A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Simulation and Analysis of Distributed Reaction Systems
Tekijät: Brodo, Linda; Bruni, Roberto; Falaschi, Moreno; Petre, Ion
Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)
Kustannuspaikka: PISCATAWAY
Julkaisuvuosi: 2025
Journal: IEEE Access
Tietokannassa oleva lehden nimi: IEEE Access
Lehden akronyymi: IEEE ACCESS
Vuosikerta: 13
Aloitussivu: 119709
Lopetussivu: 119725
Sivujen määrä: 17
ISSN: 2169-3536
eISSN: 2169-3536
DOI: https://doi.org/10.1109/ACCESS.2025.3586078
Verkko-osoite: https://doi.org/10.1109/access.2025.3586078
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/499375201
Reaction systems (RSs) are a computational framework inspired by the interplay between biochemical interactions. Similarly to regulatory networks, reaction products can dynamically activate or inhibit other reactions. This provides a framework for discrete-time, interactive computation, where each state is determined by those reactions enabled in the immediately preceding state and by additional environmental interventions, if any. Since their introduction, RSs have been extended to study many aspects of complex systems: multi-agent collaborations, cause-effect relationships, model checking, and many others. The gap in the literature addressed in this paper is the lack of software tools for simulating and analysing distributed reaction systems (DRSs). We introduce a process algebraic approach to describing, simulating, and analysing DRSs. This allows designing complex models by re-using modular components in a well-structured and compositional way, and analysing them with the BioReSolve modelling software. We demonstrate our approach by experimenting with distributed Lotka-Volterra models, where multiple agents evolve according to their own periodic dynamics, but can also synchronise their cycles through diverse communication topologies.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
This work was supported in part by Italian Ministero dell' Universita e della Ricerca (MUR) Progetti di ricerca di Rilevante Interesse Nazionale (PRIN) 2022 Project ''MEDICA'' under Grant 2022RNTYWZ and Grant CUP_B53D23013170006; in part by the NextGeneration EU Programme Project Piano Nazionale di Ripresa e Resilienza (PNRR) ECS00000017-''Tuscany Health Ecosystem (THE)''-Spoke 3 under Grant CUP B63C22000680007; in part by Italian MUR PRIN PNRR 2022 Project ''Decentralized Ledgers inCircular Economy (DELICE)'' under Grant P20223T2MF; in part by Italian MUR PRIN 2022 PNRR Project ''Resource Awareness in Programming: Algebra, Rewriting, and Analysis'' under Grant P2022HXNSC; in part by the Next Generation EU Programme Project PNRR ''SEcurity and RIghts In the Cyber Space (SERICS)'' under Grant PE00000014 and Grant CUP H73C2200089001; and in part bythe Istituto Nazionale di Alta Matematica (INdAM)-Gruppo Nazionale per il Calcolo Scientifico (GNCS) Project under Grant CUP_E53C22001930001.
