Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics

Abstract

Physiological processes rely on control of cell proliferation in time and space and dysregulation of cell growth underlies pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-lab studies may be integrated with different mathematical modeling approaches to aid interpretation of the results and to enable prediction of cell behaviors, specifically in the context of cancer.

Flow cytometry: Aspects and application in plant and biological science

“…They might involve time-lapse measurements or single timepoint measurements [170]. Either cytochemically, using DNA dyes like Hoechst 33258, whose fluorescence is quenched by BrdU [166,173], or immunocytochemically, employing fluoresceinated BrdU-antibodies, are used to identify the integrated BrdU [174]. Using time-lapse data, one may determine a collection of BrdU-labeled cells that aids in detecting the rate of progression through different cell cycle phases [175].…”

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

“…The results obtained from the wet-lab methods can be integrated with computational approaches for the interpretation of the results. The integration of experimental and modeling techniques provides deeper insights, a more accurate and dynamic estimate of efficacies of treatments ( Cotner et al, 2023 ). The integration of the two approaches can be achieved through the use of in vitro microfluidic devices such as microfluidic used to mimic vascularization ( Cotner et al, 2023 ).…”