Application of fuzzy-logic models for metabolic control analysis.
A priori information or valuable qualitative knowledge can be incorporated explicitly to describe enzyme kinetics making use of fuzzy-logic models. Although restricted to linear relationships, it is shown that fuzzy-logic augmented models are not only able to capture non-linear features of enzyme kinetics but also allow the proper mathematical treatment of metabolic control analysis. The explicit incorporation of valuable qualitative knowledge is crucial, particularly when handling data estimated from in vivo kinetics studies, since this experimental information is scarce and usually contains measurement errors. Therefore, data-driven techniques, such as the one presented in this work, form a serious alternative to established kinetics approaches.
Franco-Lara
Ezequiel
E
Lehrstuhl für Bioverfahrenstechnik, Technische Universität München, Boltzmannstr. 15, 85748 Garching, Germany. E.Franco-Lara@lrz.tum.de
Weuster-Botz
Dirk
D
D000818:Animals; D003198:Computer Simulation; D004798:Enzymes / Q000378:metabolism; D017143:Fuzzy Logic; D006801:Humans; D008660:Metabolism; D008954:Models, Biological; D015233:Models, Statistical
D004798:Enzymes