Dynamic Social Network Modeling of Diffuse Subcellular Morphologies
Andrew Durden
Allyson T Loy
Barbara Reaves
Mojtaba Fazli
Abigail Courtney
Frederick D Quinn
S Chakra Chennubhotla
Shannon P Quinn
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.
Biomedical Imaging, Graph Theory, Social Networks
DOI10.25080/Majora-4af1f417-000