Tumor recurrence predictions based on a fully coupled space–time multiscale framework for modeling tumor growth may soon be possible, according to researchers at the University of Texas at San Antonio.

The team collaborated with colleagues at The University of Texas at Austin and the MD Anderson Cancer Center to create a novel algorithm that takes into account major biological events in the tissue and cells of the patient. In a paper published in Computer Methods in Applied Mechanics and Engineering, the researchers describe a framework that consists of a tissue scale model, a model of cellular activities, and a subcellular transduction signaling pathway model.

The researchers developed tissue models, cellular models, and subcellular models in this framework using partial differential equations for tissue growth. These formulas also were used for an agent-based model for cellular events. The team developed a model that employs ordinary differential equations for signaling transduction pathway as a network at subcellular scale.

The researchers hope that this approach can be adopted to help predict the morphologic instability and growth patterns of different cell phenotypes. The researchers also theorized that this model could be used to measure cell density in a tumor and aid in drug delivery assessment.