Document Type
Thesis - University Access Only
Award Date
2025
Degree Name
Master of Science (MS)
Department / School
Mechanical Engineering
First Advisor
Saikat Basu
Abstract
Delivery and distribution of biofluids within solid tumors are complex processes influenced by the unique mechanical and physiological characteristics of the tumor microenvironment (TME). Understanding fluid transport in biological systems is fundamental for advancing biomedical science, particularly in the study of complex environments such as solid tumors. The ability of therapeutic agents—such as chemotherapy drugs or nanoparticles—to penetrate tumor tissue and reach cancer cells is severely hindered by the TME. This includes abnormal vasculature, elevated interstitial fluid pressure (IFP), and a dense extracellular matrix (ECM), all of which create barriers to effective drug delivery and solute transport. Consequently, many regions within a tumor remain poorly perfused, limiting treatment efficacy and contributing to resistance. The primary aim of this research is to develop a reduced order theoretical model that predicts plasma transport and solute uptake within solid tumors. This model integrates the Diffusion–Reverse Advection (DRA) framework with Computational Fluid Dynamics (CFD) simulations to capture the complex dynamics of biofluid transport in tumor tissues. By focusing on the theoretical aspects, this approach seeks to offer a computationally efficient and biologically relevant tool for simulating tumor perfusion. The DRA model captures both diffusion and advection mechanisms, including reverse advection, where solutes migrate against the direction of bulk fluid flow due to concentration gradients and elevated IFP. Such behavior is particularly important in tumors, where low-flow zones—often adjacent to densely packed ECM fibers—impede traditional solute transport. A key feature of this framework is the use of a biomimetic reduced-order geometry, designed to represent essential structural features of the tumor microenvironment—such as fiber packing and flow obstruction—without the computational cost of full-scale anatomical modeling. The mathematical formulation centers around a time-dependent convection–diffusion equation, with externally supplied velocity fields. This simplification avoids the need for solving momentum equations and enhances computational tractability. The theoretical model is implemented in Mathematica 14.1 using the Finite Element Method (FEM), and applied to tumor-mimetic geometries containing non-overlapping circular inclusions that represent ECM fiber networks. To validate the model predictions, experimental data from microfluidic tumor-mimetic platforms and 3D tumor spheroids were incorporated. These experiments, conducted in collaboration with Syracuse University, provided insight into solute propagation under controlled conditions. The theoretical model was able to capture spatial solute distribution trends consistent with both experimental observations and CFD simulations. While both the theoretical and numerical models exhibited similar patterns of solute propagation, CFD simulations predicted faster transport due to their ability to resolve finer-scale flow dynamics and recirculation effects. To reconcile this temporal discrepancy, a tuning factor (TF) was introduced, defined as the ratio of CFD-predicted perfusion time to the theoretical prediction for the same solute coverage. This factor enables the theoretical model to match CFD-level predictions without the associated computational cost. Incorporating this tuning factor establishes the DRA model as a practical surrogate for high-fidelity CFD simulations. The integration of theoretical modeling, CFD benchmarking, and experimental validation provides a robust framework for simulating solute transport in tumors. Furthermore, by linking micro-structural tumor features—such as fiber packing fraction and vascular geometry—to perfusion behavior, this framework offers a path toward optimizing drug delivery strategies and guiding personalized therapy. The long-term vision of this research is to make tumor perfusion predictions by offering a validated, computationally efficient alternative to traditional numerical models.
Publisher
South Dakota State University
Recommended Citation
Yeasin, Mohammad, "Integrative Theoretical-Numerical Modeling of Plasma Transport and Perfusion in Solid Tumors" (2025). Electronic Theses and Dissertations. 1525.
https://openprairie.sdstate.edu/etd2/1525
Rights Statement
