Sara Montagna

Description and composition of bio-inspired design patterns: a complete overview

In the last decade, bio-inspired self-organising mechanisms have been applied to different domains, achieving results beyond traditional approaches. However, researchers usually use these mechanisms in an ad-hoc manner. In this way, their interpretation, definition, boundary (i.e. when one mechanism stops, and when another starts), and implementation typically vary in the existing literature, thus preventing these mechanisms from being applied clearly and systematically to solve recurrent problems. To ease engineering of artificial bio-inspired systems, this paper describes a catalogue of bio-inspired mechanisms in terms of modular and reusable design patterns organised into different layers. This catalogue uniformly frames and classifies a variety of different patterns. Additionally, this paper places the design patterns inside existing self-organising methodologies and hints for selecting and using a design pattern.

Spatial Coordination of Pervasive Services through Chemical-Inspired Tuple Spaces

To support and engineer the spatial coordination of distributed pervasive services, we propose a chemical-inspired model, which extends tuple spaces with the ability of evolving tuples mimicking chemical systems, that is, in terms of reaction and diffusion rules that apply to tuples modulo semantic match. The suitability of this model is studied by considering a self-adaptive display infrastructure providing people nearby with several visualization services (advertisements, news, personal and social content). The key result of this article is that general-purpose chemical reactions inspired by population dynamics can be used in pervasive applications to enact spatial computing patterns of competition and gradient-based interaction.

Self-aware Pervasive Service Ecosystems

Here we present the overall objectives and approach of the SAPERE (“Self-aware Pervasive Service Ecosystems”) project, focussed on the development of a highly-innovative nature-inspired framework, suited for the decentralized deployment, execution, and management, of self-aware and adaptive pervasive services in future network scenarios.

Chemical-oriented simulation of computational systems with ALCHEMIST

In this paper we address the engineering of complex and emerging computational systems featuring situatedness, adaptivity and self-organisation, like pervasive computing applications in which humans and devices, dipped in a very mobile environment, opportunistically interact to provide and exploit information services. We adopt a meta-model in which possibly mobile, interconnected and communicating agents work according to a set of chemical-like laws. According to this view, substantiated by recent research on pervasive computing systems, we present the Alchemist simulation framework, which retains the performance of known Stochastic Simulation Algorithms for (bio)chemistry, though it is tailored to the specific features of complex and situated computational systems.

Developing pervasive multi-agent systems with nature-inspired coordination

Pervasive computing systems can be modelled effectively as populations of interacting autonomous components. The key challenge to realizing such models is in getting separately-specified and -developed sub-systems to discover and interoperate with each other in an open and extensible way, supported by appropriate middleware services. In this paper, we argue that nature-inspired coordination models offer a promising way of addressing this challenge. We first frame the various dimensions along which nature-inspired coordination models can be defined, and survey the most relevant proposals in the area. We describe the nature-inspired coordination model developed within the SAPERE project as a synthesis of existing approaches, and show how it can effectively support the multifold requirements of modern and emerging pervasive services. We conclude by identifying what we think are the open research challenges in this area, and identify some research directions that we believe are promising.

A&A for modelling and engineering simulations in Systems Biology

Systems Biology (SB) promotes a system-level understanding of biological systems, and requires modelling and simulation tools for analysing biological systems dynamics. The articulation of Multiagent Systems (MASs) in terms of multiple, distributed and autonomous computational entities makes MAS a seemingly fit paradigm in SB. In this paper we adopt the Agents and Artefacts (A&A) metamodel where the notions of agent, artefact and workspace are taken as the basic bricks for MASs as the ontological foundation for our Multiagent-based Simulation (MABS) framework, and discuss how this impacts on SB. After recasting the A&A abstractions within the domain and design models, we specialise A&A within the SB context, and show an operational model based on the TuCSoN agent coordination infrastructure, upon which our simulation framework is implemented. As a case study, we model the well-studied metabolic pathway of glycolysis, and present some results of the simulation.

Description and composition of bio-inspired design patterns: the gradient case

Bio-inspired mechanisms have been extensively used in the last decade for solving optimisation problems and for decentralised control of sensors, robots or nodes in P2P systems. Different attempts at describing some of these mechanisms have been proposed, some of them under the form of design patterns. However, there is not so far a clear catalogue of these mechanisms, described as patterns, showing the relations between the different patterns and identifying the precise boundaries of each mechanism. To ease engineering of artificial bio-inspired systems, this paper describes a group of bio-inspired mechanisms in terms of design patterns organised into different layers. This approach is exemplified through the description of 7 bio-inspired mechanisms: three basic ones (Spreading, Aggregation, and Evaporation), a mid-level one (Gradient) obtained by composing the basic ones, and three top-level ones (Chemotaxis, Morphogenesis, and Quorum sensing) exploiting the mid-level one.

BIO-CORE: Bio-inspired Self-organising Mechanisms Core

This paper discusses the notion of “core bio-inspired services” - low-level services providing basic bio-inspired mechanisms, such as evaporation, aggregation or spreading - shared by higher-level services or applications. Design patterns descriptions of self-organising mechanisms, such as gossip, morphogenesis, or foraging, show that these higher-level mechanisms are composed of basic bio-inspired mechanisms (e.g. digital pheromone is composed of spreading, aggregation and evaporation). In order to ease design and implementation of self-organising applications (or high-level services), by supporting reuse of code and algorithms, this paper proposes BIO-CORE, an execution model that provides these low-level services at the heart of any middleware or infrastructure supporting such applications, and provides them as “core” built-in services around which all other services are built.

An automatic quantification and registration strategy to create a gene expression atlas of zebrafish embryogenesis

In order to properly understand and model the gene regulatory networks in animals development, it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains. In this paper, we propose a complete computational framework to fulfill this task and create a 3D Atlas of the early zebrafish embryogenesis annotated with both the cellular localizations and the level of expression of different genes at different developmental stages. The strategy to construct such an Atlas is described here with the expression pattern of 5 different genes at 6 hours of development post fertilization.

A Framework for Modelling and Simulating Networks of Cells

Several complex biological phenomena are to be modelled in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events plays an essential role. Key examples are embryogenesis and morphogenesis processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. This paper introduces a practical framework for modelling and simulating these scenarios. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespies direct method. As an example of application of our framework, we model the first stages of Drosophila Melanogaster development, which generate the early spatial pattern of gene expression, and we show the correctness of our model comparing the simulation results with real data of gene expression and spatial/temporal resolution acquired in free on-line sources.