During the past two decades many groundbreaking technologies, including Green Fluorescent Protein (GFP)-tagging and super-resolution microscopy, emerged and allowed the visualization of protein dynamics and molecular interactions at different levels of spatial and temporal resolution. In the meantime, the automated quantification of microscopy images depicting moving biomolecules has become of major importance in cell biology since it offers a better understanding of fundamental mechanisms including membrane transport, cell signaling, cell division and motility. Consequently, dedicated image analysis methods have been developed to process challenging temporal series of 2D-3D images and to estimate individual trajectories of biomolecules. Nevertheless, the current tracking methods cannot provide global information about biomolecule trafficking. This motivated the development of simulation techniques able to generate realistic fluorescence microscopy image sequences depicting trafficking of small moving particles in interaction, with variable velocities within the cell. In this chapter, we describe a simulation approach based on the concept of Network Tomography (NT) which is generally used in network communications and transport to infer the main routes of communication between origins and destinations. The trafficking model, scaled down for microscopy, is combined with real 2D-3D image sequences to generate artificial videos depicting fluorescently tagged moving proteins within cells. Simulation in bioimaging is timely since it has become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms.