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Mitochondria are dynamic organelles that integrate bioenergetics, biosynthesis, and signaling in cells and regulate redox homeostasis, apoptotic pathways, and cell proliferation and differentiation. Depending on the environmental conditions, the mitochondrial morphology dynamically changes to match the energy demands. The mitochondrial dynamics is related to the initiation, migration, and invasion of diverse human cancers and thus affects cancer metastasis, metabolism, drug resistance, and cancer stem cell survival. We reviewed the current image-based analytical tools and machine-learning techniques for phenotyping mitochondrial morphology in different cancer cell lines from confocal microscopy images. We listed and applied pipelines and packages available in ImageJ/Fiji, CellProfiler, MATLAB, Java, and Python for the analysis of fluorescently labeled mitochondria in microscopy images and compared their performance, usability and applications. Furthermore, we discussed the potential of automatic mitochondrial segmentation, classification and prediction of mitochondrial abnormalities using machine learning techniques. Quantification of the mitochondrial morphology provides potential indicators for identifying metabolic changes and drug responses in cancer cells.

Cellular bioenergetics and mitochondrial dynamics are crucial for the secretion of insulin by pancreatic beta cells in response to elevated blood glucose concentrations. To obtain better insights into the interactions between energy production and mitochondrial fission/fusion dynamics, we combine live-cell mitochondria imaging with biophysical-based modeling and network analysis to elucidate the principle regulating mitochondrial morphology to match metabolic demand in pancreatic beta cells. A minimalistic differential equation-based model for beta cells was constructed to include glycolysis, oxidative phosphorylation, simple calcium dynamics, and graph-based fission/fusion dynamics controlled by ATP synthase flux and proton leak flux. The model revealed that mitochondrial fission occurs in response to hyperglycemia, starvation, ATP synthase inhibition, uncoupling, and diabetic condition, in which the rate of proton leak exceeds the rate of mitochondrial ATP synthesis. Under these metabolic challenges, the propensities of tip-to-tip fusion events simulated from the microscopic images of the mitochondrial networks were lower than those in the control group and prevented mitochondrial network formation. The modeling and network analysis could serve as the basis for further detailed research on the mechanisms of bioenergetics and mitochondrial dynamics coupling.

Oxidative phosphorylation (OXPHOS) is an oxygen-dependent process that consumes catabolized nutrients to produce adenosine triphosphate (ATP) to drive energy-dependent biological processes such as excitation-contraction coupling in cardiomyocytes. In addition to in vivo and in vitro experiments, in silico models are valuable for investigating the underlying mechanisms of OXPHOS and predicting its consequences in both physiological and pathological conditions. Here, we compare several prominent kinetic models of OXPHOS in cardiomyocytes. We examine how their mathematical expressions were derived, how their parameters were obtained, the conditions of their experimental counterparts, and the predictions they generated. We aim to explore the general landscape of energy production mechanisms in cardiomyocytes for future in silico models.

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