Research
Ca2+ dynamics in the pancreatic islet
Functional networks in pancreatic islets
In biological systems, complex behavior can be described by networks, including brain neural networks, or the networks of interacting proteins. Recently it has been shown that network properties emerge in the islets of Langerhans. Within the networks there are cells which have disproportionately large number of functional links to other cells, and which control the dynamics of the rest of the cells. Network theory, combined with quantitative imaging techniques offer a powerful approach to interrogate connections between structure and function of complex systems. We apply network theory analysis to Ca2+ traces generated both from computational modeling and from multi-scale quantitative microscopy to understand and predict structural-functional connectivity in the pancreatic islets and disease pathogenesis.
Simulated 3D islet of beta cells
Computational modeling of the islet function
Cell electrophysiology or the coupled β-cell model we use was adapted from the published Cha–Noma single-cell model where the change of the membrane potential with time for each β-cell is related to the sum of individual ion currents through that cell's membrane. This model modified with addition of the coupling current between the cells to form the coupled islet allows to predict Ca2+ and insulin secretion dynamics of the β-cell islet in-silico. We use the model to test how removal of specific cell subpopulations effects the function of the rest of the sells, to predict functional changes under diabetic conditions (reduced cell-cell coupling) and more.
Quantifying patterns of contact-based interactions in insulitis
Immune-endocrine interactions in pre-diabetes
We study to which extent alpha-rich regions are contacted by immune cells due to chance vs due to non-random contact-based interactions. We are using antibody-labeled pancreatic slices from human donors and mice in healthy and diabetic conditions. Network analysis, AI-assisted image segmentation, and MATLAB algorithms are used to reveal dynamic patterns of diabetes development.