A logic-based ordinary differential equation model of the myocyte mechanosignalling network correctly predicted 78% of independent experimental results and was robust to parameter changes.
Quantifying uncertainty in a cardiac myocyte mechanosignalling model showed robustness to parameter changes but sensitivity to reaction weights and logic structure.
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'.
Cao et al. (Sun,) conducted a other in Cardiac myocyte mechanosignalling. Monte Carlo and polynomial chaos expansion simulations was evaluated on Prediction accuracy of the model. A logic-based ordinary differential equation model of the myocyte mechanosignalling network correctly predicted 78% of independent experimental results and was robust to parameter changes.