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arxiv: 2605.11675 · v1 · submitted 2026-05-12 · 🧬 q-bio.QM · q-bio.NC

Recognition: no theorem link

Accounting for Missed Events in the Bayesian Modeling of IP3R Multimodal Gating

Audrey Denizot (AISTROSIGHT), Hugues Berry (AISTROSIGHT), Schayma Ben Marzougui (AISTROSIGHT)

Pith reviewed 2026-05-13 00:52 UTC · model grok-4.3

classification 🧬 q-bio.QM q-bio.NC
keywords IP3R gatingmissed eventsBayesian inferenceMarkov modelscalcium channelspatch clampmultimodal gatingkinetic modeling
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The pith

Integrating missed-event correction into Bayesian likelihood clarifies IP3R's Park and Drive gating modes as variants of a single 3-state Markov model.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that correcting for undetected short events in patch-clamp recordings of IP3R channels, by embedding the correction in the likelihood of hierarchical Markov models, leads to a simplified multimodal model upon Bayesian selection. In this model, both Park and Drive modes follow the same three-state chain but with parameters that stabilize different closed states depending on the mode. A sympathetic reader would care because such bias from missed events can distort inferred kinetics, affecting understanding of how these channels control calcium release in cells. The analysis further reveals that calcium concentrations around intermediate levels strongly inhibit transitions into the Park mode.

Core claim

By incorporating a correction for missed events directly into the likelihood function within a Bayesian framework for hierarchical Markov chain models, the selected model shows that IP3R gating operates in Park and Drive modes that each correspond to the same 3-state Markov model, but with mode-dependent rates: the Drive mode stabilizes the closed state connected to the open state, whereas the Park mode stabilizes the disconnected closed state. Intermediate calcium concentrations are shown to depress the transition rate from Drive to Park mode, restricting frequent Park mode activity to low or high calcium levels.

What carries the argument

Hierarchical Markov chain models with missed-event correction integrated directly into the Bayesian likelihood function for parameter inference and model selection.

Load-bearing premise

The specific functional form and hierarchical structure used to correct for missed events in the likelihood function accurately represent the underlying true gating kinetics without introducing new biases.

What would settle it

High-temporal-resolution recordings that detect all short events, when analyzed without correction, should yield parameters matching those from the corrected lower-resolution data if the method is accurate.

Figures

Figures reproduced from arXiv: 2605.11675 by Audrey Denizot (AISTROSIGHT), Hugues Berry (AISTROSIGHT), Schayma Ben Marzougui (AISTROSIGHT).

