Applied Microbiology and Biotechnology, Vol.98, No.5, 2279-2288, 2014
Impact of initial conditions on extant microbial kinetic parameter estimates: application to chlorinated ethene dehalorespiration
Monod kinetics are the foundation of mathematical models of many environmentally important biological processes, including the dehalorespiration of chlorinated ethene groundwater contaminants. The Monod parameters-q(max), the maximum specific substrate utilization rate, and K-S, the half-saturation constant-are typically estimated in batch assays, which are superficially simple to prepare and maintain. However, if initial conditions in batch assays are not chosen carefully, it is unlikely that the estimated parameter values will be meaningful because they do not reflect microbial activity in the environmental system of interest, and/or they are not mathematically identifiable. The estimation of q(max) and K-S values that are highly correlated undoubtedly contributes significantly to the wide range in reported parameter values and may undermine efforts to use mathematical models to demonstrate the occurrence of natural attenuation or predict the performance of engineered bioremediation approaches. In this study, a series of experimental and theoretical batch kinetic assays were conducted using the tetrachloroethene-respirer Desulfuromonas michiganensis to systematically evaluate the effects of initial batch assay conditions, expressed as the initial substrate (S-0)-to-initial biomass concentration (X-0) ratio (S-0/X-0) and the S-0/K-S ratio on parameter correlation. An iterative approach to obtain meaningful Monod parameter estimates was developed and validated using three different strains and can be broadly applied to a range of other substrates and populations. While the S-0/X-0 ratio is critical to obtaining kinetic parameter estimates that reflect in situ microbial activity, this study shows that optimization of the S-0/K-S ratio is key to minimizing Monod parameter correlation.
Keywords:Dehalorespiration;Monod kinetics;Identifiability analysis;Experimental design;Mathematical modeling;Tetrachloroethene (PCE)