Controllability in Multiplex Biological Networks withInsights into Virus-Related Diseases

Complex networks, inspired by biological systems and grounded in mathematical and computational models, have beenextensively employed to represent diverse biological phenomena. The significance of network controllability in comprehendingintricate biological systems is universally acknowledged, leading to the development of several algorithms aimed at analyzingnetwork controllability. These algorithms serve the purpose of manipulating input signals to guide biological system dynamicstowards desired states. New studies have shown that there are complicated connections between things that are hard to showin simple networks, and this makes us question the idea that things are isolated. In response to these complexities, multiplexnetworks have emerged as robust constructs capable of accommodating and capturing multiple relationships simultaneouslywithin high-dimensional spaces. This research introduces a novel framework designed to regulate the behavior of biologicalmultiplex networks through the identification of pivotal driver nodes. This challenge is formulated as the identification ofminimum driver nodes, with the framework’s efficacy evaluated through its application to authentic biological multiplexes.Applied to three virus multiplexes, the framework underscores the potential for specific nodes to serve as targets for drugenrichment or as subjects for the investigation of intricate diseases. The implications of this research extend to the identificationof potential driver genes for a variety of virus-related diseases within the landscape of biological multiplex networks.