Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology
Anum S. Kazerouni,
Manasa Gadde,
Andrea Gardner,
David A. Hormuth, II,
Angela M. Jarrett,
Kaitlyn E. Johnson,
Ernesto A.B. F. Lima,
Guillermo Lorenzo,
Caleb Phillips,
Amy Brock,
Thomas E. Yankeelov
Affiliations
Anum S. Kazerouni
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Manasa Gadde
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
Andrea Gardner
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
David A. Hormuth, II
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
Angela M. Jarrett
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
Kaitlyn E. Johnson
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Ernesto A.B. F. Lima
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78712, USA
Guillermo Lorenzo
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Caleb Phillips
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Amy Brock
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
Thomas E. Yankeelov
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Corresponding author
Summary: We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.
