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

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

Closure in artificial cell signalling networks - investigating the emergence of cognition in collectively autocatalytic reaction networks

Cell Signalling Networks (CSNs) are complex biochemical networks responsible for the coordination of cellular activities in response to internal and external stimuli. We hypothesize that CSNs are subsets of collectively autocatalytic reaction networks. The signal processing or cognitive abilities of CSNs would originate from the closure properties of these systems. We investigate how Artificial CSNs, regarded as minimal cognitive systems, could emerge and evolve under this condition where closure may interact with evolution. To assist this research, we employ a multi-level concurrent Artificial Chemistry based on the Molecular Classifier Systems and the Holland broadcast language. A critical issue for the evolvability of such undirected and autonomous evolutionary systems is to identify the conditions that would ensure evolutionary stability. In this paper we present some key features of our system which permitted stable cooperation to occur between the different molecular species through evolution. Following this, we present an experiment in which we evolved a simple closed reaction network to accomplish a pre-specified task. In this experiment we show that the signal-processing ability (signal amplification) directly resulted from the evolved systems closure properties

The Evolution of complexity in self-maintaining cellular information processing networks

We examine the role of self-maintenance (collective autocatalysis) in the evolution of computational biochemical networks. In primitive proto-cells (lacking separate genetic machinery) self-maintenance is a necessary condition for the direct reproduction and inheritance of what we here term Cellular Information Processing Networks (CIPNs). Indeed, partially reproduced or defective CIPNs may generally lead to malfunctioning or premature death of affected cells. We explore the interaction of this self-maintenance property with the evolution and adaptation of CIPNs capable of distinct information processing abilities. We present an evolutionary simulation platform capable of evolving artificial CIPNs from a bottom-up perspective. This system is an agent-based multi-level selectional Artificial Chemistry (AC) which employs a term rewriting system called the Molecular Classifier System (MCS). The latter is derived from the Holland broadcast language formalism. Using this system, we successfully evolve an artificial CIPN to improve performance on a simple pre-specified information processing task whilst subject to the constraint of continuous self-maintenance. We also describe the evolution of self-maintaining, crosstalking and multitasking, CIPNs exhibiting a higher level of topological and functional complexity. This proof of concept aims at contributing to the understanding of the open-ended evolutionary growth of complexity in artificial systems

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance