Michael Becka
Bayer AG, Biometry, Aprather Weg, 42096 Wuppertal, Germany
It is often assumed that all kinetics of a dynamic system are of first-order. This assumption allows for an easy mathematical handling. However, almost all biochemical processes are catalyzed or influenced by enzymes or proteins, where the velocity of a process depends on the amount of substrate in a non-linear way. A typical characteristic in the context of investigating kinetic patterns is that all information has to be derived from observing dynamic processes over time. In contrast to an observed time dependent concentration in a special compartment, the kinetic pattern describes the law behind or, in other words, the velocity of the kinetic processes which lead to this concentration. When the exact kinetic pattern becomes of interest, as for example in toxicological risk assessment, the problem is to characterize the non-linear kinetics and to estimate corresponding parameters. The interesting question then is how a process works at different concentrations of a substrate, whereas the observable data merely consist of the dynamic reaction of this process to single disturbances. In this situation, the change of velocity in relation to the concentration of substrate becomes of interest. A statistical approach using acceleration information and nonlinear regression was developed in order to investigate non-linear kinetic processes. This approach is based on characteristics of systems of mathematical first order differential equations and allows to detect and to characterize deviations from the usually made first-order kinetics assumption. It allows for population modeling using mixed models.
Reference: PAGE 9 () Abstr 102 [www.page-meeting.org/?abstract=102]
Poster: poster