Boolean Networks and Their Applications in Science and Engineering

Jose C. Valverde, Henning S. Mortveit, Carlos Gershenson, and Yongtang Shi

Complexity
Volume 2020, Article ID 6183798

 

In recent decades, Boolean networks (BN) have emerged as an effective mathematical tool to model not only computational processes, but also several phenomena in science and engineering. For this reason, the development of the theory of such models has become a compelling need that has attracted the interest of many research groups in recent years. Dynamics of BN are traditionally associated with complexity, since they are composed of many elemental units whose behavior is relatively simple in comparison with the behavior of the entire system.

BN are a generalization of other relevant mathematical models, which appeared previously as cellular automata (CA), inspired by von Neumann and studied by Wolfram and others to explore the computational universe, or Kauffman networks (KN), proposed by Kauffman in 1969 for modeling gene regulatory networks. This gives an idea of the versatility of this new paradigm in applications to several branches of science (mathematics, physics chemistry, biology, ecology, etc.) and engineering (computing, artificial intelligence, electronics, circuits, etc.).

The aim of this special issue was to collect cutting-edge research on the different models of BN (deterministic and nondeterministic, synchronous and asynchronous, homogenous and non-homogenous, directed and undirected, regular and non-regular, etc.). Thus, several research groups in this field submitted their recent developments and future research directions concerning new models. In addition, original research articles showing some applications of BN in science and engineering were received.

 

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