Parametric model-based simulation approaches enable flexibly generating images for specific purposes. Parametric modeling allows incorporating prior knowledge of the physical properties of the image acquisition device and of the underlying biological phenomenon and objects into the simulation system. The simulation process is controlled by a set of model parameters, which allow generating synthetic images with full control on the outcome. Parametric models have been introduced in various areas of biomedical image simulation and object synthesis, ranging from modeling of different imaging and measurement modalities to objects in various scales and dimensions, such as cells and organelles, populations, and tissues. The control of the simulation process through parameters enables simulating various conditions, making validation of image analysis algorithms and tools with simulated images an appealing alternative for manual annotation-based validation. We introduce parametric model-based simulation approaches for generating synthetic cell images, covering the modeling of shape, appearance, spatial distribution, and image acquisition system.