Using fuzzy logic and a hybrid genetic algorithm for metabolic modeling

The identification of metabolic systems such as metabolic pathways, enzyme actions, and gene regulations is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ordinary differential equations have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that: (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete; and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model.