author = {Artiom Alhazov and Rudolf Freund and Sergiu Ivanov and Sergey Verlan},
keywords = {Membrane computing, Natural computing, Numerical P systems, Reaction systems, Spiking neural P systems, Fuzzy P systems},
abstract = {Numerical P systems (NPS) are a very particular class of P systems having important differences from most models in this area. The main particularity of the model is the usage of numerical variables whose values are shared among applicable rules, contrary to the concurrence for objects in the multiset for the traditional P systems case. In 2007, Freund and Verlan developed a formal framework for P systems to capture most of the essential features of P systems and to define their functioning in a formal way. Subsequent papers developed versions of this framework for the case of spiking neural P systems and P systems with dynamically evolving structure. These results permitted to obtain a different view on P systems giving a general framework to analyze, relate and extend different variants of P systems and other related models, like Petri nets or register machines. This paper aims to provide a similar generalization for the case of numerical P systems (NPS) and related variants like enzymatic or generalized NPS. We call the obtained model Numerical Networks of Cells (NNC). As in the case of the formal framework it allows to accurately describe NPS, as well as other types of P systems like those using fuzzy sets as computation support. Also, the new model generalizes other well-known models like Boolean networks or reaction systems and this can potentially help to bring bridges between P systems and these areas.}
author = {Segretain, R{\'e}mi and Ivanov, Sergiu and Trilling, Laurent and Glade, Nicolas},
title = {Implementation of a Computing Pipeline for Evaluating the Extensibility of Boolean Networks{\textquoteright} Structure and Function},
elocation-id = {2020.10.02.323949},
year = {2020},
doi = {10.1101/2020.10.02.323949},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Formal interaction networks are well suited for representing complex biological systems and have been used to model signalling pathways, gene regulatory networks, interaction within ecosystems, etc. In this paper, we introduce Sign Boolean Networks (SBNs), which are a uniform variant of Threshold Boolean Networks (TBFs). We continue the study of the complexity of SBNs and build a new framework for evaluating their ability to extend, i.e. the potential to gain new functions by addition of nodes, while also maintaining the original functions. We describe our software implementation of this framework and show some first results. These results seem to confirm the conjecture that networks of moderate complexity are the most able to grow, because they are not too simple, but also not too constrained, like the highly complex ones. Biological Regulation, Biological Networks, Sign Boolean Networks, Complexity, Extensibility, Network GrowthCompeting Interest StatementThe authors have declared no competing interest.},
