Melding Boolean networks and reaction systems under synchronous, asynchronous and most permissive semantics
Open Access
- 1 June 2024
- journal article
- research article
- Published by Springer Nature in Natural Computing
- Vol. 23 (2) , 235-267
Abstract
This paper forges a strong connection between two well known computational frameworks for representing biological systems, in order to facilitate the seamless transfer of techniques between them. Boolean networks are a well established formalism employed from biologists. They have been studied under different (synchronous and asynchronous) update semantics, enabling the observation and characterisation of distinct facets of system behaviour. Recently, a new semantics for Boolean networks has been proposed, called most permissive semantics, that enables a more faithful representation of biological phenomena. Reaction systems offer a streamlined formalism inspired by biochemical reactions in living cells. Reaction systems support a full range of analysis techniques that can help for gaining deeper insights into the underlying biological phenomena. Our goal is to leverage the available toolkit for predicting and comprehending the behaviour of reaction systems within the realm of Boolean networks. In this paper, we first extend the behaviour of reaction systems to several asynchronous semantics, including the most permissive one, and then we demonstrate that Boolean networks and reaction systems exhibit isomorphic behaviours under the synchronous, general/fully asynchronous and most permissive semantics.Keywords
Funding Information
- Università di Pisa
This publication has 33 references indexed in Scilit:
- Causal analysis of positive Reaction SystemsInternational Journal on Software Tools for Technology Transfer, 2024
- Quantitative extensions of reaction systems based on SOS semanticsNeural Computing and Applications, 2023
- Dynamic Slicing of Reaction Systems Based on Assertions and Monitors2023
- Process Algebras in Systems Biology2022
- Controllability of reaction systemsJournal of Membrane Computing, 2020
- A survey of gene regulatory networks modelling methods: from differential equations, to Boolean and qualitative bioinspired modelsJournal of Membrane Computing, 2020
- Concurrency in Boolean networksNatural Computing, 2019
- Fixed Points and Attractors of Reaction Systems2014
- Rule-Based Modeling of Biochemical Systems with BioNetGen2009
- P Systems, a New Computational Modelling Tool for Systems BiologyLecture Notes in Computer Science, 2006