BPS2026 – Spatially explicit MCMC model of combined thin and thick filament cooperativity

Cardiac muscle contraction and relaxation at the sarcomeric level are driven by cooperative nearest neighbor recruitment in both the actin thin and myosin thick filaments. Previous computational modeling work has primarily focused on the strength of the mechanical propagation of motion between regulatory units on tropomyosin, a protein essential to thin filament cooperativity. Such models of actin-affiliated protein level behavior invariably capture the characteristic Hill-type force generation for varying pCa but do not simultaneously predict the Ca-sensitivity of the rate of force redevelopment following the slack and restretch protocol (ktr). In our work, we develop a spatially explicit Markov Chain Monte Carlo model that includes both thin and thick filament cooperativity. We use the previously defined tropomyosin energy landscape and the steady state ratios of super-relaxed (SRX), disordered-relaxed (DRX) and bound myosin distributions to construct a combined free-energy landscape and derive dynamic transition rates. Structurally, our model also incorporates updated tropomyosin azimuthal angles corresponding to blocked, closed, and open regulatory states. Additionally, our model implements a realistic distribution of MyBP-C in the cardiac-specific C zone and nearest-neighbor myosin recruitment along the thick filament. We show that the interdependent feedback between the thin and thick filament equilibrium states coupled with inhibitive MyBP-C interactions are sufficient to concurrently reproduce realistic force-pCa and force-ktr behavior. We first validate our model by fitting it to skinned fiber force development experiments in rat myocardium. Next, through numerical analysis, we tune myosin cooperative zonal coupling and MyBP-C interactions to uncover the inhibition, excitation, and saturation regimes of the force-ktr curve. Our model captures the experimentally expected SRX, DRX, and bound steady state distributions specific to the C and D zones. Finally, we use our model to probe potential underlying mechanisms of disease phenotypes.