Application of Gene Expression Data and Cellular Automata to Predict Disease Progress in a Cardiac Hypertrophy Model

Abstract

The discernment of gene expression changes at the molecular level presents great opportunities to conquer human disease; therefore it is important that these data find use in clinical practice. Here an example is given of how gene expression data may be used in the prediction of the severity of heart disease leading to cardiac hypertrophy (enlargement of heart muscle). Using a mathematical modeling approach based on cellular automata and available temporal expression gene profiles, the rate of development of hypertrophy in cardiomyocytes was studied. In the model, during 5% of the time transcriptional activation of genes resulted in significant increases in heart muscle mass. For hypertrophy originating in one or a few cells, the spread of the lesion occurs as a result of the intercellular transmission of information from the ‘seed’ cells to neighbors. Model output indicated that signal transmission time correlated with net increases in cardiomyocyte mass. There was a threshold signal time of 12 minutes, below which net mass increase was negligible. This implies that certain persons with a tendency to short transmission times would probably not suffer heart damage even while experiencing limited heart muscle growth, in the event of hemodynamic overload. We hypothesize that anti-hypertrophic drugs actually reduce the transcellular signal transmission time, resulting in reduced incidence/severity of heart disease.