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1

School of Science and Technology, University of Camerino, Camerino 62032, Italy

2

Computational Science, University of Amsterdam, Amsterdam 1098 XH, The Netherlands

3

Complexity Institute, Nanyang Technological University, Singapore 637723, Singapore

4

ITMO University, St. Petersburg 199034, Russian





*

Author to whom correspondence should be addressed.



Academic Editor: Raúl Alcaraz Martínez

Abstract In this paper, we propose a methodology for deriving a model of a complex system by exploiting the information extracted from topological data analysis. Central to our approach is the SB paradigm in which a complex system is represented by a two-level model. One level, the structural S one, is derived using the newly-introduced quantitative concept of persistent entropy, and it is described by a persistent entropy automaton. The other level, the behavioral B one, is characterized by a network of interacting computational agents. The presented methodology is applied to a real case study, the idiotypic network of the mammalian immune system. View Full-Text

Keywords: topological data analysis; persistent entropy automaton; higher dimensional automata; immune system; idiotypic network; computational agents topological data analysis; persistent entropy automaton; higher dimensional automata; immune system; idiotypic network; computational agents





Author: Emanuela Merelli 1,* , Matteo Rucco 1, Peter Sloot 2,3,4 and Luca Tesei 1

Source: http://mdpi.com/



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