Extended logistic growth model for heterogeneous populations. (14th May 2018)

• Record Type: Journal Article
• Authors: Jin, