Network controllability solutions for computational drug repurposing using genetic algorithms

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

doi:10.1038/s41598-022-05335-3

Abstract

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.

Highlights

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine
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
Of the related drug-targets, we found seventeen that are targeting the inputs identified only by the genetic algorithm, with seven under research for the treatment of ovarian cancer

Summary

Introduction

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 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. Network modeling in systems medicine has emerged as a powerful analytics approach in the last couple of

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Conclusion