organizing particle systems consist of numerous autonomous, purely reflexive agents ("particles") whose collective movements through space are determined primarily by local influences they exert upon one another. Inspired by biological phenomena (bird flocking, fish schooling, etc.), particle systems have been used not only for biological modeling, but also increasingly for applications requiring the simulation of collective movements such as computer-generated animation. In this research, we take some first steps in extending particle systems so that they not only move collectively, but also solve simple problems. This is done by giving the individual particles (agents) a rudimentary intelligence in the form of a very limited memory and a top-down, goal-directed control mechanism that, triggered by appropriate conditions, switches them between different behavioral states and thus different movement dynamics. Such enhanced particle systems are shown to be able to function effectively in performing simulated search-and-collect tasks. Further, computational experiments show that collectively moving agent teams are more effective than similar but independently moving ones in carrying out such tasks, and that agent teams of either type that split off members of the collective to protect previously acquired resources are most effective. This work shows that the reflexive agents of contemporary particle systems can readily be extended to support goal-directed problem solving while retaining their collective movement behaviors. These results may prove useful not only for future modeling of animal behavior, but also in computer animation, coordinated movement control in robotic teams, particle swarm optimization, and computer games.
35 citations
TL;DR: It can be predicted that recurrence is more likely when a small, wild-type-based starting pool is used with efficient selection and search strategies involving little online mutagenesis within a rugged adaptive landscape with a strong local optimum.
Abstract: Recurrence is the possibility of resulting in the same endpoint multiple times when a living system is allowed to evolve repeatedly starting from a given initial point. This concept is of concern to both evolutionary theoreticians and molecular biologists who use nucleic acid selection techniques to mimic biotic and computorial processes in the test tube. Using the continuous in vitro evolution methodology, many replicate experimental evolutionary lineages with populations of catalytic RNA were performed to gain insight into the parameters that could affect recurrence. The likelihood that the same genotype will result in parallel trials of an evolution experiment in vitro depends on several factors, including the phenotype under selection, the size and composition of the initial diverse pool of nucleic acids used in the experiment, the degree of mutation possible during the experiment, the shape of the fitness landscape through which the population evolves, and the strategies used to invoke selection and to search the landscape, among others. By considering these factors, it can be predicted that recurrence is more likely when a small, wild-type-based starting pool is used with efficient selection and search strategies involving little online mutagenesis within a rugged adaptive landscape with a strong local optimum. The recurrence experiments performed here on the 150-nucleotide ligase ribozyme demonstrate that it repeatedly jumps from one peak in a fitness landscape to another, apparently hurdling a deep fitness valley. These predictions can and should be tested by additional multiple replicates of actual evolution experiments in the laboratory.
TL;DR: The autopoietic criticisms of genetic information, reproduction, and evolution are reviewed in the light of a biology that can solve the problem of living organization.
Abstract: The contribution of the theory of autopoiesis to the definition of life and biological theory affirms biological autonomy as a central notion of scientific and philosophical inquiry, and opposes other biological approaches, based on the notion of genetic information, that consider reproduction and evolution to be the central aspects of life and living phenomenology. This article reviews the autopoietic criticisms of genetic information, reproduction, and evolution in the light of a biology that can solve the problem of living organization.
TL;DR: It is shown that clones adapted to a specific environment can adapt to new environments quickly and efficiently, although their history remains a significant factor in their fitness.
Abstract: We evolved multiple clones of populations of digital organisms to study the effects of chance, history, and adaptation in evolution. We show that clones adapted to a specific environment can adapt to new environments quickly and efficiently, although their history remains a significant factor in their fitness. Adaptation is most significant (and the effects of history less so) if the old and new environments are dissimilar. For more similar environments, adaptation is slower while history is more prominent. For both similar and dissimilar transfer environments, populations quickly lose the ability to perform computations (the analogue of beneficial chemical reactions) that are no longer rewarded in the new environment. Populations that developed few computational "genes" in their original environment were unable to acquire them in the new environment.
TL;DR: This work studies the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) in an environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles.
Abstract: Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.
TL;DR: It is shown that in immunology, the concepts of autopoiesis can be employed to generate clear novel hypotheses, models demonstrating these ideas, testable predictions, and novel therapeutic procedures.
