Network controllability solutions for computational drug repurposing using genetic algorithms

• Published in: Scientific reports 2022-01, Vol.12 (1), p.1437-16, Article 1437
• Main Authors: Popescu, Victor-Bogdan, Kanhaiya, Krishna, Năstac, Dumitru Iulian, Czeizler, Eugen, Petre, Ion
• Format: Article
• Language: English
• Subjects: 631/114/2408; 631/553/2702; 692/4028; 692/700/565/1436/1437; Algorithms; Antineoplastic Agents - therapeutic use; Breast Neoplasms - drug therapy; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Computational Biology - methods; Computer applications; Control theory; Drug Repositioning - methods; Female; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks - drug effects; Genetic algorithms; Humanities and Social Sciences; Humans; Molecular Targeted Therapy; multidisciplinary; Neoplasm Proteins - antagonists & inhibitors; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Ovarian Neoplasms - drug therapy; Ovarian Neoplasms - genetics; Ovarian Neoplasms - metabolism; Ovarian Neoplasms - pathology; Pancreatic Neoplasms - drug therapy; Pancreatic Neoplasms - genetics; Pancreatic Neoplasms - metabolism; Pancreatic Neoplasms - pathology; Prescription Drugs - therapeutic use; Protein interaction; Protein Interaction Maps - drug effects; Science; Science (multidisciplinary); Therapeutic targets
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-05335-3

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.