Multiplex PCR is a key modality of nucleic acid amplification testing with growing applications in clinical diagnostics, especially in infectious diseases. Recent work has demonstrated that thermodynamic and kinetic information embedded in amplification curves (ACs) can be leveraged for target identification in the multiplex setting. This technology, named Amplification Curve Analysis (ACA), requires a mechanistic simulation tool linking biochemical design choices to AC features. We present DYNAMIC, an open-source Python implementation of a kinetic model acting as a digital twin of singleplex TaqMan PCR. Based on established kinetic and stoichiometric principles, DYNAMIC predicts fluorescence values over a wide range of experimental conditions. Key features include separate modeling of primer and probe annealing, a flexible 2-parameter thermal degradation model of Taq activity, and support for atypical regimes relevant to ACA, such as asymmetric primer concentrations. A global optimization algorithm identifies thermodynamic hyperparameters linking assay characteristics to AC features. In comparison with experimental data, DYNAMIC reproduces AC variations driven by changes in primer and probe concentrations, captures late-cycle efficiency loss from enzyme degradation, and yields realistic cycle threshold trends across orders of magnitude in input DNA. Tested against a dilution series of four previously published assays, the model robustly identifies key kinetic hyperparameters. Overall, DYNAMIC provides a mechanistic framework for predicting TaqMan PCR kinetics that can streamline assay development, reduce empirical optimization, and support the rational design of multiplex panels where target identification relies on AI-enabled classification of AC features.
Kreitmann et al. (Sun,) studied this question.