A fully coupled space-time multiscale modeling framework for predicting tumor growth

Authored by Thomas E Yankeelov, Mohammad Mamunur Rahman, Yusheng Feng, J Tinsley Oden

Date Published: 2017

DOI: 10.1016/j.cma.2017.03.021

Sponsors: United States National Institutes of Health (NIH) United States Department of Energy (DOE) United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Most biological systems encountered in living organisms involve highly complex heterogeneous multi-component structures that exhibit different physical, chemical, and biological behavior at different spatial and temporal scales. The development of predictive mathematical and computational models of multiscale events in such systems is a major challenge in contemporary computational biomechanics, particularly the development of models of growing tumors in humans. The aim of this study is to develop a general framework for tumor growth prediction by considering major biological events at tissue, cellular, and subcellular scales. The key to developing such multiscale models is how to bridge spatial and temporal scales that range from 10(-3) to 10(3) mm in space and from 10(-6) to 10(7) sin time. In this paper, a fully coupled space time multiscale framework for modeling tumor growth is developed. The framework consists of a tissue scale model, a model of cellular activities, and a subcellular transduction signaling pathway model. The tissue, cellular, and subcellular models in this framework are solved using partial differential equations for tissue growth, agent-based model for cellular events, and ordinary differential equations for signaling transduction pathway as a network at subcellular scale. The model is calibrated using experimental observations. Moreover, this model is biologically-driven, from a signaling pathway, volumetrically-consistent between cellular and tissue scale in terms of tumor volume evolution in time, and a biophysically-sound tissue model that satisfies all conservation laws. The results show that the model is capable of predicting major characteristics of tumor growth such as the morphological instability, growth patterns of different cell phenotypes, compact regions of the higher cell density at the tumor region, and the reduction of growth rate due to drug delivery. The predicted treatment outcomes show a reduction in proliferation at different rates in response to different drug dosages. Moreover, the results of several 3D applications to tumor growth and the evolution of cellular and subcellular events are presented. Published by Elsevier B.V.

Tags

Simulation glioblastoma cancer Inhibition Biology Therapy Tissue Network modeling Cancer modeling Treatment outcome prediction Continuum mixture theory Signaling transduction pathway Bridging scale algorithm Mammalian target Rapamycin Gravity