Prediction of novel synthetic pathways for the production of desired chemicals.

BACKGROUND: There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism. RESULTS: In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates. CONCLUSIONS: It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks.

SUMMARY: WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.