Abstract
AbstractBackgroundModel validation depends on the agreement between the predicted and experimental data. However, finding solutions to problems, described by equations with many parameters, for which virtually nothing is known, is a difficult task. For example, the extraction of kinetic parameters from complex schemes representing the conversion of a substrate into a product by an enzyme in the presence of an inhibitor is extremely difficult, as even the orders of magnitude of the parameters are not known. This makes curve fitting very difficult in case of multidimensional and nonlinear data. This article presents a graphical user interface-based program employing a hybrid stochastic and deterministic approach, which allows for easy and reliable determination of model parameters.ResultsThe program has been extensively used in several laboratories at our institute and has proven to be efficient in determining model parameters in many different fields. Although its origins are related to kinetic studies in enzymology, it has been successfully tested on data from various sources, such as pharmacological studies of ligand−receptor binding, entomological studies of populations, bacterial growth, photosynthesis, toxicology, differential scanning calorimetry, isothermal titration calorimetry and nuclear magnetic resonance spectroscopy.ConclusionsThis program presents an effective solution for researchers facing the problem of extracting model parameters from multidimensional and nonlinear data where even the orders of magnitude of parameters are not known. Its graphical user interface makes it easy to use, does not require any programming skills, and it is cost-free. It is available for Windows and Linux platforms.
Publisher
Cold Spring Harbor Laboratory