Network controllability solutions for computational drug repurposing using genetic algorithms
Popescu Victor-Bogdan; Kanhaiya Krishna; Czeizler Eugen; Petre Ion; Nastac Dumitru Iulian
Network controllability solutions for computational drug repurposing using genetic algorithms
Popescu Victor-Bogdan
Kanhaiya Krishna
Czeizler Eugen
Petre Ion
Nastac Dumitru Iulian
NATURE PORTFOLIO
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022081154027
https://urn.fi/URN:NBN:fi-fe2022081154027
Tiivistelmä
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.
Kokoelmat
- Rinnakkaistallenteet [19207]