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Identifying efficient controls of complex interaction networks using genetic algorithms

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Document pages: 34 pages

Abstract: Control theory has seen recently impactful applications in network science,especially in connections with applications in network medicine. A key topic ofresearch is that of finding minimal external interventions that offer controlover the dynamics of a given network, a problem known as networkcontrollability. We propose in this article a new solution for this problembased on genetic algorithms. We tailor our solution for applications incomputational drug repurposing, seeking to maximise its use of FDA-approveddrug targets in a given disease-specific protein-protein interaction network.We show how our algorithm identifies a number of potentially efficient drugsfor breast, ovarian, and pancreatic cancer. We demonstrate our algorithm onseveral benchmark networks from cancer medicine, social networks, electroniccircuits, and several random networks with their edges distributed according tothe Erdős-Rényi, the small-world, and the scale-free properties.Overall, we show that our new algorithm is more efficient in identifyingrelevant drug targets in a disease network, advancing the computationalsolutions needed for new therapeutic and drug repurposing approaches.

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