title: Novel differential elimination method for determining kinetic coefficients under substrate self-inhibition.
abstract: A differential elimination method (DEM) is developed to determine the kinetic coefficients for substrate self-inhibition. Finite differentiation of the equation eliminates either K(I) or K(S), which enables the equation to be linearized so that q, K(S), and K(I) can be estimated without using nonlinear least square regression (NLSR). The DEM options that eliminate K(I) or K(S) computed the parameter values exactly when the data did not contain any errors. If one-point or random errors were not too large, both DEM options worked as well as NLSR when data were acquired with geometric intervals for substrate concentration. The DEM was more accurate for fitting the data for the smallest and largest values of S, but relatively weaker in estimating the observed maximum substrate utilization rate, q(max). The estimates for S(max), the concentration at which the maximum specific substrate utilization rate is observed, were relatively invariant among the methods, even when K(S) and K(I) differed. When the intervals were arithmetic (i.e., equal intervals of substrate concentration) and the data contained errors, the DEM and NLSR estimated the parameters poorly, indicating that collecting data with an arithmetic interval greatly increases the risk of poor parameter estimation. Parameter estimates by DEM fit very well experimental data from nitrification or photosynthesis, which were taken with geometric intervals of substrate concentration or light intensity, but fit poorly phenol-degradation data, which were obtained with arithmetic substrate intervals. Besides providing a reasonable substitute for NLSR, the DEM also can be used as a tool to diagnose the quality of experimental data by comparing its estimates between the DEM options, or, more rigorously, to those from NLSR.
authors.affiliation: Center for Environmental Biotechnology, Biodesign Institute at Arizona State University, Tempe, 85287-5701, USA. Seongjun.Park@asu.edu
authors.forename: Seongjun
authors.initials: S
authors.lastname: Park
authors.forename: Bruce E
authors.initials: BE
authors.lastname: Rittmann
authors.forename: Wookeun
authors.initials: W
authors.lastname: Bae
mesh_terms: D001419:Bacteria / Q000737:chemistry* / Q000201:enzymology* / Q000378:metabolism; D001426:Bacterial Proteins / Q000737:chemistry* / Q000378:metabolism; D007700:Kinetics; D016018:Least-Squares Analysis; D008432:Mathematical Computing; D008962:Models, Theoretical; D013379:Substrate Specificity
chemical_list: D001426:Bacterial Proteins