keywords = {network medicine, computational modelling, precision medicine, drug repurposing, centrality measures, systems controllability, graph theory.},
abstract = {Summary We discuss in this chapter several network modelling methods and their applicability to precision medicine. We review several network centrality methods (degree centrality, closeness centrality, eccentricity centrality, betweenness centrality, and eigenvector-based prestige) and two systems controllability methods (minimum dominating sets and network structural controllability). We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein–protein interactions (PPI) built around a specific patient's mutated genes, around the targets of the drugs used in the standard of care in multiple myeloma, and around multiple myeloma-specific essential genes. For each network, we demonstrate how the network methods we discuss can be used to identify personalized, targeted drug combinations uniquely suited to that patient.}
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.},
