The choice of freely available and commercial software for performing nonlinear regression and numerical integration and other tasks is a matter of personal taste, but any well-written package is expected to generate the same results with a given set of data. Here those I have used for publishing kinetic results, for producing all examples and calculations in this website, and in the book Kinetics of Enzyme-Modifier

• KAPattern allows the derivation of rate equations for enzymes and transporters. Written by Qi et al. and based on the King and Altman systematic method, it uses linear graph theory for generating directed graphs. The package can be freely downloaded for Windows applications and MATLAB.
• DynaFit (BioKin Ltd.) is a software package to fit kinetic models to experimental data, which can be equilibria or progress curves. It uses a combination of nonlinear regression and numerical integration.
• Maple can be used in kinetics for almost all purposes, provided the user is skilled in writing the algorithms. Very useful for the verification of algebraic expressions, calculation of limits, solution of equations, three-dimensional graphics and much more.
• MATLAB is useful for producing simulated data sets with/without added random scatter. For instance, the full Botts-Morales equation can be used for the simulation of steady-state data without any restriction (the simulated examples in this website have been produced in this way). Simulink is a graphical programming language that depends on MATLAB. Block libraries are used to represent symbolically and simulate time-dependent processes.
• KinTek Explorer, a creation of Kenneth Johnson, is dedicated to the simulation of kinetic processes by numerical integration. A graphical user interface allows scrolling the values of parameters and to display in real time  progress curves. For fitting models to experimental data (from single curves, to sets of curves and sets of different experiments), simulation is first performed by numerical integration. The progress curves are then compared with data to calculate the sum of squared deviations. This nonlinear regression is continued until reaching a minimum. But this minimum is not always sufficient! So, the FitSpace Explorer, integrated in the software, “calculates the dependence of the sum square error on each pair of parameters while allowing all remaining parameters to be adjusted in seeking the best fit“.
• Last but not least  I call attention to BestCurvFit, an economical package with solid statistics written by Frank Perrella. The library contains several models that embrace a broad range of kinetic applications, including all basic enzyme-modifier mechanisms. The user can also write in a simple way her/his own functions. The search for the best fit is achieved through “multiple nonlinear regression methods in tandem” and, in model discrimination, goodness of fit is ascertained by the smallest normalized Akaike information criterion.