Abstract: The fundamental concepts of autopoiesis, which emphasize the circular organization underlying both living organisms and cognition, have been criticized on the grounds that since they are conceived as a tight logical chain of definitions and implications, it is often not clear whether they are indeed a scientific theory or rather just a potential scientific vocabulary of doubtful utility to working scientists. This article presents the deployment of the concepts of autopoiesis in the field of immunology, a discipline where working biologists themselves spontaneously have long had recourse to "cognitive" metaphors: "recognition"; a "repertoire" of recognized molecular shapes; "learning" and "memory"; and, most striking of all, a "self versus non-self" distinction. It is shown that in immunology, the concepts of autopoiesis can be employed to generate clear novel hypotheses, models demonstrating these ideas, testable predictions, and novel therapeutic procedures. Epistemologically, it is shown that the self-non-self distinction, while quite real, is misleadingly named. When a real mechanism for generating this distinction is identified, it appears that the actual operational distinction is between (a) a sufficiently numerous set of initial antigens, present from the start of ontogeny, in conditions that allow for their participation in the construction of the system's organization and operation, and (b) single antigens that are first presented to the system after two successive phases of maturation. To call this a self-non-self distinction obscures the issue by presupposing what it ought to be the job of scientific investigation to explain.
TL;DR: A novel CA where the cell handles data and signals is presented, designed as a digital system comprising a processing unit and a control unit that allows the realization of various growing structures, including self-replicating loops and biomorphs.
Abstract: In a traditional cellular automaton (CA) a cell is implemented by a rule table defining its state at the next time step, given its present state and those of its neighbors. The cell thus deals only with states. We present a novel CA where the cell handles data and signals. The cell is designed as a digital system comprising a processing unit and a control unit. This allows the realization of various growing structures, including self-replicating loops and biomorphs. We also describe the hardware implementation of these structures within our electronic wall for bio-inspired applications, the BioWall.
TL;DR: This research reveals several different effects of development on the Baldwin effect, some promoting and others inhibiting, and an evolved cooperation between direct blueprints and indirect developmental recipes in searching for unstructured and partially structured target patterns in large, needle-in-the-haystack fitness landscapes.
Abstract: Baldwin's classic hypothesis states that behavioral plasticity can speed evolution by (a) smoothing the fitness landscape and (b) indirect genetic assimilation of acquired characteristics. This latter phase demands a strong correlation between genotype and phenotype space. But the natural world shows signs of this correlation at only a very coarse level, since the intervening developmental process greatly complicates the mapping from genetics to physiology and ethology. Hence, development appears to preclude a strong Baldwin effect. However, by adding a simple developmental mechanism to Hinton and Nowlan's classic model of the Baldwin effect, and by allowing evolution to determine the proper balance between direct and indirect mapping of genome to phenotype, this research reveals several different effects of development on the Baldwin effect, some promoting and others inhibiting. Perhaps the most interesting result is an evolved cooperation between direct blueprints and indirect developmental recipes in searching for unstructured and partially structured target patterns in large, needle-in-the-haystack fitness landscapes.
TL;DR: The emergence and dynamics of competing strains of digital organisms in a world with two depletable resources and how populations evolve that seem to avoid the oscillations of intermediate to large amplitudes is studied.
Abstract: We study the emergence and dynamics of competing strains of digital organisms in a world with two depletable resources. Consumption of one resource produces the other resource as a by-product, and vice versa. As a consequence, two types of mutually dependent organisms emerge that each prey on the waste product of the other. In the absence of mutations, that is, in a purely ecological setting, the abundances of the two types of organisms display a wide range of different types of oscillations, from regular oscillations with large amplitude to irregular oscillations with amplitudes ranging from small to large. In this regime, time-averaged abundance levels seem to be controlled by the relative fitness of the organisms in the absence of resources. Under mutational pressure, on the other hand, populations evolve that seem to avoid the oscillations of intermediate to large amplitudes. In this case, the relative fitness of the organisms in the presence of resources plays an important role in the time-averaged abundance levels as well.
TL;DR: It is found that operant mechanisms allow the learning of IPD play against other strategies, and it is suggested thatoperant learning might be involved in reciprocal altruism.
