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Abstract

Data simulation and analysis bridge the gap between a mechanistic or kinetic model of a certain reaction and the corresponding experimental data. Since the temporal behavior of most dynamic systems is nonlinear and highly complex, it usually cannot be deduced intuitively. Although in some cases, kinetic models can be simplistic enough to allow for some intuitive insight, in most cases, specifically with nonlinear systems, the human brain is not adapted to compute the response of kinetic systems to perturbations of any kind. These perturbations could involve rapid mixing events or changes in equilibrium by pressure and temperature changes to obtain a response that could be recorded in time. Therefore, it is important to have tools for “what if” analysis to get a prediction of the expected behavior of systems and ways to analyze data ideally without being misguided. The availability of immense computing power, even with ordinary personal computers in combination with major advances in simulation and fitting programs, allows for nearly unlimited complexity of analysis. However, it is still the quality of the data and good human sense that are crucial for scientific success. Therefore, it is still important to understand the basic principles of data analysis (of course including its limitations) and to look under the hood of the computational machines. The following sections describe in hierarchical, but also mostly functional order, the modules that are relevant for data simulation and analysis.