Emergence of auto-catalytic structure in stochastic self-reinforcing reaction networks

We consider a chemical reaction network model in which some of the reactions are stochastic and depend on past history. In this chemical reaction network, we found the emergence of autocatalytic sets (ACS) and complex dynamics in which ACS are repeatedly created and destroyed. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Introduction of a new platform for parameter estimation of kinetically complex environmental systems

A modeling framework (ReKinSim - Reaction Kinetics Simulator) is introduced, within which biogeochemical reactions in environmental systems can be described and inversely fitted to experimental data. Three key features of this simulation environment are: (1) a generic mathematical tool for solving sets of unlimited, arbitrary, non-linear ordinary differential equations; (2) no limitation to the number or type of reactions or other influential dynamics (e.g., isotope fractionation or small-scale mass-transfer limitations); (3) an easy to use and flexible module for nonlinear data-fitting. It allows users to easily define any kinetic model by a set of biogeochemical reactions relevant to the experimental application and to obtain the values of the kinetic parameters by fitting of the model to data. By allowing users to include the environmentally related processes and solving them along with the chemical kinetics, ReKinSim helps the user to elucidate the extent that these processes are controlled by factors other than kinetics. The novelty of the presented program primary lays in its unique combination of flexibility, computational efficiency and user-friendliness. ReKinSim's usability is showcased by four case studies of varying complexity, and compared against a set of currently available modeling tools.     

Exploring Lake Victoria ecosystem functioning using the Atlantis modeling framework

Lake Victoria has experienced human induced pressures such as overfishing, introduction of alien species, increased eutrophication and climate change impacts. However, there is limited understanding of the system dynamics, major processes, drivers and responses to the changes. To address this challenge, we developed the first end-to-end whole ecosystem model (Atlantis) for the lake. The model is spatially resolved into 12 unique dynamic areas based on depth, species composition, physical-chemical characteristics and fisheries management zones. A total of 38 functional groups constitute the biological model. Four fishing fleets with different targeting options are simulated. Reliability of the model is confirmed by the good fit of simulations output to observational data sets. Herein, we describe the evolution of the biophysical system, illustrating how it responded to the aforementioned induced perturbations since 1958. The constructed virtual Lake Victoria ecosystem model provides a platform for exploring the impact of management interventions before actual implementation.     

Approaching parallel computing to simulating population dynamics in demography

Agent-based modelling and simulation is a promising methodology that can be applied in the study of population dynamics. The main advantage of this technique is that it allows representing the particularities of the individuals that are modeled along with the interactions that take place among them and their environment. Hence, classical numerical simulation approaches are less adequate for reproducing complex dynamics. Nowadays, there is a rise of interest on using distributed computing to perform large-scale simulation of social systems. However, the inherent complexity of this type of applications is challenging and requires the study of possible solutions from the parallel computing perspective (e.g., how to deal with fine grain or irregular workload). In this paper, we discuss the particularities of simulating populating dynamics by using parallel discrete event simulation methodologies. To illustrate our approach, we present a possible solution to make transparent the use of parallel simulation for modeling demographic systems: Yades tool. In Yades, modelers can easily define models that describe different demographic processes with a web user interface and transparently run them on any computer architecture environment thanks to its demographic simulation library and code generator. Therefore, transparency is provided by two means: the provision of a web user interface where modelers and policy makers can specify their agent-based models with the tools they are familiar with, and the automatic generation of the simulation code that can be executed in any platform (cluster or supercomputer). A study is conducted to evaluate the performance of our solution in a High Performance Computing environment. The main benefit of this outline is that our findings can be generalized to problems with similar characteristics to our demographic simulation model.     