Abstract: The prisoner's dilemma (PD) is the leading metaphor for the evolution of cooperative behavior in populations of selfish agents. Although cooperation in the iterated prisoner's dilemma (IPD) has been studied for over twenty years, most of this research has been focused on strategies that involve nonlearned behavior. Another approach is to suppose that players' selection of the preferred reply might he enforced in the same way as feeding animals track the best way to feed in changing nonstationary environments. Learning mechanisms such as operant conditioning enable animals to acquire relevant characteristics of their environment in order to get reinforcements and to avoid punishments. In this study, the role of operant conditioning in the learning of cooperation was evaluated in the PD. We found that operant mechanisms allow the learning of IPD play against other strategies. When random moves are allowed in the game, the operant learning model showed low sensitivity. On the basis of this evidence, it is suggested that operant learning might be involved in reciprocal altruism.
TL;DR: Three different mechanisms of self- protection are considered and implemented on a cellular-automaton-based evolutionary system, the evoloop, and simulation results imply a positive effect of those mechanisms on diversity maintenance, especially when the self-protection is moderate so that it conserves both the attacker and the attacked.
Abstract: The concept of self-protection, a capability of an organism to protect itself from exogenous attacks, is introduced into the design of artificial evolutionary systems as a possible method to create and maintain diversity in the population. Three different mechanisms of self-protection are considered and implemented on a cellular-automaton-based evolutionary system, the evoloop. Simulation results imply a positive effect of those mechanisms on diversity maintenance, especially when the self-protection is moderate so that it conserves both the attacker and the attacked. This letter briefly reports the models and the simulation results obtained using those models.
TL;DR: It is found that if the organisms undergo natural selection between branch points, the methods will be successful even on very large time scales, however, these algorithms often falter when selection is absent.
Abstract: Phylogenetic trees group organisms by their ancestral relationships. There are a number of distinct algorithms used to reconstruct these trees from molecular sequence data, but different methods sometimes give conflicting results. Since there are few precisely known phylogenies, simulations are typically used to test the quality of reconstruction algorithms. These simulations randomly evolve strings of symbols to produce a tree, and then the algorithms are run with the tree leaves as inputs. Here we use Avida to test two widely used reconstruction methods, which gives us the chance to observe the effect of natural selection on tree reconstruction. We find that if the organisms undergo natural selection between branch points, the methods will be successful even on very large time scales. However, these algorithms often falter when selection is absent.
TL;DR: Although work in digital evolution of the past decade has been reviewed by us recently, the versatility of the Avida software to conduct evolution experiments has never been displayed in the manner it is displayed in this special issue.
Abstract: Whether or not the field of artificial life has succeeded in doing what its name suggests, namely synthesizing life from non-living components, is a matter of contention. Clearly, this journal covers a broad range of topics related to the synthesis and simulation of living systems, but only a few articles go so far as to unabashedly study wholly artificial forms of life. The field of digital evolution is an exception: Artificial life forms, in the form of self-replicating computer code inhabiting specially prepared areas of a standard computer, have been used to learn about fundamental aspects of the evolutionary process since Tom Ray introduced us to them [23]. In this issue, we present experiments using digital organisms of the Avida variety (that is, implemented with the Avida software described in this issue [20]), but there are a number of other implementations of digital evolution that have been used for experimental evolution (see, e.g., [31, 32, 22, 5]). Whether or not these digitals are truly alive is ultimately of no concern to us as researchers: We use them because we are interested in complicated and vexing questions of evolutionary biology, and digitals offer us the possibility to attack them. Digital evolution is currently undergoing a boom phase, and public perception of this discipline is steadily increasing [21]. This boom can be traced back in part to a maturation of the Avida software used in the majority of digital evolution experiments, in part to a perceived need for rigor in evolution experiments [7], and in part to the adoption of digitals as experimental organisms alongside bacteria and viruses by a growing number of microbiologists (see, e.g., [16]). Although work in digital evolution of the past decade has been reviewed by us recently [29], the versatility of the Avida software to conduct evolution experiments has never been displayed in the manner we have the opportunity to do in this special issue. One of us (C.A.) has been teaching artificial life and evolution to advanced undergraduates and beginning graduate students at the California Institute of Technology since 1995 [1], and digital life has been a cornerstone of this class from the very beginning. The Avida software used in teaching this class, and developed expressly for research in evolutionary biology, was first written by C. Titus Brown and then by Charles Ofria in 1993 [19]. Since then, it has gone through many versions and revisions, with code contributions from a growing number of people, and a growing user base. But it is with the students taking CNS 175 (Artificial Life) and, since 2002, CNS 178 (Evolution and Biocomplexity) at Caltech that Avida has had its most lasting relationship. Each term, the students have to solve a number of problem sets, and at term’s end, instead of a final examination, the students are asked to turn in a final project that uses Avida to perform an experiment in evolution. But while for CNS 175 the students could choose one of three carefully selected projects, for CNS 178 we decided to take a different approach. We would not only let the students answer a question, we would let them pose it, too. So, when final project time approached, the students were asked via a
TL;DR: The response of populations of digital organisms that adapt to a time-varying fitness landscape of two oscillating peaks is studied, corroborating in general predictions from quasi-species theory in dynamic landscapes such as adaptation to the average fitness landscape at small periods and quasistatic adaptation at large periods.
