Studying the nonlinearities of T-cell leukemia growth and the underlying metabolism upon glucocorticoid treatment through the application of dynamic mathematical methodologies

Glucocorticoids are considered to be indispensable agents in the treatment of hematologic malignancies. A critical established glucocorticoid action is the apoptotic effect they exert on leukemic cells. However, little is known about the molecular response of malignant cells on glucocorticoid exposure. Even less is known about the cell proliferation dynamics governing leukemic cells under glucocorticoid influence. Dynamic parameters of the cell population state like growth rate or its time derivative are largely overlooked in cell population studies. In the present work a quantitative mathematical and modeling approach is endeavored regarding growth and metabolic dynamics. Cell populations and metabolic factors, such as glucose, lactate and lactate dehydrogenase (LDH) are measured. Growth and metabolic features are assumed to be of nonlinear nature. A model based prediction of glucocorticoid effects is derived by applying a nonlinear fitting approximation to the measured parameters.