Tatsuya Sekiguchi

Kinetic modeling and sensitivity analysis of xylose metabolism in Lactococcus lactis IO-1

We proposed a kinetic simulation model of xylose metabolism in Lactococcus lactis IO-1 that describes the dynamic behavior of metabolites using the simulator WinBEST-KIT. This model was developed by comparing the experimental time-course data of metabolites in batch cultures grown in media with initial xylose concentrations of 20.3-57.8 g/l with corresponding calculated data. By introducing the terms of substrate activation, substrate inhibition, and product inhibition, the revised model showed a squared correlation coefficient (r2) of 0.929 between the experimental time-course of metabolites and the calculated data. Thus, the revised model is assumed to be one of the best candidates for kinetic simulation describing the dynamic behavior of metabolites. Sensitivity analysis revealed that pyruvate flux distribution is important for higher lactate production. To confirm the validity of our kinetic model, the results of the sensitivity analysis were compared with enzyme activities observed during increasing lactate production by adding natural rubber serum powder to the xylose medium. The experimental results on pyruvate flux distribution were consistent with the prediction by sensitivity analysis.

WinBEST-KIT: WINDOWS-BASED BIOCHEMICAL REACTION SIMULATOR FOR METABOLIC PATHWAYS

We have implemented an efficient, user-friendly biochemical reaction simulator called Web-based BEST-KIT (Biochemical Engineering System analyzing Tool-KIT) for analyzing large-scale nonlinear networks such as metabolic pathways. Users can easily design and analyze an arbitrary reaction scheme through the Internet and an efficient graphical user interface without considering the mathematical equations. The reaction scheme can include several reaction types, which are represented by both the mass action law (mass balance) and approximated velocity functions of enzyme kinetics at steady state, such as Michaelis-Menten, Hill cooperative, Competitive inhibition. However, since all modules in Web-based BEST-KIT have been developed in Java applet style, users cannot optionally make use of original mathematical equations in addition to the prepared equations. In the present study, we have developed a new version of BEST-KIT (for Microsoft Windows called WinBEST-KIT) to allow users to define original mathematical equations and to customize these equations very easily as user-defined reaction symbols. The following powerful system-analytical methods are prepared for system analysis: time-course calculation, parameter scanning, estimation of the values of unknown kinetic parameters based on experimentally observed time-course data of reactants, dynamic response of reactants against virtual external perturbations, and real-time simulation (Virtual Dry Lab).

WinBEST-KIT for analyzing multilayer and multicellular systems

Previously, we developed a biochemical reaction simulator called WinBEST-KIT (Biochemical Engineering System analyzing Tool-KIT, which runs under Microsoft Windows) for analyzing complicated metabolic pathways. WinBEST-KIT provides an integrated simulation environment for experimental researchers in metabolic engineering. A particularly notable feature of WinBEST-KIT is that users can easily define and customize reaction symbols in the graphical user interface. Users can use their original kinetic equations, in addition to the pre-installed standard kinetic equations, to represent unknown kinetic mechanisms as reaction steps. However, owing to the increasing size of reaction systems to be analyzed in metabolic pathways, large-scale reaction systems must be divided into several arbitrary compartmental reaction systems and procedures are needed, such as multilayered hierarchical representation, to describe the interactions between the compartmental reaction systems. Accordingly, in this study, we developed a new version of WinBEST-KIT that enables users to construct several arbitrary reaction schemes as layers, to connect the layers, and to analyze the interactions between them. This hierarchical representation is effective for constructing multilayered mathematical models of biochemical systems, such as genome-enzyme-metabolite systems, reaction cascade systems, and multicellular systems.