Parallel network motif search using message passing approach for biological complex networks

Abstract

Study of complex biological networks is essential for understanding their functional characteristics. Network motifs have functional significance in biological networks as they represent building blocks of these networks. This study evaluates master-worker parallelization approach on sequential PATRICIA trie based fast network motif search algorithm for distributed memory model based High Performance Clusters (HPCs). Proposed algorithm uses PATRICIA trie for data compression during census of subgraphs based upon ESU algorithm. Parallel implementation was done using MPI and C language. We applied proposed parallel algorithm to three real networks viz. networks of metabolic pathway of E.coli, electronic and social networks. PATRICIA based parallel approach was able to achieve speedup of 50.75, 49.37, 38.07 as analysed on 101 cores on networks of metabolic pathway of E.coli, electronic and social networks respectively for large motifs of size 9 for E.coli, social and 10 for electronic networks over the PATRICIA trie based sequential algorithm.