Abstract: The current invention describes systems, methods and apparatus for the combination of high-throughput flow imaging microscopy coupled with convolutional neural networks to analyze particles, such as aggregated biomolecules, and cells for use in in a variety of diagnostic, therapeutic and industrial applications.

Abstract: A method of tracking a plurality of tagged molecules in a cell in two or three dimensions may include receiving a plurality of unambiguous track segments, where each of the plurality of unambiguous track segments may include a plurality of time-valued observations of individual tagged molecules. The method may also include separating the plurality of unambiguous track segments into a plurality of time windows. The method may additionally include, for each of the plurality of unambiguous track segments, deriving one or more data sets representing features of the unambiguous track segment. The method may further include associating a first unambiguous track segment from a first time window with a second unambiguous track segment from a second time window using the one or more data sets.

Abstract: A method of tracking and inferring the underlying dynamics of a tagged molecule in a living cell may include receiving an ordered data set of time-valued location observations of the molecule, dividing the data set into time windows, and assigning a stochastic differential equation (SDE) model to each of the time windows with a set of parameters. The method may also include fitting the SDE models assigned to each of the plurality of time windows using likelihood-based techniques, and determining an initial value for each parameter. The method may further include fitting the set of parameters for each of the SDE models using a nonlinear maximum likelihood estimation search, applying an optimization routine to generate a set of computed parameters, determining whether the computed parameters are valid using goodness-of-fit tests, and determining whether each of the SDE models is valid based on the goodness-of-fit-tests.