Network controllability solutions for computational drug repurposing using genetic algorithms

: Popescu Victor-Bogdan, Kanhaiya Krishna, Nastac Dumitru Iulian, Czeizler Eugen, Petre Ion

Publisher: NATURE PORTFOLIO

: 2022

: Scientific Reports

: SCIENTIFIC REPORTS

: SCI REP-UK

: 1437

: 12

: 16

: 2045-2322

DOI: https://doi.org/10.1038/s41598-022-05335-3

: https://www.nature.com/articles/s41598-022-05335-3

: 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.

s41598-022-05335-3.pdf