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Reference kinetic Markov models for IP3Rs proposed by Siekmann et al. [27]. Red circles correspond to open states, and blue ones to closed states. Arrows represent transition rates from one state to another. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of transition rates inferred from IP3R2 patch-clamp recordings using the classical MCMC method and our DCPROGS-based inference. Each box plot shows the transition rates estimated across all IP3R2 datasets at 10 μM IP3, 5 mM ATP, and various Ca2+ concentration (10, 50, 200, 1000, 5000, and 10000 nM) from Wagner & Yule [30]. Transition rate inference is shown for the Park mode in topology (a) (Fig… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of simulated single-channel IP3R currents obtained using transition rates inferred with the classical MCMC approach or our DCPROGS￾based method. Simulated traces are shown for topology (b) ( [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Posterior distributions and dependence on Ca2+ concentration of the inferred rates for the Park Mode. Posterior distributions of intra-modal rate constants using model (b) inferred using our DCPROGS-based approach are displayed at two represen￾tative calcium concentrations: 0.01 µM (A) and 10 µM (B). Full vertical lines locate median posterior values. (C) (Left) Model selected for the Park mode, model (b),… view at source ↗
Figure 6
Figure 6. Figure 6: Posterior distributions and dependence on Ca2+ concentration of the inferred rates for the Drive Mode. Posterior distributions of intra-modal rate constants using model (b) inferred using our DCPROGS-based approach are displayed at two represen￾tative calcium concentrations: 0.01 µM (A) and 10 µM (B). Full vertical lines locate median posterior values. (C) (Left) Model selected for the Drive mode, model (b… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of intra-modal transition rates in the Drive and Park modes as a function of Ca2+ concentration. This figure is a replot of Fig. 5C and Fig. 6C, providing a direct comparison of the impact of the modes on the inferred values of parameters q13 (A), q31 (B), q12 (C), and q21 (D). Transition rates inferred from topology (b) (Fig 2b) are depicted in pink and gray, for the Drive and Park modes, respe… view at source ↗
Figure 8
Figure 8. Figure 8: An IP3R2 Markov model that takes into account missed events. The IP3R channel can be in two modes, Park and Drive, that share the same topology but differ by the values of the intra-modal transition rates q P ij and q D ij . The intra-modal transition rates were inferred using our DCPROGS-based approach. The resulting values are presented in [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Inter-modal transition rates estimated with DCPROGS. Inferred inter-modal transition rates qPD (Park → Drive) and qDP (Drive → Park) for IP3R2 at different Ca2+ concentrations, 10 μM IP3, 5 mM ATP. Both Park and Drive modes were modelled using the intra-modal topology of [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Calcium dependence of the Drive-to-Park transition rate qDP. Inferred values of the Drive-to-Park transition rate qDP (red circles, mean ± s.d.) as a function of cytosolic Ca2+ concentration obtained from IP3R2 single-channel recordings at 10 μM IP3 and 5 mM ATP. The solid red line shows the fit of Eq. 3. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
read the original abstract

The Inositol 1,4,5-trisphosphate receptor channel (IP 3 R) 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 IP 3 R gating behavior using patch clamp data, with particular attention to missed events. We modeled IP 3 R 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 Ca 2+ concentrations are found to strongly depress the Drive to Park transition rate, so that the IP 3 R channel undergoes frequent transitions to the Park mode only for __ 50 nM or micromolar Ca 2+ concentrations. Overall, our approach provides a refined perspective on IP 3 R channel modeling and highlights the critical importance of accounting for missed events upon model selection based on single-channel recordings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript develops a Bayesian framework for inferring hierarchical Markov models of IP3R single-channel gating from patch-clamp recordings. It integrates an analytic correction for missed brief events (arising from finite temporal resolution) directly into the likelihood, then performs model selection across candidate topologies and parameterizations. The central result is that this correction yields a clarified multimodal description in which both the Park and Drive modes are realized by the same 3-state Markov chain, but with mode-specific rate constants: the Drive mode stabilizes the closed state adjacent to the open state while the Park mode stabilizes the disconnected closed state; in addition, intermediate Ca2+ concentrations strongly suppress the Drive-to-Park transition rate, restricting frequent Park-mode visits to sub-50 nM or micromolar Ca2+ regimes.

Significance. If the missed-event correction is shown to be unbiased, the work supplies a concrete, falsifiable 3-state mechanistic picture of IP3R modal gating together with a quantitative Ca2+ dependence that can be tested in future experiments. The explicit embedding of the correction inside the likelihood and the use of Bayesian model selection constitute a methodological advance that could be adopted for other channels where brief sojourns are routinely missed. The paper thereby strengthens the link between experimental bandwidth limitations and inferred kinetic schemes.

