Towards kinetic modeling of metabolic networks with incomplete parameters

Modeling is an important direction in systems biology. The target towards kinetic modeling for metabolic network is to develop a practical computational method which can handle incomplete parameters. In principle, we could start with a set of randomly chosen parameters; calculating fluxes and metabolites concentration and comparing with experiments; iterating until the best parameters are found. But the large parametric space may require billions of times of iterations. In order to overcome such a difficulty, we develop a method to obtain the structure of parametric space. We are able to discover the correlation between parameters and variables, which is helpful for us to estimate the possible value of parameters. Differ from previous method, the implicit relationship between parameter and variable are also provided directly by our method, which provides a potential for us to analyze the feature of metabolic network.