Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

Anum S Kazerouni; Manasa Gadde; Andrea Gardner; David A Hormuth 2nd; Angela M Jarrett; Kaitlyn E Johnson; Ernesto A B F Lima; Guillermo Lorenzo; Caleb Phillips; Amy Brock; Thomas E Yankeelov

doi:10.1016/j.isci.2020.101807

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

iScience. 2020 Nov 13;23(12):101807. doi: 10.1016/j.isci.2020.101807. eCollection 2020 Dec 18.

Authors

Anum S Kazerouni¹, Manasa Gadde^{1

2}, Andrea Gardner¹, David A Hormuth 2nd^{3

4}, Angela M Jarrett^{3

4}, Kaitlyn E Johnson¹, Ernesto A B F Lima^{3

5}, Guillermo Lorenzo³, Caleb Phillips³, Amy Brock^{1

4}, Thomas E Yankeelov^{1

2

6

3

4

7}

Affiliations

¹ Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
² Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA.
³ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
⁴ Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
⁵ Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78712, USA.
⁶ Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA.
⁷ Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Abstract

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.

Keywords: Bioengineering; Cancer; In Silico Biology; Systems Biology.

Publication types

Review

Abstract

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