(3) The CellOrganizer project: An open source system to learn image-derived models of subcellular organization over time and space

Abstract

The CellOrganizer project (http://cellorganizer.org) provides open source tools for learning generative models of cell organization directly from images and for synthesizing cell images (or other representations) from one or more of those models. Model learning captures variation among cells in a collection of images. Images used for model learning and instances synthesized from models can be two- or three-dimensional static images or movies. Current components of CellOrganizer can learn models of cell shape, nuclear shape, chromatin texture, vesicular organe lie number, size, shape and position, and microtubule distribution. These models can be conditional upon each other: for example, for a given synthesized cell instance, organelle position will be dependent upon the cell and nuclear shape of that instance. The models can be parametric, in which a choice is made about an explicit form to represent a particular structure, or non-parametric, in which distributions are learned empirically. One of the main uses of the system is in support of cell simulations: models learned from separate experiments can be combined into one or more synthetic cell instances that are output in a form compatible with cell simulation engines such as MCell, Virtual Cell and Smoldyn. Another important application of the system is in comparison of target patterns and perturbagen effects in high content screening and analysis. This is currently done using numerical features, but these are difficult to compare across different microscope systems or cell types since features can be affected by changes in more than one aspect of cell organization. More robust comparisons can be made using generative model parameters, since these can distinguish effects on cell size or shape from effects on organelle pattern. Ultimately, it is anticipated that collaborative efforts by many groups will enable creation of image-derived generative models that permit accurate modeling of cell behaviors, and that can be used to drive experimentation to improve them through active learning. [replace "perturbation" for the word "perturbagen"]