Paper Info
Title
Reaction Networks As A Language For Systemic Modeling: On The Study Of Structural Changes
Abstract
Reaction Networks have been recently proposed as a framework for systems modeling due to its capability to describe many entities interacting in contextual ways and leading to the emergence of meta-structures. Since systems can be subjected to structural changes that not only alter their inner functioning, but also their underlying ontological features, a crucial issue is how to address these structural changes within a formal representational framework. When modeling systems using reaction networks, we find that three fundamentally different types of structural change are possible. The first corresponds to the usual notion of perturbation in dynamical systems, i.e., change in system's state. The second corresponds to behavioral changes, i.e., changes not in the state of the system but on the properties of its behavioral rules. The third corresponds to radical structural changes, i.e., changes in the state-set structure and/or in reaction-set structure. In this article, we describe in detail the three types of structural changes that can occur in a reaction network, and how these changes relate to changes in the systems observable within this reaction network. In particular, we develop a decomposition theorem to partition a reaction network as a collection of dynamically independent modules, and show how such decomposition allows for precisely identifying the parts of the reaction network that are affected by a structural change.
Year
DOI
Venue
2017
10.3390/systems5020030
SYSTEMS
Keywords
Field
DocType
reaction networks, chemical organization theory, system theory, emergence, structural change
Ontology,Observable,Structural change,Computer science,Theoretical computer science,Decomposition theorem,Dynamical systems theory,Systems modeling,Artificial intelligence,Natural language processing
Journal
Volume
Issue
ISSN
5
2
2079-8954
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Tomas Veloz16714.69
Pablo Razeto-Barry221.11