A Bayesian framework for systems model refinement and selection of calcium signaling.

Calcium (Ca) is a crucial messenger that modulates contractile and electrophysiological processes in eukaryotic cells. Dysregulation of Ca-signaling influences these processes and is strongly associated with diseases such as cancer, immune disorders, and heart failure. Computational modeling of Ca dynamics offers valuable insights into these processes. However, traditional approaches often overlook the inherent heterogeneity within cell populations, including cell-to-cell variability and population-wide differences. To overcome these limitations, we developed and implemented an advanced statistical approach (a Bayesian inference framework using a hierarchical mixture architecture) specifically engineered to capture and model the diverse behaviors seen in fundamental calcium signaling pathways within cells. We applied this framework to myoblasts and to a HEK293 cell line expressing the cardiac proteins SERCA2a and RyR2. Using fluorescence microscopy, we monitored Ca dynamics in response to extracellular adenosine triphosphate, as well as spontaneous Ca release and uptake between cellular compartments. Our framework leverages the microscopy data to identify the most probable models and parameters that reproduce experimental observations, effectively distinguishing multiple clusters of cells with distinct kinetic behaviors. This approach provides deeper insights into the underlying biological processes and their variability across multiple populations of cells. Our findings demonstrate that this Bayesian method significantly improves our ability to create accurate computational models of Ca signaling by explicitly accounting for cellular differences. This, in turn, enhances our capacity to understand the complex regulatory networks that govern how cells use calcium signals.