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Nonlinear Estimation of Microbial and Enzyme Kinetic Parameters from Progress Curve Data
Author(s) -
Goudar Chetan T.,
Devlin John F.
Publication year - 2001
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.2175/106143001x139263
Subject(s) - nonlinear regression , kinetic energy , nonlinear system , robustness (evolution) , curve fitting , biological system , simplex , estimation theory , penicillium chrysogenum , mathematics , substrate (aquarium) , chemistry , regression analysis , algorithm , statistics , physics , biochemistry , biology , ecology , geometry , quantum mechanics , gene
A computer program (BIOKINFIT) was developed to estimate microbial and enzyme kinetic parameters from progress curve data by nonlinear regression. BIOKINFIT overcomes the limitations associated with parameter estimation in nonlinear equations that are implicit in the dependent variable by numerically approximating the substrate concentration in the integrated kinetic expressions. The simplex method was used to minimize the error between experimentally observed and theoretically predicted substrate depletion data. The robustness of this approach was initially verified using synthetic substrate depletion data that were characterized by either simple or relative errors of known magnitude. Subsequently, previously published data from three different experiments including the pyruvate kinase reaction, 2‐chlorophenol biodegradation, and glucose uptake by Penicillium chrysogenum were used to verify the utility of the present approach for kinetic parameter estimation. In all cases, experimental substrate depletion data were accurately described by the theoretical curves and kinetic parameter estimates obtained in this study using the simplex method were in close agreement with those reported previously. BIOKINFIT, available free of charge on request, offers a convenient and robust method of analyzing progress curve data in implicit kinetic expressions.