The Frieboes lab at UofL pursues an improved understanding of disease progression and response to treatment by applying principles from engineering and the physical sciences. The lab expertise is focused on the development and integration of mathematical modeling, computational simulation, and experimental biology techniques to study cancer. This work is part of the burgeoning field of “Physical Oncology,” in which cancer is studied not only from a biological standpoint but also as a physical system using mathematics and physics. This interdisciplinary study of cancer requires that experimental and clinical data drive the computational and modeling work. The aim of Dr. Frieboes’ research is to predict tumor behavior from the molecular and cellular scale events, with the ultimate goal to help guide the treatment of individual patients. This novel research intersects the fields of cancer biology, scientific computing, data visualization, mathematical biology, and physical oncology.

The ultimate goal of this integrated physical sciences/biology work is to dramatically improve cancer treatment outcomes. To this end, the work can be divided into the following scientific contributions:

Mathematical modeling and computational simulation to characterize tumor growth
Multiscale linking of molecular- to cell- to-tissue-scale events during tumor progression
Integration of modeling and experimentation to characterize cancer treatment response
Modeling and simulation of cancer nanotherapy
Modeling and simulation of cancer immunotherapy

Personnel:

We plan to work on one project on the LCC cluster, namely, mathematical modeling and computational simulation to characterize tumor growth. This project seeks to simulate larger-scale tumors, for which the computational and memory requirements are larger than are available at UofL. Current personnel includes the PI (Frieboes) and one GRA (Dylan Goodin)

Computational Methods:

The computational methods include the solution of partial differential equations and multigrid techniques to represent tumors in a 3D spatial domain. The PDE solution is computed using an internally developed C++ program that uses two parallel processing libraries: (1) CUDA and (2) MPI, both of which are readily available from Nvidia and openMPI, respectively.

Software:

There are two compilers used for the project: (1) MPI’s mpicc C++ wrapper compiler and (2) CUDA’s nvcc compiler. Result visualization is done via Matlab.

Student:

The only student intended to be involved in this project at this time is the GRA Dylan Goodin.

Grants:

Publications:

Dylan Goodin and Hermann Frieboes. Simulation of 3D centimeter-scale continuum tumor growth at sub-millimeter resolution via distributed computing. Computers in Biology & Medicine (2021) in press.

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