Computational Modeling of Cardiac Metabolism in Atrial Myocytes

Funebi Francis Ijebu1, Qince Li1, Kuanquan Wang1, Haibo Sui1, Lufang Zhou2, Yong Feng1, Henggui Zhang3
1Harbin Institute of Technology, 2School of Medicine, University of Alabama at Birmingham, 3University of Manchester


Aim: Atrial metabolic reactions are central to proper functioning of the heart which ensures continuation of life. Though several cardiovascular disease conditions are linked to atrial metabolic dysfunction, available experimental techniques have not been able to quantitatively elucidate such dysfunctions. Hence, a computational model elucidating effects of cytosolic metabolic processes on cardiac metabolism and excitation-contraction coupling of an atrial cell is presented in this study.

Methods: Kinetic and thermodynamic rate laws were applied to describe Michaelis-Menten dynamics of metabolites in glycolysis and tricarboxylic acid cycle. Our model links cytosolic to mitochondrial metabolism by passive diffusion and carrier mediated transport using law of mass action, in adherence to selective permeability of the mitochondrial membrane.

Results: Simulation results for metabolic substrates in both compartments under control conditions showed stable dynamics for excitation-contraction coupling of the heart. Our model simulated metabolites and substrates responses to modulated cardiac workload using protocol of dynamic abrupt switch of cardiac pacing from low frequency of 0.25Hz to high frequency of 2.0Hz. Results showed glucose concentration increased ~2 folds, while other cytosolic substrates decreased by different magnitudes in response to the switch. We also observed that mitochondrial NADH dynamics was similar to glucose under both workloads, and while some substrates concentration showed a monophasic increase (e.g. Succinyl-CoA, Succinate and Fumarate); others showed biphasic decreases (e.g. Citrate, Isocitrate, α-ketoglutarate, Malate and Oxaloacetate) with different magnitudes in response to the increase in cardiac energy demand.

Conclusion: Taken together, our results are consistent with previous experimental results on the glucose dynamics and theoretical submissions of substrate variation under different metabolic conditions. The ability of our model to simulate cardiac energy demand-supply balance under varied conditions confirms its strength which can further help to explore mechanisms of the pathology of diseases resulting from metabolic dysfunction and discover the possibility of therapeutic intervention.