This code implements a system for tracking of chromosomal GFP dots in yeast. There are two top-level GUIs. Particletrack2 identifies dots in the image stack and fits one- and two-gaussian models to every cell. It expects your image data to be a folder of TIFFs acquired by Micro-Manager. If your data is not in this format you will need to rename it so that it follows the Micro-Manager naming convention or replace MMparse and MMstruct with functions to load your data. Particletrack2 allows you to find dots in a number of images before starting the time consuming fitting process. After running the 'Find Dots' command in each image, you queue it for analysis with the 'Queue Dataset' button. You can then load a new set of images, find dots, and queue it. When all images are queued, 'Analyze queued' will begin the analysis. For a 60 time point movie with ~100 dots, the analysis takes about an hour. The results are saved in a file 'analysis.mat' in the image directories. Particleanalyze allows you to view the results of a Particletrack2 run. Copy the analysis.mat file into the matlab workspace and run Particleanalyze. Particleanalyze determines whether to use the one- or two-dot model for each cell at each timepoint, and fits an intensity model to the resulting intensity trace. The crossover and penalty parameters are used to determine which model it prefers - the fit of the two dot model must be at least crossover of the one dot fit model to be preferred (i.e. for crossover 0.95, the fit must be 0.95 of the one dot fit, or 5% better) and penalty sets the value of the penalty for switching models. Dots whose final modeled intensity is less than 30% of their initial intensity are flagged as 'curious' and highlighted in green. These can then be manually inspected and have any of the other flags assigned to them. The 'Show Models' button will display the raw image of the dots along with the corresponding modeled images; the 'Plot Intensity' button will plot the intensity of the one- and two-dot models, the intensity of the preferred model, and the resulting fit. 'Export Data' allows you to save the resulting data in the MATLAB workspace.