Dynamic self-assembly in living systems as computation

Biochemical reactions taking place in living systems that map different inputs to specific outputs are intuitively recognized as performing information processing. Conventional wisdom distinguishes such proteins, whose primary function is to transfer and process information, from proteins that perform the vast majority of the construction, maintenance, and actuation tasks of the cell (assembling and disassembling macromolecular structures, producing movement, and synthesizing and degrading molecules). In this paper, we examine the computing capabilities of biological processes in the context of the formal model of computing known as the random access machine (RAM) [Dewdney AK (1993) The New Turing Omnibus. Computer Science Press, New York], which is equivalent to a Turing machine [Minsky ML (1967) Computation: Finite and Infinite Machines. Prentice-Hall, Englewood Cliffs, NJ]. When viewed from the RAM perspective, we observe that many of these dynamic self-assembly processes – synthesis, degradation, assembly, movement – do carry out computational operations. We also show that the same computing model is applicable at other hierarchical levels of biological systems (e.g., cellular or organism networks as well as molecular networks). We present stochastic simulations of idealized protein networks designed explicitly to carry out a numeric calculation. We explore the reliability of such computations and discuss error-correction strategies (algorithms) employed by living systems. Finally, we discuss some real examples of dynamic self-assembly processes that occur in living systems, and describe the RAM computer programs they implement. Thus, by viewing the processes of living systems from the RAM perspective, a far greater fraction of these processes can be understood as computing than has been previously recognized.     

A parallel machine for multiset transformation and its programming style

One of the most challenging problems in the field of computing science today concerns the development of software for more and more powerful parallel machines. In order to tackle this issue we present a new paradigm for parallel processing; this model, called Γ, is based on the chemical reaction metaphor: the only data structure is the multiset and the computation can be seen as a succession of chemical reactions consuming elements of the multiset and producing new elements according to specific rules. We detail some examples showing the relevance of this model for AI applications. Furthermore, due to its lack of imperative features, this language can be very naturally implemented in a distributed way. We describe an implementation of Γ on a vector architecture. The advantages of this architecture is that it needs very simple processors (with two ports) and all the processors perform the same job. So we advocate the separation of the design of programs for massively parallel machines into two steps which can be verified in a formal way: the construction of a program with implicit parallelism (Γ-program) and its translation into a network of processes.     

Aligning Business and It: The Process-Driven Architecture Model

Due to the fact that the corporate IT function is tightly coupled to enterprise processes and the organization's information needs, today's IT investments and processes are driven by the same dynamics as the enterprise itself. This article describes a model of a four-layer process-driven architecture (PDA) that can be used as a framework for addressing the alignment of business and IT.     

Emulating cellular automata in chemical reaction–diffusion networks

Chemical reactions and diffusion can produce a wide variety of static or transient spatial patterns in the concentrations of chemical species. Little is known, however, about what dynamical patterns of concentrations can be reliably programmed into such reaction–diffusion systems. Here we show that given simple, periodic inputs, chemical reactions and diffusion can reliably emulate the dynamics of a deterministic cellular automaton, and can therefore be programmed to produce a wide range of complex, discrete dynamics. We describe a modular reaction–diffusion program that orchestrates each of the fundamental operations of a cellular automaton: storage of cell state, communication between neighboring cells, and calculation of cells’ subsequent states. Starting from a pattern that encodes an automaton’s initial state, the concentration of a “state” species evolves in space and time according to the automaton’s specified rules. To show that the reaction–diffusion program we describe produces the target dynamics, we simulate the reaction–diffusion network for two simple one-dimensional cellular automata using coupled partial differential equations. Reaction–diffusion based cellular automata could potentially be built in vitro using networks of DNA molecules that interact via branch migration processes and could in principle perform universal computation, storing their state as a pattern of molecular concentrations, or deliver spatiotemporal instructions encoded in concentrations to direct the behavior of intelligent materials.     

