Michael A. Henson Group

Dynamic Modeling of Tumor Metabolism and Growth

The objective of this project is to develop model-based analysis techniques for the dependence of tumor growth on the metabolism of individual cells under normal conditions and chemotherapeutic treatment. Our initial work involved the development and analysis of a dynamic model of multicellular spheroid growth that includes the diffusion of multiple nutrients and their effects on cellular metabolism in different microenvironments. Dynamic simulation and parametric sensitivity studies were used to evaluate model behavior, including the spatial distribution of proliferating, quiescent, and dead cells for different cellular characteristics. The critical cell survival parameters that have the greatest impact on overall spheroid physiology were determined, demonstrating that oxygen transport has a greater effect than glucose transport on the distribution of quiescent cells. We are currently integrating dynamic contrast MRI images into tumor cord models to simulate patient dependent tumor heterogeneity and to identify promising therapeutic strategies. Our long-term goal is to develop in vivo tumor models that have the potential to predict therapeutic efficiency and can be used to design effective chemotherapeutic strategies.

Funding: UMass

Student: Raja Venkatasubramanian (6th year Ph.D.)

Collaborator: Prof. Neil Forbes (UMass, co-advisor)

Publications and Recent Presentations:

1. Venkatasubramanian, R., M. A. Henson and N. S. Forbes, "Incorporating Energy Metabolism into a Growth Model of Multicellular Tumor Spheriods," Journal of Theoretical Biology, 242, 440-453 (2006). [PDF]
2. Venkatasubramanian, R., M. A. Henson and N. S. Forbes, "Integrating Cell Cycle Progression, Drug Penetration and Energy Metabolism to Identify Improved Cancer Therapeutic Strategies," Journal of Theoretical Biology, 253, 98-117 (2008). [PDF]
3. Venkatasubramanian, R., M. A. Henson and N. S. Forbes, "Integration of Dynamic Contrast MRI Images into Tumor Cord Models to Simulate Patient Dependent Tumor Heterogeneity," AIChE Annual Meeting, Salt Lake City, UT, November 2007.