"Image-derived Spatiotemporal Models of Subcellular Organization, Differentiation and Perturbation"

Given the complexity of biological systems, machine-learning methods are critically needed for building systems models of cell and tissue behavior and for studying their perturbations. Such models require accurate information about the subcellular distributions of proteins, RNAs and other macromolecules in order to be able to capture and simulate their spatiotemporal dynamics. Microscope images provide the best source of this information, and we have developed tools to build generative models of cell organization directly from such images. Generative models are capable of producing new instances of a pattern that are expected to be drawn from the same underlying distribution as those it was trained with. Our open source system, Cell Organize (http://CellOrganizer.org), currently contains components that can build probabilistic generative models of cell, nuclear and organelle shape, organelle position, and microtubule distribution. These models capture heterogeneity within cell populations, and can be dependent upon each other and be combined to create new higher level models. The parameters of these models can be used as a highly interpretable basis for analyzing perturbations (e.g., induced by drug addition), and generative models of cell organization can be used as a framework for cell simulations to identify mechanisms underlying cell behavior. Results for analysis of systems ranging from neuronal differentiation to perturbation of plant protoplast organization will be presented.