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Quantification of model and data uncertainty in a network analysis of cardiac myocyte mechanosignalling.

ABSTRACT: Cardiac myocytes transduce changes in mechanical loading into cellular responses via interacting cell signalling pathways. We previously reported a logic-based ordinary differential equation model of the myocyte mechanosignalling network that correctly predicts 78% of independent experimental results not used to formulate the original model. Here, we use Monte Carlo and polynomial chaos expansion simulations to examine the effects of uncertainty in parameter values, model logic and experimental validation data on the assessed accuracy of that model. The prediction accuracy of the model was robust to parameter changes over a wide range being least sensitive to uncertainty in time constants and most affected by uncertainty in reaction weights. Quantifying epistemic uncertainty in the reaction logic of the model showed that while replacing 'OR' with 'AND' reactions greatly reduced model accuracy, replacing 'AND' with 'OR' reactions was more likely to maintain or even improve accuracy. Finally, data uncertainty had a modest effect on assessment of model accuracy. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

SUBMITTER: Cao S

PROVIDER: S-EPMC7287329 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Quantification of model and data uncertainty in a network analysis of cardiac myocyte mechanosignalling.

Cao Shulin S Aboelkassem Yasser Y Wang Ariel A Valdez-Jasso Daniela D Saucerman Jeffrey J JJ Omens Jeffrey H JH McCulloch Andrew D AD

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 20200525 2173

Cardiac myocytes transduce changes in mechanical loading into cellular responses via interacting cell signalling pathways. We previously reported a logic-based ordinary differential equation model of the myocyte mechanosignalling network that correctly predicts 78% of independent experimental results not used to formulate the original model. Here, we use Monte Carlo and polynomial chaos expansion simulations to examine the effects of uncertainty in parameter values, model logic and experimental ...[more]

PMID: 32448062

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Project description:Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating computational models. UQ involves empirically determining the uncertainty in model inputs-typically resulting from natural variability or measurement error-and then calculating the resultant uncertainty in model outputs. SA involves calculating how uncertainty in model outputs can be apportioned to input uncertainty. Rigorous comprehensive UQ/SA provides confidence that model-based decisions are robust to underlying uncertainties. However, comprehensive UQ/SA is not currently feasible for whole heart models, due to numerous factors including model complexity and difficulty in measuring variability in the many parameters. Here, we present a significant step to developing a framework to overcome these limitations. We: (i) developed a novel action potential (AP) model of moderate complexity (six currents, seven variables, 36 parameters); (ii) prescribed input variability for all parameters (not empirically derived); (iii) used a single "hyper-parameter" to study increasing levels of parameter uncertainty; (iv) performed UQ and SA for a range of model-derived quantities with physiological relevance; and (v) present quantitative and qualitative ways to analyze different behaviors that occur under parameter uncertainty, including "model failure". This is the first time uncertainty in every parameter (including conductances, steady-state parameters, and time constant parameters) of every ionic current in a cardiac model has been studied. This approach allowed us to demonstrate that, for this model, the simulated AP is fully robust to low levels of parameter uncertainty - to our knowledge the first time this has been shown of any cardiac model. A range of dynamics was observed at larger parameter uncertainty (e.g., oscillatory dynamics); analysis revealed that five parameters were highly influential in these dynamics. Overall, we demonstrate feasibility of performing comprehensive UQ/SA for cardiac cell models and demonstrate how to assess robustness and overcome model failure when performing cardiac UQ analyses. The approach presented here represents an important and significant step toward the development of model-based clinical tools which are demonstrably robust to all underlying uncertainties and therefore more reliable in safety-critical decision-making.

Project description:Computational models of the human head are promising tools for estimating the impact-induced response of the brain, and thus play an important role in the prediction of traumatic brain injury. The basic constituents of these models (i.e., model geometry, material properties, and boundary conditions) are often associated with significant uncertainty and variability. As a result, uncertainty quantification (UQ), which involves quantification of the effect of this uncertainty and variability on the simulated response, becomes critical to ensure reliability of model predictions. Modern biofidelic head model simulations are associated with very high computational cost and high-dimensional inputs and outputs, which limits the applicability of traditional UQ methods on these systems. In this study, a two-stage, data-driven manifold learning-based framework is proposed for UQ of computational head models. This framework is demonstrated on a 2D subject-specific head model, where the goal is to quantify uncertainty in the simulated strain fields (i.e., output), given variability in the material properties of different brain substructures (i.e., input). In the first stage, a data-driven method based on multi-dimensional Gaussian kernel-density estimation and diffusion maps is used to generate realizations of the input random vector directly from the available data. Computational simulations of a small number of realizations provide input-output pairs for training data-driven surrogate models in the second stage. The surrogate models employ nonlinear dimensionality reduction using Grassmannian diffusion maps, Gaussian process regression to create a low-cost mapping between the input random vector and the reduced solution space, and geometric harmonics models for mapping between the reduced space and the Grassmann manifold. It is demonstrated that the surrogate models provide highly accurate approximations of the computational model while significantly reducing the computational cost. Monte Carlo simulations of the surrogate models are used for uncertainty propagation. UQ of the strain fields highlights significant spatial variation in model uncertainty, and reveals key differences in uncertainty among commonly used strain-based brain injury predictor variables.

Project description:RationaleLoss-of-function studies in cardiac myocytes (CMs) are currently limited by the need for appropriate conditional knockout alleles. The factors that regulate CM maturation are poorly understood. Previous studies on CM maturation have been confounded by heart dysfunction caused by whole organ gene inactivation.ObjectiveTo develop a new technical platform to rapidly characterize cell-autonomous gene function in postnatal murine CMs and apply it to identify genes that regulate transverse tubules (T-tubules), a hallmark of mature CMs.Methods and resultsWe developed CRISPR/Cas9/AAV9-based somatic mutagenesis, a platform in which AAV9 delivers tandem guide RNAs targeting a gene of interest and cardiac troponin-T promoter-driven Cre to RosaCas9GFP/Cas9GFP neonatal mice. When directed against junctophilin-2 (Jph2), a gene previously implicated in T-tubule maturation, we achieved efficient, rapid, and CM-specific JPH2 depletion. High-dose AAV9 ablated JPH2 in 64% CMs and caused lethal heart failure, whereas low-dose AAV9 ablated JPH2 in 22% CMs and preserved normal heart function. In the context of preserved heart function, CMs lacking JPH2 developed T-tubules that were nearly morphologically normal, indicating that JPH2 does not have a major, cell-autonomous role in T-tubule maturation. However, in hearts with severe dysfunction, both adeno-associated virus-transduced and nontransduced CMs exhibited T-tubule disruption, which was more severe in the transduced subset. These data indicate that cardiac dysfunction disrupts T-tubule structure and that JPH2 protects T-tubules in this context. We then used CRISPR/Cas9/AAV9-based somatic mutagenesis to screen 8 additional genes for required, cell-autonomous roles in T-tubule formation. We identified RYR2 (Ryanodine Receptor-2) as a novel, cell-autonomously required T-tubule maturation factor.ConclusionsCRISPR/Cas9/AAV9-based somatic mutagenesis is a powerful tool to study cell-autonomous gene functions. Genetic mosaics are invaluable to accurately define cell-autonomous gene function. JPH2 has a minor role in normal T-tubule maturation but is required to stabilize T-tubules in the failing heart. RYR2 is a novel T-tubule maturation factor.

Dataset Information

Quantification of model and data uncertainty in a network analysis of cardiac myocyte mechanosignalling.

Publications

Quantification of model and data uncertainty in a network analysis of cardiac myocyte mechanosignalling.

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