A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning

Abstract

BackgroundDrug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning.ResultsDrug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases.ConclusionsWe find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0371-3) contains supplementary material, which is available to authorized users.

The Power of Many: A Physarum Swarm Steiner Tree Algorithm

“…The Steiner tree has been a topic of great interest to mathematicians and computer scientists since the 19th century [16]. It has many practical applications including cable routing, chip design, drug repositioning, and phylogenetic tree routing [8,[17][18][19][20].…”

“…The behavior of Physarum and the models it has inspired have found many different uses among which are drug repositioning, developing bio-computing chips, approximating highways layouts, and designing subway systems [2,[8][9][10]. In order to illustrate the operation of the Physarum Steiner Algorithm and demonstrate its applicability to real world problems, we consider the following:…”

“…Applications of Physarum include drug repositioning, building unconventional computer chips, approximating highways, and designing subway systems [2,[8][9][10]. In order to illustrate the novelty of the explore-and-fuse method as well as the benefits of its use, we dedicate Section IV to describing several different uses it has:…”

Spatio-Temporal Gene Discovery For Autism Spectrum Disorder

“…The prize-collecting version of the problem aims to find a tree that maximizes the sum of the prizes of the selected nodes while penalizing the total cost of the connecting edges. In the biology domain, PCST has proven useful for applications like detecting functional modules in PPIs (Dittrich et al, 2008), signaling pathway prediction (Huang and Fraenkel, 2009;Bailly-Bechet et al, 2011), metabolic pathway prediction (Faust et al, 2010) and drug repositioning (Sun et al, 2016). Prize-collecting Steiner forest (PCSF) problem is a relaxation of PCST such that multiple disconnected components (trees) are allowed.…”

Bioinformatics and systems biology research update from the 15th International Conference on Bioinformatics (InCoB2016)

“…Ezzat et al [21] have addressed this class imbalance problem using an ensemble learning approach to successfully predict several new drug-target interactions. Sun et al [22] have addressed the same challenge using a Physarum-inspired Prize-Collecting Steiner Tree algorithm, to build drug similarity networks, from which ten frequently occurring drug molecules have been reported as potential new cardiovascular therapeutic agents.…”