author = {Alhazov, Artiom and Freund, Rudolf and Ivanov, Sergiu and Verlan, Sergey},
title = {Tissue P Systems with Vesicles of Multisets},
journal = {International Journal of Foundations of Computer Science},
volume = {33},
number = {03n04},
pages = {179-202},
year = {2022},
doi = {10.1142/S0129054122410015},
URL = {
https://doi.org/10.1142/S0129054122410015
},
eprint = {
https://doi.org/10.1142/S0129054122410015
}
,
abstract = { We consider tissue P systems working on vesicles of multisets with the very simple operations of insertion, deletion, and substitution of single objects. With the whole multiset being enclosed in a vesicle, sending it to a target cell can be indicated in those simple rules working on the multiset. As derivation modes we consider the sequential derivation mode, where, if possible, one rule is applied in a derivation step, and the set maximally parallel derivation mode, where in each derivation step a non-extendable set of rules indicating the same target cell is applied. With the set maximally parallel derivation mode, computational completeness can already be obtained with tissue P systems having a tree structure, whereas tissue P systems even with an arbitrary communication structure are not computationally complete when working in the sequential mode. Adding polarizations — only the three polarizations − 1, 0, 1 are sufficient — allows for obtaining computational completeness even for tissue P systems working in the sequential mode. }
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.},
