Accounting for Missed Events in the Bayesian Modeling of IP3R2 Multimodal Gating

Abstract

The Inositol 1,4,5-trisphosphate receptor channel (IP3R) is an important calcium channel involved in calcium-induced calcium release, playing a prominent role in intracellular calcium signaling. However, accurately characterizing its gating behavior remains a challenge, particularly due to the temporal resolution of patch clamp techniques that is not large enough to detect all short-lived events. This limitation can significantly bias the inference of kinetic models describing the receptor activity. To address this issue, we focused on the quantitative analysis of IP3R2 gating behavior using patch clamp data, with particular attention to missed events. We modeled IP3R2 channel gating using Hierarchical Markov chains and used a Bayesian approach that integrates missed event correction directly into the likelihood function, enabling more accurate parameter inference and model evaluation. We show that accounting for missed events deeply clarifies the multi-modal model that emerges from model selection. In this new model, the Park and Drive modes both consist of the same 3-state Markov model, with mode-dependent kinetic parameters: the Drive mode stabilizes the closed state directly connected to the open one, whereas the Park mode stabilizes the other closed state, that is not connected to the open one. Intermediate Ca2+ concentrations are found to strongly depress the Drive to Park transition rate, so that the IP3R2 channel undergoes frequent transitions to the Park mode only for < 50 nM or micromolar Ca2+ concentrations. Overall, our approach provides a refined perspective on IP3R2 channel modeling and highlights the critical importance of accounting for missed events upon model selection based on single-channel recordings.