Hybrid static/dynamic algorithm

YUGl, K., NAKAYAMA, Y., TOMITA, M., A hybrid static/dynamic simulation algorithm Towards large-scale pathway simulation, in Proceedings of the 3" International Conference on Systems Biology (E. Aurell, J. Elf, J. Jeppsson, eds.). 2002, p. 235... 

For each reaction in the generated pathway, users can manually select the reactions to be represented by dynamic equations. InitiaUy, the GEM system would automatically search for static reactions based on monomer enzymes found in Brenda and Swiss-PROT databases. Based on the search results, the static part of the model is then generated using the hybrid dynamic/static simulation algorithm detailed below. Finally, the generated pathway model can be used for simulations in E-Ccll System. Users can then specify dynamic equations by selecting an appropriate reaction mechanism and input reaction parameters, or they can program their own set of reaction process description files. 

Figure 7.6 Hybrid dyiiamic/static simulation algorithm. Using flux-based methods, the algorithm allows dynamic models to be integrated with stoichiometry-based models.

Chiou, J.P. and Wang, F.S., 1999, Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process. Computers and Chemical Engineering, 23,1277. 

The next step is to analyze the hybrid model to determine whether the quality requirements are met. The hybrid model will be analyzed with respect to static performance, dynamic performance, complexity, interpretability and process independence. The complexity of the fuzzy models is mainly determined by the number of rales and the rale base. The clustering algorithm in combination with structure optimization provides a way to minimize fuzzy model complexity, which yielded acceptable results. 

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