2Path: A terpenoid metabolic network modeled as graph database

Abstract

Terpenoids are involved in interactions as signaling for communication intra/inter species, signal molecules to attract pollinating insects, and defense against herbivores and microbes. Due to their chemical composition, many terpenoids possess vast pharmacological applicability in medicine and biotechnology, besides important roles in ecology, industry and commerce. The biosynthesis of terpenes has been widely studied over the years, and it is well known that they can be synthesized from two metabolic pathways: mevalonate pathway (MVA) and non-mevalonate pathway (MEP). On the other hand, genome-scale reconstruction of metabolic networks faces many challenges, including organizational data storage and data modeling, to properly represent the complexity of systems biology. Recent NoSQL database paradigms have introduced new concepts of scalable storage and data queries. Among them graph databases, which are versatile enough to cope with biological data. In this paper, we propose 2Path, a graph database model designed to represent terpenoid metabolic networks, with thousands of secondary metabolism reactions, such that it preserves important terpenoid biosynthesis characteristics.