In Silico Approach to Find an Optimal Strategy in Selective Targeting of Cancer Cells

Abstract

Cancer chemoresistance (including adaptive resistance) has emerged as a barrier in developing successful chemotherapeutic strategies. We use Monte Carlo simulation based single cell analysis to provide insights into the regulatory mechanisms for generating chemoresistance under TRAIL (death ligand) induction. Based on stochastic computer simulations we elucidate systems biology of cancer cell apoptosis (at the level of single cells) and search for an optimal death ligand from a group of recently studied TRAIL affinity variants. In addition to assessing the population level behavior in cell death activation under induction of TRAIL/TRAIL-variant, Monte Carlo approach allows us to analyze cell-to-cell stochastic fluctuations in time-to-death that has implications for generating resistant cancer cells. We discuss application of Monte Carlo simulations in the context of developing more personalized approaches in treating various cancers. Initial findings indicate single cell in silico approaches can be utilized for disease subtype classification and in characterizing a given tumor, and, for finding an optimal strategy (such as network modules to target and ligands needed) in targeting a given tumor.