Simulating the time series of a selected gene expression profile in an agent-based tumor model

Abstract

To elucidate the role of environmental conditions in molecular-level dynamics and to study their impact on macroscopic brain tumor growth patterns, the expression of the genes Tenascin C and PCNA in a 2D agent-based model for the migratory trait is calibrated using experimental data from the literature, while the expression of these genes for the proliferative trait is obtained as the model output. Numerical results confirm that the gene expression of Tenascin C is consistently higher in the migratory glioma cell phenotype and show that the expression of PCNA is consistently higher among proliferating tumor cells. Furthermore, detrended fluctuation analysis (DFA) suggests that for prediction purposes, the simulated gene expression profiles of Tenascin C and PCNA that were determined separately for the migrating and proliferating phenotypes exhibit lesser predictability than those of the phenotypic mixture combining all viable tumor cells typically found in clinical biopsies.