Abstract: We study the response of populations of digital organisms that adapt to a time-varying (periodic) fitness landscape of two oscillating peaks. We corroborate in general predictions from quasi-species theory in dynamic landscapes, such as adaptation to the average fitness landscape at small periods (high frequency) and quasistatic adaptation at large periods (low frequency). We also observe adaptive phase shifts (time lags between a change in the fitness landscape and an adaptive change in the population) that indicate a low-pass filter effect, in agreement with existing theory. Finally, we witness long-term adaptation to fluctuating environments not anticipated in previous theoretical work.
TL;DR: This study confirms an earlier experimental effort using microorganisms, in that diversification can be understood at least in part in terms of bifurcations on saddle points leading to peak shifts, as in the picture drawn by Sewall Wright.
Abstract: One of the central questions in evolutionary biology concerns the dynamics of adaptation and diversification. This issue can be addressed experimentally if replicate populations adapting to identical environments can be investigated in detail. We have studied 501 such replicas using digital organisms adapting to at least two fundamentally different functional niches (survival strategies) present in the same environment: one in which fast replication is the way to live, and another where exploitation of the environment's complexity leads to complex organisms with longer life spans and smaller replication rates. While these two modes of survival are closely analogous to those expected to emerge in so-called r and K selection scenarios respectively, the bifurcation of evolutionary histories according to these functional niches occurs in identical environments, under identical selective pressures. We find that the branching occurs early, and leads to drastic phenotypic differences (in fitness, sequence length, and gestation time) that are permanent and irreversible. This study confirms an earlier experimental effort using microorganisms, in that diversification can be understood at least in part in terms of bifurcations on saddle points leading to peak shifts, as in the picture drawn by Sewall Wright.
TL;DR: It is shown here that folding plays a key role in enhancing the evolutionary stability of the intermolecular recognition necessary for the prevalent mode of catalytic action in replication, namely, in trans, one molecule catalyzing the replication of another copy, rather than itself.
Abstract: Sequence folding is known to determine the spatial structure and catalytic function of proteins and nucleic acids. We show here that folding also plays a key role in enhancing the evolutionary stability of the intermolecular recognition necessary for the prevalent mode of catalytic action in replication, namely, in trans, one molecule catalyzing the replication of another copy, rather than itself. This points to a novel aspect of why molecular life is structured as it is, in the context of life as it could be: folding allows limited, structurally localized recognition to be strongly sensitive to global sequence changes, facilitating the evolution of cooperative interactions. RNA secondary structure folding, for example is shown to be able to stabilize the evolution of prolonged functional sequences, using only a part of this length extension for intermolecular recognition, beyond the limits of the (cooperative) error threshold. Such folding could facilitate the evolution of polymerases in spatially heterogeneous systems. This facilitation is, in fact, vital because physical limitations prevent complete sequence-dependent discrimination for any significant-size biopolymer substrate. The influence of partial sequence recognition between biopolymer catalysts and complex substrates is investigated within a stochastic, spatially resolved evolutionary model of trans catalysis. We use an analytically tractable nonlinear master equation formulation called PRESS (McCaskill et al., Biol. Chem. 382: 1343-1363), which makes use of an extrapolation of the spatial dynamics down from infinite dimensional space, and compare the results with Monte Carlo simulations.
