Dynamic Social Network Modeling of Diffuse Subcellular Morphologies

The use of fluorescence microscopy has catalyzed new insights into biological function, and spurred the development of quantitative models from rich biomedical image datasets. While image processing in some capacity is commonplace for extracting and modeling quantitative knowledge from biological systems at varying scales, general-purpose approaches for more advanced modeling are few. In particular, diffuse organellar morphologies, such as mitochondria or actin microtubules, have few if any established spatiotemporal modeling strategies, all but discarding critically important sources of signal from a biological system. Here, we discuss initial work into building spatiotemporal models of diffuse subcellular morphologies, using mitochondrial protein patterns of cervical epithelial (HeLa) cells. We leverage principles of graph theory and consider the diffuse mitochondrial patterns as a social network: a collection of vertices interconnected by weighted and directed edges, indicating spatial relationships. By studying the changing topology of the social networks over time, we gain a mechanistic understanding of the types of stresses imposed on the mitochondria by external stimuli, and can relate these effects in terms of graph theoretic quantities such as centrality, connectivity, and flow. We demonstrate how the mitochondrial pattern can be faithfully represented parametrically using a learned mixture of Gaussians, which is then perturbed to match the spatiotemporal evolution of the mitochondrial patterns over time. The learned Gaussian components can then be converted to graph Laplacians, formally defining a network, and the changes in the topology of the Laplacians can yield biologically-meaningful interpretations of the evolving morphology. We hope to leverage these preliminary results to implement a bioimaging toolbox, using existing open source packages in the scientific Python ecosystem (SciPy, NumPy, scikit-image, OpenCV), which builds dynamic social network models from time series fluorescence images of diffuse subcellular protein patterns. This will enable a direct quantitative comparison of network structure over time and between cells exposed to different conditions.