Zipper logic

Zipper logic is a graph rewrite system, consisting in only local rewrites on a class of zipper graphs. Connections with the chemlambda artificial chemistry and with knot diagrammatics based computation are explored in the article.Comment: 16 pages, 24 colour figure

Exploring evolutionary stability in a concurrent artificial chemistry

Multi-level selection has proven to be an affective mean to provide resistance against parasites for catalytic networks (Cronhjort and Blomberg, 1997). One way to implement these multi-level systems is to group molecules into several distinct compartments (cells) which are capable of cellular division (where an offspring cell replaces another cell). In such systems parasitized cells decay and are ultimately displaced by neighboring healthy cells. However in relatively small cellular populations, it is also possible that infected cells may rapidly spread parasites throughout the entire cellular population. In which case, group selection may fail to provide resistance to parasites. In this paper, we propose a concurrent artificial chemistry (AC) which has been implemented on a cluster of computers where each cell is running on a single CPU. This multi-level selectional artificial chemistry called the Molecular Classifier Systems was based on the Holland broadcast language. An attribute inherent to such a concurrent system is that the computational complexity of the molecular species contained in a reactor may now affect the fitness of the cell. This molecular computational cost may be regarded as the chemical activation energy necessary for a reaction to occur. Such a property is often not considered in typical Artificial Life models. Our experimental results obtained with this system suggest that this activation energy property may improve the resistance to parasites for catalytic networks. This work highlights some of the benefits that could be obtained using a concurrent architecture on top of computational efficiency. We first briefly present the Molecular Classifier Systems, this is then followed by a description of the multi-level concurrent model. Finally we discuss the benefits of using this multi-level concurrent model to enhance evolutionary stability for catalytic networks in our AC

Unexpected evolutionary dynamics in a string based artificial chemistry

This work investigates closure in Cell Signaling Networks, which is one research area within the ESIGNET project. We employ a string-based Artificial Chemistry based on Holland’s broadcast language (Molecular Classifier System, Broadcast Language, or MCS.b). We present a series of experiments focusing on the emergence and evolution of self-maintaining molecular organizations. Such experiments naturally relate to similar studies conducted in artificial chemistries such as Tierra, Alchemy and Alpha-Universes. However, our results demonstrate some counter-intuitive outcomes, not indicated in previous literature. Each of these “unexpected” evolutionary dynamics (including an elongation catastrophe phenomenon) are examined and explained both informally and formally. We also demonstrate how the elongation catastrophe can be prevented using a multi-level selectional model of the MCS.b (which acts both at the molecular and cellular level). This work provides complementary insights into the understanding of evolutionary dynamics in minimal artificial chemistries

Development and Evolution of Neural Networks in an Artificial Chemistry

We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and cell stimulation. We demonstrate the model's power with a hand-written genome that leads to the growth of a simple network which performs classical conditioning. To evolve more complex structures, we implemented a platform-independent, asynchronous, distributed Genetic Algorithm (GA) that allows users to participate in evolutionary experiments via the World Wide Web.Comment: 8 pages LaTeX, style file included, 8 embedded postscript figures. To be published in Proc. of 3rd German Workshop on Artificial Life (GWAL

A framework and simulation engine for studying artificial life

The area of computer-generated artificial life-forms is a relatively recent field of inter-disciplinary study that involves mathematical modelling, physical intuition and ideas from chemistry and biology and computational science. Although the attribution of “life” to non biological systems is still controversial, several groups agree that certain emergent properties can be ascribed to computer simulated systems that can be constructed to “live” in a simulated environment. In this paper we discuss some of the issues and infrastructure necessary to construct a simulation laboratory for the study of computer generated artificial life-forms. We review possible technologies and present some preliminary studies based around simple models

Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity

Programs as Polypeptides

We describe a visual programming language for defining behaviors manifested by reified actors in a 2D virtual world that can be compiled into programs comprised of sequences of combinators that are themselves reified as actors. This makes it possible to build programs that build programs from components of a few fixed types delivered by diffusion using processes that resemble chemistry as much as computation.Comment: in European Conference on Artificial Life (ECAL '15), York, UK, 201

Influence of drainage status on soil and water chemistry, litter decomposition and soil respiration in central Amazonian forests on sandy soils

Central Amazonian rainforest landscape supports a mosaic of tall terra firme rainforest and ecotone campinarana, riparian and campina forests, reflecting topography-induced variations in soil, nutrient and drainage conditions. Spatial and temporal variations in litter decomposition, soil and groundwater chemistry and soil CO2 respiration were studied in forests on sandy soils, whereas drought sensitivity of poorly-drained valley soils was investigated in an artificial drainage experiment. Slightly changes in litter decomposition or water chemistry were observed as a consequence of artificial drainage. Riparian plots did experience higher litter decomposition rates than campina forest. In response to a permanent lowering of the groundwater level from 0.1 m to 0.3 m depth in the drainage plot, topsoil carbon and nitrogen contents decreased substantially. Soil CO2 respiration decreased from 3.7±0.6 µmol m-2 s-1 before drainage to 2.5±0.2 and 0.8±0.1 µmol m-2 s-1 eight and 11 months after drainage, respectively. Soil respiration in the control plot remained constant at 3.7±0.6 µmol m-2 s-1. The above suggests that more frequent droughts may affect topsoil carbon and nitrogen content and soil respiration rates in the riparian ecosystem, and may induce a transition to less diverse campinarana or short-statured campina forest that covers areas with strongly-leached sandy soil

Solving Vertical Transport and Chemistry in Air Pollution Models.

For the time integration of stiff transport-chemistry problems from air pollution modelling, standard ODE solvers are not feasible due to the large number of species and the 3D nature. The popular alternative, standard operator splitting, introduces artificial transients for short-lived species. This complicates the chemistry solution, easily causing large errors for such species. In the framework of an operational global air pollution model, we focus on the problem formed by chemistry and vertical transport, which is based on diffusion, cloud-related vertical winds, and wet deposition. Its specific nature leads to full Jacobian matrices, ruling out standard implicit integration. We compare Strang operator splitting with two alternatives: source splitting and an (unsplit) Rosenbrock method with approximate matrix factorization, all having equal computational cost. The comparison is performed with real data. All methods are applied with half-hour time steps, and give good accuracies. Rosenbrock is the most accurate, and source splitting is more accurate than Strang splitting. Splitting errors concentrate in short-lived species sensitive to solar radiation and species with strong emissions and depositions