Network controllability solutions for computational drug repurposing using genetic algorithms

Victor-Bogdan Popescu, Krishna Kanhaiya, Dumitru Iulian Năstac, Eugen Czeizler, Ion Petre*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
2 Downloads (Pure)


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 Erdős–Rényi, 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.

Original languageEnglish
Article number1437
JournalScientific Reports
Publication statusPublished - 2022
MoE publication typeA1 Journal article-refereed


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