TL;DR: How fortunate it is that an exceptional and multifaceted scientist like Francisco Varela has not only provided a rich legacy of ideas that are worthy of serious and active (re-)discovery and exploration, but has also himself been a predecessor and supporter of the field.
Abstract: To say that artificial life is a young discipline in name only is to exaggerate, but it would be mistaken to think that its goals are new. The marriage of synthetic scientific aims with computational techniques makes artificial life a product of the last fifteen years, but its motivations have much deeper roots in cybernetics, theoretical biology, and the age-old drive to comprehend the mysteries of life and mind. Little wonder that a good part of the work in this field has been one of rediscovery and renewal of hard questions. Other disciplines have sidestepped such questions, often for very valid reasons, or have put them out of the focus of everyday research; yet these questions are particularly amenable to be treated with novel techniques such as computational modeling and other synthetic methodologies. What is an organism? What is cognition? Where do purposes come from? To rediscover and reinvent can be a pleasurable but difficult job. As historians of science know very well, concepts and methods evolve, disfavored theories get buried under successful ones (and not necessarily because they are any less valuable), metaphors and languages change, and social perception and pressures influence the directions of research. In view of this, how fortunate that an exceptional and multifaceted scientist like Francisco Varela has not only provided us with a rich legacy of ideas that, both in content and in perspective, are worthy of serious and active (re-)discovery and exploration, but has also himself been a predecessor and supporter of the field. Concrete examples of his work follow the methods of artificial life, both from when the label did not exist and from afterwards. We also have direct collaborators, many of whom are contributors to this special issue, who worry about many of the same problems as Varela did and whose work is directly connected to research lines in this field. Varela’s key scientific worry was the understanding of biological systems in their full autonomy—neither as a collection of inert components nor as something magical, but as introducing into the universe of physical interactions a special kind of novelty: an autonomously organized system with a formal identity and a point of view. This central worry led him in the 1970s to formulate, together with Humberto Maturana, the theory of autopoiesis, which radically alters the perspective on many biological phenomena by taking seriously (actually by founding itself on) the self-producing nature of bounded metabolic activity. The organism provides us with our primary biological unity, not only as an ontological foundation for biology but, more importantly, from an everyday pragmatic and scientific perspective. Dobzhansky’s famous motto could well be paraphrased as: “Nothing in biology makes sense except in the light of the organism.” The obviousness in the new version is apparent, for understanding what makes an organism remains our problem and is what separates biology from physics. Following the principle of biological autonomy has allowed Varela to formulate radically novel theoretical proposals for key unsolved problems such as the origin of life, the artificial synthesis of minimal cells, the somatic ecology of the extra-cellular
TL;DR: Examples of tracking of trajectories of evolutionary systems in the spaces of genotypes, strategies, and some global characteristics are provided to represent results of a series of simulations.
Abstract: This article proposes a method of visualizing and measuring evolution in artificial life simulations. The evolving population of agents is treated as a dynamical system. The proposed method is inspired by the notion of trajectory. The article provides examples of tracking of trajectories of evolutionary systems in the spaces of genotypes, strategies, and some global characteristics. Visualization similar to a bifurcation diagram is used to represent results of a series of simulations.
TL;DR: This essay examines a number of lifelike toys to discover the properties of real organisms that their designers have attempted to recreate, demonstrating the extent to which intuition may sway the authors' intellectual reasoning about real biology.
Abstract: Play things are often engineered to replicate the character of real organisms. In the past, inventors lavished great expense on their lifelike automata, their constraints being typically related to the mechanical technology they employed and the amount of time and effort they were able to commit to the enterprise. The devices that are currently produced are usually intended for the mass market. The cost of production therefore is a major concern, even though the technology is more sophisticated and highly-automated than in the past. Consequently, toymakers and engineers, as well as artists, of the past and present alike have had to think abstractly about living systems in order to construct their simulacra economically. This essay examines a number of lifelike toys to discover the properties of real organisms that their designers have attempted to recreate. That we, as users of these devices, so readily recognize in them a degree of lifelikeness demonstrates the extent to which intuition may sway our intellectual reasoning about real biology. As a result, an innovative toymaker or artist is able to manipulate us to zoomorphize even the most extreme abstractions--at least momentarily-despite our rational reluctance to accept the trickery.
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