Alireza Khanteymoori1*, Soudeh Behrouzinia1 and Fariba Dehghanian2
Inspired by biological systems and grounded in mathematical and computational models, complex networks have been extensively employed to represent diverse biological phenomena. The significance of network controllability in comprehending intricate biological systems is universally acknowledged, leading to the development of several algorithms aimed at analyzing network controllability. These algorithms serve the purpose of manipulating input signals to guide biological system dynamics toward desired states. New studies in biological systems have shown that there are complicated connections between nodes that are hard to show in simple networks. In response to these complexities, multiplex networks have emerged as robust constructs capable of accommodating and capturing multiple relationships simultaneously simultaneously within high-dimensional spaces. This research introduces a framework designed to regulate the behavior of biological multiplex networks by identifying pivotal driver nodes. This framework is presented to identify minimum driver nodes that the efficacy of it evaluated through its application to authentic biological multiplexes. Applied to three virus multiplex networks, the framework underscores the potential for identified driver nodes to serve as targets for drug enrichment or as subjects for investigating intricate diseases. The implications of this research extend to identifying potential driver genes for various virus-related diseases within the landscape of biological multiplex networks.
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Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report
