Species-dependent adaptation of the cardiac Na+/K+ pump kinetics to the intracellular Na+ concentration

The Na(+)/K(+) ATPase (NKA) plays a critical role in maintaining ionic homeostasis and dynamic function in cardiac myocytes, within both the in vivo cell and in silico models. Physiological conditions differ significantly between mammalian species. However, most existing formulations of NKA used to simulate cardiac function in computational models are derived from a broad range of experimental sources spanning many animal species. The resultant inability of these models to discern species-specific features is a significant obstacle to achieving a detailed quantitative and comparative understanding of physiological behaviour in different biological contexts. Here we present a framework for characterising the steady-state NKA current using a biophysical mechanistic model specifically designed to provide a mechanistic explanation of the NKA flux supported by self-consistent species-specific data. We thus compared NKA kinetics specific to guinea- pig and rat ventricular myocytes. We observe that the apparent binding affinity for sodium in the rat is significantly lower, whereas the overall pump cycle rate is doubled, in comparison to the guinea pig. This sensitivity of NKA to its regulatory substrates compensates for the differences in Na(+) concentrations between the cell types. NKA is thereby maintained within its dynamic range over a wide range of pacing frequencies in these two species, despite significant disparities in sodium concentration. Hence, by replacing a conventional generic NKA model with our rat-specific NKA formula into a whole-cell simulation, we have, for the first time, been able to accurately reproduce the action potential duration and the steady-state sodium concentration as functions of pacing frequency

Weak localisation and the "Destruction" of the two-dimensional metallic behaviour by a parallel magnetic field

No abstract available

Balance of active, passive, and anatomical cardiac properties in doxorubicin-induced heart failure

Late-onset heart failure (HF) is a known side effect of doxorubicin chemotherapy. Typically, patients are diagnosed when already at an irreversible stage of HF, which allows few or no treatment options. Identifying the causes of compromised cardiac function in this patient group may improve early patient diagnosis and support treatment selection. To link doxorubicin-induced changes in cardiac cellular and tissue mechanical properties to overall cardiac function, we apply a multi-scale biophysical biomechanics model of the heart to measure the plausibility of changes in model parameters representing the passive, active, or anatomical properties of the left ventricle for reproducing measured patient phenotypes. We create representative models of healthy controls (N= 10) and patients with HF induced by (N= 22) or unrelated to (N= 25) doxorubicin therapy. The model predicts that HF in the absence of doxorubicin is characterized by a 2- to 3-fold stiffness increase, decreased tension (0-20%), and ventricular dilation (of order 10-30%). HF due to doxorubicin was similar but showed stronger bias toward reduced active contraction (10-30%) and less dilation (0-20%). We find that changes in active, passive, and anatomical properties all play a role in doxorubicin-induced cardiotoxicity phenotypes. Differences in parameter changes between patient groups are consistent with doxorubicin cardiotoxicity having a greater dependence on reduced cellular contraction and less anatomical remodeling than HF not caused by doxorubicin.</p

Weak localisation and the "Destruction" of the two-dimensional metallic behaviour by a parallel magnetic field

No abstract available

Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats

Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function (R2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’