A1 Refereed original research article in a scientific journal
Network controllability solutions for computational drug repurposing using genetic algorithms
Authors: Popescu Victor-Bogdan, Kanhaiya Krishna, Nastac Dumitru Iulian, Czeizler Eugen, Petre Ion
Publisher: NATURE PORTFOLIO
Publication year: 2022
Journal: Scientific Reports
Journal name in source: SCIENTIFIC REPORTS
Journal acronym: SCI REP-UK
Article number: 1437
Volume: 12
Number of pages: 16
ISSN: 2045-2322
eISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-022-05335-3
Web address : https://www.nature.com/articles/s41598-022-05335-3
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/174956773
Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdos-Renyi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.
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