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 reactio

SUBMITTER: Cao S 

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

REPOSITORIES: biostudies-literature