Mathematical models of tumor cell proliferation: A review of the literature. Issue 12 (2nd December 2018)

• Record Type: Journal Article
• Authors: Jarrett, Angela M.; Lima, Ernesto A.B.F.; Hormuth, David A.; McKenna, Matthew T.; Feng, Xinzeng; Ekrut, David A.; Resende, Anna Claudia M.; Brock, Amy; Yankeelov, Thomas E.
• Is Part Of: Expert review of anticancer therapy. Volume 18:Issue 12(2018)
• Page Start: 1271
• Page End: 1286
• Publication Date: 2018-12-02
• Subjects: Computational -- biophysical -- cancer -- oncology -- cell growth; Cancer -- Treatment -- Periodicals; 616.99406
• DOI: 10.1080/14737140.2018.1527689
• Languages: English
• ISSNs: 1473-7140

Abstract

ABSTRACT: Introduction : A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. Areas covered : We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. Expert commentary : The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation.