Stochastic Simulation Of Excitation-contraction Coupling

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SUMMARY OF WORK As a result of extensive collaboration with Clara Franzini-Armstrong we have obtained extensive statistical data on the distribution of organelles and ryanodine receptors in rabbit siono-atrial node cells. These data indicated that the parameters of our 3D stochastic SANC model need to be extensively revised. However, the EM data are not sufficient to define the critical distribution of ryanodine receptors on the cell surface. We have done extensive imaging using ultra-resolution SIM microscopy, and have developed software that enables 3D reconstruction of the location and size of ryanodine receptor clusters, which will be used directly in the model. We are currently developing software that will use 1000 processors to model the shape and Ryr distribution of individual cells. We have developed software that can detect, classify and track calcium release event in 3D+time, both in simulations and in experimental records. This has led to new understanding of the way that propagation occurs in the model as a function of adrenergic stimulation, and to the discovery that there are many more release events in experimental records than previously suspected. We have begun studies of heterogeneity of cells within the sinus node, both in isolated cells and in high space and time resolution images of whole sinus node preparations from mouse. We have extended the 3D stochastic model to multiple, interacting cells. In the next program period we will attempt to model the way that heterogeneous interacting cells give rise to the heart rhythm as an emergent property. We have also initiated a new study that applies statistical physics methods from solid-state theory to the interactions of clustered ryanodine receptors. This has shown that the process of EC coupling involves two different phase transitions that can be modeled analytically. As advised by the Board of Scientific Counselors, we are undertaking to translate our extensive modeling software into a form that can be used by other investigators. This has been complicated because the model software in written in the computer algebra language Macsyma whose commercial form is no longer available. To solve this problem we undertook a 4 month project in collaboration with some of the original developers of the language to upgrade the free, open-source version (called Maxima for copyright reasons) so that it can process our modeling software. This upgrade has now been incorporated in the latest version of Maxima (Sourceforge.com) so that our modeling suite can now be published and used by others. However, during the past 6 months this work has been interrupted by the Covid-19 pandemic. Taking advantage of the fact that spread of an epidemic has a mathematical structure almost identical to that of the calcium-induced calcium release we have been studying for decades, we have repurposed our mathematical and computational tools to develop a model of Covid-19 epidemiology. This work addressed the hypothesis that the complex patterns of propagation being observed in real time could be explained by the effect of social heterogeneity not generally accounted for in many forecasting models. We have developed a general epidemic simulation tool that can represent a population by a large number of sub-populations, some representing social rather than geographic units, and connected by a network of interactions specified by the user. This model is fully stochastic and incorporates the effects of super-spreading. We find that vulnerable subgroups unable to socially-distance can drive the epidemic and lead to complex and unpredictable patterns of spread that can actualy defeat the efforts of social distancing in the main population. This work has been submitted for publication and published in a pre-print on MedRxiv.