Abstract
We applied the so-called chemical kinetics approach to complex bacterial growth patterns that were dependent on the liquid-surface-area-to-volume ratio (SA/V) of the bacterial cultures. The kinetic modeling was based on current experimental knowledge in terms of autocatalytic bacterial growth, its inhibition by the metabolite CO2, and the relief of inhibition through the physical escape of the inhibitor. The model quantitatively reproduces kinetic data of SA/V-dependent bacterial growth and can discriminate between differences in the growth dynamics of enteropathogenic E. coli, E. coli JM83, and Salmonella typhimurium on one hand and Vibrio cholerae on the other hand. Furthermore, the data fitting procedures allowed predictions about the velocities of the involved key processes and the potential behavior in an open-flow bacterial chemostat, revealing an oscillatory approach to the stationary states.
Highlights
Kinetic curves deliver the dynamic fingerprint of timeevolving systems
We applied the so-called chemical kinetics approach to complex bacterial growth patterns that were dependent on the liquidsurface-area-to-volume ratio (SA/V) of the bacterial cultures
The model quantitatively reproduces kinetic data of SA/V-dependent bacterial growth and can discriminate between differences in the growth dynamics of enteropathogenic E. coli, E. coli JM83, and Salmonella typhimurium on one hand and Vibrio cholerae on the other hand
Summary
Kinetic curves deliver the dynamic fingerprint of timeevolving systems. This applies to the study of complex chemical systems [1], has been used to portray ecological population dynamics [2], and is perhaps true for bacterial systems despite their tremendous microscopic complexity. Peleg and Corradini [15] recently discussed the benefits of the CKA and pointed out that the use of fixed order kinetics, as well as fixed stoichiometries in the CKA, could be a potential disadvantage in dealing with macroscopic and complex elements such as bacteria This is readily understood by considering, for instance, the interaction between one bacterium and one nutrient molecule leading to some change in the bacteria, that is, the chemical equivalent of the conversion of the bacteria into a product. Our modeling indicates that the SA/V-effect, as well as the particular kinetic curve shapes, is caused by a common mechanism for the different bacteria and that changes may occur when the global growth dynamics differ. The applied procedure permitted predictions of the possible growth behavior in a bacterial chemostat, shedding more light on the dynamic properties of SA/Vdependent bacterial growth
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