NETWORK TOMOGRAPHY FOR TRAFFICKING SIMULATION AND ANALYSIS IN FLUORESCENCE MICROSCOPY IMAGING

GFP-tagging and time-lapse fluorescence microscopy can be considered as investigation tools to observe molecular dynamics and interactions in live cells at both the microscopic and nanoscopic scales. Consequently, it is imperative to develop novel image analysis techniques able to quantify dynamics of biological processes observed in such image sequences. This motivates our present research effort which is to develop novel methods to extract information from nD data. In trafficking analysis, object tracking using conventional techniques can be very hard or impossible, especially when more than one hundred small and poorly distinguishable objects interact. However, determining the full trajectories of all the objects are not needed to monitor the cell activity. Indeed, estimating origin and destination regions of the objects of interest may be more relevant. In this paper, we propose an original approach to recover the origin and destination pairs from traffic information. Thus, we propose to consider the MEMBRANE TRAFFICKING as a road trafficking, and for the first time we exploit the recent advances in network tomography (NT) commonly used in network communication for biological trafficking analysis. This idea is demonstrated on realistic artificial image sequences for the Rab6 protein, a GTPase involved in the regulation of intracellular membrane trafficking