Modeling the effect of task and graphical representation on response latency in a graph reading task

We report an investigation into the processes involved in a common graph-reading task using two types of Cartesian graph. We describe an experiment and eye movement study, the results of which show that optimal scan paths assumed in the task analysis approximate the detailed sequences of saccades made by individuals. The research demonstrates the computational inequivalence of two sets of informationally equivalent graphs and illustrates how the computational advantages of a representation outweigh factors such as user unfamiliarity. We describe two models, using the ACT rational perceptual motor (ACT-R/PM) cognitive architecture, that replicate the pattern of observed response latencies and the complex scan paths revealed by the eye movement study. Finally, we outline three guidelines for designers of visual displays: Designers should (a) consider how different quantities are encoded within any chosen representational format, (b) consider the full range of alternative varieties of a given task, and (c) balance the cost of familiarization with the computational advantages of less familiar representations. Actual or potential applications of this research include informing the design and selection of appropriate visual displays and illustrating the practice and utility of task analysis, eye tracking, and cognitive modeling for understanding interactive tasks with external representations.     

Applying empirical software engineering to software architecture: challenges and lessons learned

In the last 15 years, software architecture has emerged as an important software engineering field for managing the development and maintenance of large, software-intensive systems. Software architecture community has developed numerous methods, techniques, and tools to support the architecture process (analysis, design, and review). Historically, most advances in software architecture have been driven by talented people and industrial experience, but there is now a growing need to systematically gather empirical evidence about the advantages or otherwise of tools and methods rather than just rely on promotional anecdotes or rhetoric. The aim of this paper is to promote and facilitate the application of the empirical paradigm to software architecture. To this end, we describe the challenges and lessons learned when assessing software architecture research that used controlled experiments, replications, expert opinion, systematic literature reviews, observational studies, and surveys. Our research will support the emergence of a body of knowledge consisting of the more widely-accepted and well-formed software architecture theories.     

An architecture for survivable coordination in large distributedsystems

Coordination among processes in a distributed system can be rendered very complex in a large-scale system where messages may be delayed or lost and when processes may participate only transiently or behave arbitrarily, e.g. after suffering a security breach. In this paper, we propose a scalable architecture to support coordination in such extreme conditions. Our architecture consists of a collection of persistent data servers that implement simple shared data abstractions for clients, without trusting the clients or even the servers themselves. We show that, by interacting with these untrusted servers, clients can solve distributed consensus, a powerful and fundamental coordination primitive. Our architecture is very practical, and we describe the implementation of its main components in a system called Fleet     

Nonparametric identification of population models via Gaussian processes

Population models are used to describe the dynamics of different subjects belonging to a population and play an important role in drug pharmacokinetics. A nonparametric identification scheme is proposed in which both the average impulse response of the population and the individual ones are modelled as Gaussian stochastic processes. Assuming that the average curve is an integrated Wiener process, it is shown that its estimate is a cubic spline. An empirical Bayes algorithm for estimating both the average and the individual curves is worked out. The model is tested on simulated data sets as well as on xenobiotics pharmacokinetic data.     

On the undecidability of the limit behavior of Cellular Automata

Cellular Automata (CA) are discrete dynamical systems and an abstract model of parallel computation. The limit set of a cellular automaton is its maximal topological attractor. A well-known result, due to Kari, says that all nontrivial properties of limit sets are undecidable. In this paper we consider the properties of limit set dynamics, i.e. properties of the dynamics of CA restricted to their limit sets. There can be no equivalent of Kari’s theorem for limit set dynamics. Anyway we show that there is a large class of undecidable properties of limit set dynamics, namely all properties of limit set dynamics which imply stability or the existence of a unique subshift attractor. As a consequence we have that it is undecidable whether the cellular automaton map restricted to the limit set is the identity map and whether it is closing, injective, expansive, positively expansive and transitive.     

Aligning Business and It: The Process-Driven Architecture Model

Abstract

Due to the fact that the corporate IT function is tightly coupled to enterprise processes and the organization's information needs, today's IT investments and processes are driven by the same dynamics as the enterprise itself. This article describes a model of a four-layer process-driven architecture (PDA) that can be used as a framework for addressing the alignment of business and IT.     

Manipulating convention emergence using influencer agents

Coordination in open multi-agent systems (MAS) can reduce costs to agents associated with conflicting goals and actions, allowing artificial societies to attain higher levels of aggregate utility. Techniques for increasing coordination typically involve incorporating notions of conventions, namely socially adopted standards of behaviour, at either an agent or system level. As system designers cannot necessarily create high quality conventions a priori, we require an understanding of how agents can dynamically generate, adopt and adapt conventions during their normal interaction processes. Many open MAS domains, such as peer-to-peer and mobile ad-hoc networks, exhibit properties that restrict the application of the mechanisms that are often used, especially those requiring the incorporation of additional components at an agent or society level. In this paper, we use Influencer Agents (IAs) to manipulate convention emergence, which we define as agents with strategies and goals chosen to aid the emergence of high quality conventions in domains characterised by heterogeneous ownership and uniform levels of agent authority. Using the language coordination problem (Steels in Artif Life 2(3):319–392, 1995), we evaluate the effect of IAs on convention emergence in a population. We show that relatively low proportions of IAs can (i) effectively manipulate the emergence of high-quality conventions, and (ii) increase convention adoption and quality. We make no assumptions involving agent mechanism design or internal architecture beyond the usual assumption of rationality. Our results demonstrate the fragility of convention emergence in the presence of malicious or faulty agents that attempt to propagate low quality conventions, and confirm the importance of social network structure in convention adoption.     

A quantization procedure based on completely positive maps and Markov operators

We describe ω-limit sets of completely positive (CP) maps over finite-dimensional spaces. In such sets and in its corresponding convex hulls, CP maps present isometric behavior and the states contained in it commute with each other. Motivated by these facts, we describe a quantization procedure based on CP maps which are induced by Markov (transfer) operators. Classical dynamics are described by an action over essentially bounded functions. A non-expansive linear map, which depends on a choice of a probability measure, is the centerpiece connecting phenomena over function and matrix spaces.     

MaTRICS: A service-based management tool for remote intelligent configuration of systems

With MaTRICS, we describe a service-oriented architecture that allows remotely connected users to modify the configuration of any service provided by a specific (application) server, like email-, news- or web-servers. Novel to our approach is that the system can manage configuration processes on heterogeneous software- and hardware-platforms, which are performed from a variety of peripherals unmatched in today’s practice, where devices like mobile phones, faxes, PDAs are enabled to be used by system managers as remote system configuration and management tools.     

Virtual test tubes

By their structure and operation, biomolecules have resolved fundamental problems as a distributed computational system that we are just beginning to unveil. One advantageous approach to gain a good understanding of the processes and algorithms involved is simulation on conventional computers. Simulations allow better understanding of the capabilities of molecules because they can occur at the level of reliability, efficiency, and programmability that are standard in conventional computation and are desirable for experiments in vitro. Here, we describe in some detail the architecture of a general-purpose simulation environment in silico, EdnaCo, establish its soundness and reliability, and benchmark its performance. The system can be described as an emulation of the events in a real test tube. We describe the major pieces of its architecture, namely, a distributed memory (file) system, a kinetic engine, and input/output mechanisms. Finally, the ability of this environment in preserving major features of the wet counterpart in vitro is evaluated via an implementation on a cluster of PCs. The results of several simulations are summarized that establish the soundness, utility, applicability, and cost efficiency of the software to facilitate experimentation in vitro.     

A Control Architecture for Robot Swarms (AMEB)

Abstract

A control architecture for heterogeneous robot swarms (AMEB) is proposed in this work. The architecture manages the processes that occur in the system and its main objectives are to allow the emergence and self-organization in the group. It is structured in three levels: an individual level, a collective level under the philosophy of emergent behavior, and a level for the management of learning and knowledge. The architecture includes a behavioral component that allows the inclusion of emotions in the members of the swarm. Finally, it described a method for verifying the occurrence of the emergence in the swarm, using fuzzy cognitive maps.