On the Ca

Computers in Biology and Medicine(2023)

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摘要
Agonist-induced Ca 2+ signaling is essential for the regulation of many vital functions in endothelial cells (ECs). A broad range of stimuli elevate the cytosolic Ca 2+ concentration by promoting a pathway mediated by inositol 1,4,5 trisphosphate (IP3) which causes Ca 2+ release from intracellular stores. Despite its importance, there are very few studies focusing on the quantification of such dynamics in the vascular endothelium. Here, by using data from isolated ECs, we established a minimalistic modeling framework able to quantitatively capture the main features (averaged over a cell population) of the cytosolic Ca 2+ response to different IP3 stimulation levels. A suitable description of Ca 2+ -regulatory function of inositol 1,4,5 trisphosphate receptors (IP3Rs) and corresponding parameter space are identified by comparing the different model variants against experimental mean population data. The same approach is used to numerically assess the relevance of cytosolic Ca 2+ buffering, as well as Ca 2+ store IP3-sensitivity in the overall cell dynamics. The variability in the dynamics’ features observed across the population can be explained (at least in part) through variation of certain model parameters (such as buffering capacity or Ca 2+ store sensitivity to IP3). The results, in terms of experimental fitting and validation, support the proposed minimalistic model as a reference framework for the quantification of the EC Ca 2+ dynamics induced by IP3Rs activation. • The modeling framework can quantitatively capture and predict the main features of EC Ca 2+ response to IP 3 stimulation. • The parameter space for different EC model variants is defined. • The link between IP 3 Rs and Ca 2+ elevation is elucidated through comparison against experimental data. • The impact of different sub-cellular processes on IP 3 -induced Ca 2+ dynamics is outlined.
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关键词
Ca2+ dynamics,Endothelial cells,Inositol trisphosphate-sensitive store receptors,Vascular function,Quantitative biology,Data-driven modeling
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