Metabolic pathways synthesis based on ant colony optimization

Abstract

A current challenge in bioinformatics is to discover how to transform particular compounds into specific products. Typically, the common approach is finding the sequence of reactions that relate the specified substrate (source) and product (target) using classical searching algorithms. However, those methods have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. In [1], we propose a novel ant colony-based algorithm for metabolic pathways synthesis. This algorithm, named Pheromone-Directed Seeker (PhDSeeker), is able to relate several compounds simultaneously by emulating the behavior of real ants while seeking a path between their colony and a source of food. The process is designed to ensure the availability of substrates for every reaction in the solution. Thus, ants explore the set of reactions on each iteration searching for possible pathways to link the compounds. After that, they share information about solutions found by each one and then perform a new search. This process is guided by a cost function that evaluates the availability of substrates, the connection between source and target, and the pathway size.