Catálogo de publicaciones - libros

The dynamics of spatial reaction systems that consists of many molecular species can be difficult to understand. Here we introduce a method that allows to observe the dynamics of a diverse spatial reaction system at different spatial scales. Using chemical organization theory we define for a given spatial location its so called spatial organization, which is the organization generated by the molecular species present in the neighborhood of this location. The scale determines the size of that neighborhood. We show that at one scale, patterns become visible that can not be seen at a different scale. Furthermore, different scales tend to map to different parts of the lattice of organizations; at small scales spatial organizations tend to be small (lower part of the lattice of organizations) while at large scales spatial organizations tend to be large (upper part of the lattice of organizations). Finally we show how the right scale can be selected by comparing the spatial reactor with its well-stirred counterpart. The method is illustrated using an artificial chemistry.

Membranes form important structures in living systems. In this paper, we propose a new formulation of membrane dynamics as an extension to our artificial chemistry. It does not explicitly specify membranes to react; instead, the surface objects of membranes decide which membranes transform and how. We model the clathrin-coated vesicular transport by the formalism, and thereby show the compatibility of our approach with natural membrane systems.

We describe a simple artificial chemistry which abstracts a small number of key features from the origin of life “replicator world” hypotheses. We report how this can already give rise to moderately complex and counter-intuitive evolutionary phenomena, including macro-evolutionary deterioration in replication fidelity (which corresponds to intrinsic replicator fitness in this model). We briefly describe the extension of this model to incorporate a higher, protocell, level of selection. We show that the interaction between the two levels of selection then serves to control parasitic exploitation at the molecular level, while still significantly constraining accessible evolutionary trajectories at the protocell level. We conclude with a brief discussion of the implications for further work.

A recently developed and presented stochastic simulation platform (‘ENVIRONMENT’ [12, 25]), which extends Gillespie’s algorithm for chemically reacting, fixed-volume, homogeneous systems to volume-changing and globally heterogeneous conditions, is applied to investigate the dynamic behaviour of self-(re-)producing vesicles whose membrane consists of both lipids and small peptides. We claim that it is through the integration of these two types of relatively simple –and prebiotically plausible– components that protocells could start their development into functional supramolecular structures, allowing the formation of increasingly complex reaction networks in their internal aqueous milieu. The model is not spatially explicit, but takes into account quite realistically volume-surface constraints, osmotic pressure, diffusion/transport processes, structural elasticity ... In this framework the time evolution of non-equilibrium proto-metabolic cellular systems is studied, paying special attention to the capacity of the system to get rid of its waste material, which proved critical for balanced cell growth (avoiding the risk of an osmotic burst). We also investigate the effects of including an explicit feedback mechanism in the system: the case in which waste transport mediated by peptide chains takes place only under osmotic stress conditions.

This paper introduces a simple model for evolutionary dynamics approaching the “coding threshold”, where the capacity to symbolically represent nucleic acid sequences emerges in response to a change in environmental conditions. The model evolves a dynamical system, where a conglomerate of primitive cells is coupled with its potential encoding, subjected to specific environmental noise and inaccurate internal processing. The separation between the conglomerate and the encoding is shown to become beneficial in terms of preserving the information within the noisy environment. This selection pressure is captured information-theoretically, as an increase in mutual information shared by the conglomerate across time. The emergence of structure and useful separation inside the coupled system is accompanied by self-organization of internal processing, i.e. an increase in complexity within the evolving system.

This rather philosophical paper discusses the necessary three ingredients which together allow a collective phenomenon to be described as “emergent”. First the phenomenon, as usual, requires a group of agents entering in a non-linear relationship and entailing the existence of two semantic descriptions depending on the scale of observation. Second this phenomenon has to be observed by a mechanical observer instead of a human one, which has the natural capacity for temporal and/or spatial integration. Finally, for this natural observer to detect and select the collective phenomenon, it needs to do so in rewards of the adaptive value this phenomenon is responsible for. The presence of natural selection drives us to defend, with many authors, the idea that emergent phenomena can only belong to biology. After a brief philosophical plea, we present a simple and illustrative computer thought experiment in which a society of agents evolves a stigmergic collective behavior as an outcome of its greater adaptive value. The three ingredients are illustrated and discussed within this experimental context.

Our agent-based model of genotype editing is defined by two distinct genetic components: a coding portion encoding phenotypic solutions, and a non-coding portion used to edit the coding material. This set up leads to an indirect, stochastic genotype/phenotype mapping which captures essential aspects of RNA editing. We show that, in drastically changing environments, genotype editing leads to qualitatively different solutions from those obtained via evolutionary algorithms that only use coding genetic material. In particular, we show how genotype editing leads to the emergence of regulatory signals, and also to a resilient memory of a previous environment

In the natural world, individual organisms can adapt as their environment changes. In most evolution, however, individual organisms tend to consist of rigid solutions, with all adaptation occurring at the population level. If we are to use artificial evolving systems as a tool in understanding biology or in engineering robust and intelligent systems, however, they should be able to generate solutions with fitness-enhancing phenotypic plasticity. Here we use Avida, an established digital evolution system, to investigate the selective pressures that produce phenotypic plasticity. We witness two different types of fitness-enhancing plasticity evolve: plasticity, in which the same sequence of actions produces different results depending on the environment, and plasticity, where organisms choose their actions based on their environment. We demonstrate that the type of plasticity that evolves depends on the environmental challenge the population faces. Finally, we compare our results to similar ones found in vastly different systems, which suggest that this phenomenon is a general feature of evolution.

Current biological theory has no commonly accepted view on the phenomenon of aging. On the one hand it is considered as an inescapable degradation immanent to complex biological systems and on the other hand as outcome of evolution. At the moment, there are three major complementary theories of evolutionary origin of senescence – the programmed death theory, the mutation accumulation theory, and the antagonistic pleiotropy theory. The later two are rather extensively studied theoretically and computationally but the former one received less attention. Here we present computer multi-agent model of aging evolution compatible with theories of programmed death and mutation accumulation. In our study we test how presence of aggression and kin-recognition affects evolution of age dependent suicide which is an analog of programmed death in the model.

Artificial selection of microbial ecosystems for their collective function has been shown to be effective in laboratory experiments. In previous work, we used evolutionary simulation models to understand the mechanistic basis of the observed ecosystem-level response to artificial selection. Here we extend this work to consider artificial ecosystem selection as a method for evolutionary optimisation. By allowing solutions involving multiple species, artificial ecosystem selection adds a new class of multi-species solution to the available search space, while retaining all the single-species solutions achievable by lower-level selection methods. We explore the conditions where multi-species solutions (that necessitate higher-level selection) are likely to be found, and discuss the potential advantages of artificial ecosystem selection as an optimisation method.