This study provides a novel conservation optimization method to estimate local intracellular metabolite concentrations in cardiac tissue, which is essential for accurate modeling of cellular metabolism.
Systems for describing myocardial cellular metabolism with appropriate thermodynamic constraints on reactions have to be on the basis of estimates of intracellular and mitochondrial concentrations of metabolites as driving forces for reactions. This requires that tissue composition itself must be modeled, but there is marked inconsistency in the literature and no full data set on hearts of any species. To formulate a self-consistent set of information on the densities, contents, or concentrations of chemical components and volumes of tissue spaces, we drew on information mostly on rats. From the data on densities, volumes, volume fractions, and mass fractions observed mainly on left ventricular myocardium, cytoplasm, and mitochondria and from morphometric data on cellular components and the vasculature, we constructed a matrix based on conservation laws for density, volume, and constituent composition. The four constituents were water, protein, fat, and solutes (or ash). To take into account the variances in the observed data sets, we used a constrained nonlinear least squares optimization to minimize the differences between the final results and the data sets. The results provide a detailed estimate of cardiac tissue composition, previously unavailable, for the translation of whole tissue concentrations or concentrations per gram protein into estimated local concentrations that are relevant to reaction processes. An example is that the concentrations of phosphocreatine and ATP in cytosolic water space are twice as high as their mean tissue concentrations. This conservation optimization method is applicable to any tissue or organ.
Vinnakota et al. (Thu,) studied this question.