We have implemented an efficient, user-friendly, and web-based "biosimulator" named BEST-KIT (Biochemical Engineering System analyzing Tool-KIT: http://www.best-kit.org) for analyzing large-scale nonlinear reaction networks such as metabolic pathways. The BEST-KIT mainly consists of a module, "MassAction++," that can construct and analyze a reaction scheme represented by both mass action law (mass balance) and approximated velocity functions of enzyme kinetics at steady state. This module was developed in Java applet style and can be carried out on "any" platform machine through a web browser. In this study, we developed the parameter-estimation module for MassAction++. This module can estimate the values of unknown kinetic parameters based on the experimentally observed time-course data of state variables. We adopted three optimization techniques, the modified Powell method, the genetic algorithm, and the Hybrid method, which incorporates the genetic algorithm into the modified Powell method. The user can use an appropriate method for each purpose.

We previously developed the biochemical reaction simulator WinBEST-KIT. In recent years, research interest has shifted from analysis of individual biochemical reactions to analysis of metabolic pathways as systems. These large-scale and complicated metabolic pathways can be considered as characteristic multi-layered structures, which, for convenience, are separated from whole biological systems according to their specific roles. These pathways include reactants having the same name but with unique stoichiometric coefficients arranged across many different places and connected between arbitrary layers. Accordingly, in this study, we have developed a new version of WinBEST-KIT that allows users (1) to utilize shortcut symbols that can be arranged with multiple reactants having the same name but with unique stoichiometric coefficients, thereby providing a layout that is similar to metabolic pathways depicted in biochemical textbooks; (2) to create layers that divide large-scale and complicated metabolic pathways according to their specific roles; (3) to connect the layers by using shortcut symbols; and (4) to analyze the interactions between these layers. These new and existing features allow users to create and analyze such multi-layered metabolic pathways efficiently. Furthermore, WinBEST-KIT supports SBML, making it possible for users to utilize these new and existing features to create and publish SBML models.