Abstract Motivation: Construction of synthetic metabolic pathways promises sustainable production of diverse chemicals and materials. To design synthetic metabolic pathways of high value, computational methods are needed to expand present knowledge by mining comprehensive chemical and enzymatic information databases. Several computational methods have been already reported for the metabolic pathway design, but until now computation complexity has limited the diversity of chemical and enzymatic data used. Results: We introduce a computational platform, M-path, to explore synthetic metabolic pathways including putative enzymatic reactions and compounds. M-path is an iterative random algorithm, which makes efficient use of chemical and enzymatic databases to find potential synthetic metabolic pathways. M-path can readily control the search space and perform well compared with exhaustively enumerating possible pathways. A web-based pathway viewer is also developed to check extensive metabolic pathways with evaluation scores on the basis of chemical similarities. We further produce extensive synthetic metabolic pathways for a comprehensive set of alpha amino acids. The scalable nature of M-path enables us to calculate potential metabolic pathways for any given chemicals. Availability and implementation: The web tool and viewer are available for free at http://bp.scitec.kobe-u.ac.jp/m-path/aa/. Contact: [email protected] and [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

We improve network-structure-search evolutionary algorithm (NSS-EA) that is a search method for inference of genetic networks by S-system. Search methods for inference of genetic networks by S-system should meet the following requirements: 1) efficient search of a set of satisfactory structures; 2) search of structures satisfying biological knowledge; and 3) search of the true structure, NSS-EA is an excellent method from the viewpoints of Requirement 1 and 2. However, it has a problem from the viewpoint of Requirement 3. In order to solve this problem, first, we improve the parameter search process by using the time course data of disrupted strains as well as that of a wild type when evaluating genetic networks. Second, we propose four new structure-search operators taking account of mutual interactions among substances. We show the effectiveness of the proposed improvements for NSS-EA from the viewpoint of Requirement 3 by comparing the performance of the original NSS-EA and the improved NSS-EA on a five-substance benchmark problem.