major comments (2)
  1. [Methods] Methods (likelihood construction): the functional form chosen for the missed-event probability (typically an approximation based on filter dead-time and noise) is inserted into the likelihood without accompanying simulation recovery tests on synthetic data generated from the final 3-state model; because the abstract states that the multimodal clarification appears only after the correction, the absence of such ground-truth validation leaves open the possibility that the reported topology and mode-specific stabilizations are artifacts of the correction rather than features of the data.
  2. [Results] Results (model selection): no table or figure directly compares posterior model probabilities or Bayes factors obtained with versus without the missed-event term; the claim that accounting for missed events 'deeply clarifies' the 3-state description therefore rests on an implicit before-after contrast that is not quantified, weakening the assertion that the correction is the decisive factor.
minor comments (3)
  1. [Abstract] Abstract: the phrase 'for __ 50 nM' is a clear typesetting placeholder and should be replaced by the intended inequality (e.g., '< 50 nM').
  2. [Throughout] Notation: ensure that state labels (C1, C2, O) and rate symbols are defined once and used consistently in both text and any supplementary equations.
  3. [Figures] Figures: if dwell-time histograms or posterior predictive checks are shown, they should be accompanied by the corresponding uncorrected versions to allow visual assessment of the correction's effect.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major point below and describe the revisions we will make to strengthen the validation of the missed-event correction and the model-selection results.

read point-by-point responses

  1. Referee: [Methods] Methods (likelihood construction): the functional form chosen for the missed-event probability (typically an approximation based on filter dead-time and noise) is inserted into the likelihood without accompanying simulation recovery tests on synthetic data generated from the final 3-state model; because the abstract states that the multimodal clarification appears only after the correction, the absence of such ground-truth validation leaves open the possibility that the reported topology and mode-specific stabilizations are artifacts of the correction rather than features of the data.

    Authors: We agree that explicit recovery tests on synthetic data are needed to rule out artifacts. We will add a dedicated subsection to the Methods describing simulation-based validation: synthetic single-channel records will be generated from the final 3-state hierarchical Markov model using the inferred mode-specific rate constants, realistic filter dead-time and noise will be imposed to produce missed events, and the Bayesian procedure will then be reapplied to recover the original topology, mode assignments, and parameter values. These results will be presented in a new supplementary figure. revision: yes

  2. Referee: [Results] Results (model selection): no table or figure directly compares posterior model probabilities or Bayes factors obtained with versus without the missed-event term; the claim that accounting for missed events 'deeply clarifies' the 3-state description therefore rests on an implicit before-after contrast that is not quantified, weakening the assertion that the correction is the decisive factor.

    Authors: We acknowledge that an explicit side-by-side comparison is required to quantify the effect of the missed-event term. We will add a new table (or supplementary figure) that reports the posterior model probabilities and Bayes factors for the candidate topologies both with and without the missed-event probability in the likelihood. This will directly demonstrate the change in model preference and the stabilization of the 3-state description once the correction is included. revision: yes

Circularity Check

0 steps flagged

No significant circularity; model emerges from data-driven selection on corrected likelihood

full rationale

The paper integrates a missed-event correction into the likelihood for hierarchical Markov models and performs Bayesian model selection on patch-clamp data. The reported 3-state structure per mode, mode-specific stabilizations, and Ca2+-dependent transition suppression are outputs of that inference and comparison process. No equation or step reduces a claimed prediction or model feature to a quantity defined solely by the fitted parameters themselves, nor does any load-bearing premise collapse to a self-citation or ansatz smuggled from prior author work. The derivation remains self-contained against the experimental recordings and standard single-channel analysis techniques.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The claim rests on standard Markov assumptions for state transitions, a domain-specific model for missed events based on recording bandwidth, and data-fitted kinetic rates; no new entities are postulated.

free parameters (1)
  • mode-dependent transition rates
    All kinetic parameters are inferred from the patch-clamp data via the Bayesian procedure.
axioms (2)
  • standard math Channel gating obeys the Markov property (memoryless transitions between discrete states)
    Invoked by the use of hierarchical Markov chains.
  • domain assumption Missed events arise from finite temporal resolution and can be corrected by integrating over unobserved short sojourns in the likelihood
    Central to the Bayesian formulation described in the abstract.

pith-pipeline@v0.9.0 · 5619 in / 1571 out tokens · 64611 ms · 2026-05-13T00:52:33.143495+00:00 · methodology

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Reference graph

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