Computational models of biological processes are important building blocks in Systems Biology studies. Calibration and validation are two important steps for moving a mathematical model to a computational model. While calibration refers to finding numerical value of the coefficients such as rate constants in a mathematical model, validation refers to verifying that the calibrated model behaves the same as the biological system under previously unseen conditions such as environmental changes (e.g., drug treatment) or mutations. In lieu of direct measurements of rate constants, modeling of the molecular mechanisms that govern biological behaviors may be able to use dynamic expression profiles of reactant biomolecules for calibration. For validation, similar data, obtained under new conditions, are probably better than direct measurements of rate constants. In any case, direct measurement of rate constants is almost always impractical and difficult or impossible. Here, we show a computer-assisted methodology to extract embedded dynamic profiles of cell-cycle proteins from statically sampled, multivariate cytometry data guided by heuristics assembled from canonical cell-cycle knowledge. The methodology is implemented using standard “list mode” cytometry data-processing software followed by CytoSys – a software tool with an easy-to-use graphical interface. We demonstrate the use of CytoSys with a case study of exponentially growing, human erythroleukemia cells and extract the dynamic expression profiles of cyclin A for calibrating an existing deterministic mathematical model of the cell cycle.