Comparing mechanism-based and machine learning models for predicting the effects of glucose accessibility on tumor cell proliferation

Abstract

Abstract Glucose plays a central role in tumor metabolism and development and is a target for novel therapeutics. To characterize the response of cancer cells to blockade of glucose uptake, we collected time-resolved microscopy data to track the growth of MDA-MB-231 breast cancer cells. We then developed a mechanism-based, mathematical model to predict how a glucose transporter (GLUT1) inhibitor (Cytochalasin B) influences the growth of the MDA-MB-231 cells by limiting access to glucose. The model includes a parameter describing dose dependent inhibition to quantify both the total glucose level in the system and the glucose level accessible to the tumor cells. Four common machine learning models were also used to predict tumor cell growth. Both the mechanism-based and machine learning models were trained and validated, and the prediction error was evaluated by the coefficient of determination (R 2). The random forest model provided the highest accuracy predicting cell dynamics (R 2 = 0.92), followed by the decision tree (R 2 = 0.89), k-nearest-neighbor regression (R 2 = 0.84), mechanism-based (R 2 = 0.77), and linear regression model (R 2 = 0.69). Thus, the mechanism-based model has a predictive capability comparable to machine learning models with the added benefit of elucidating biological mechanisms.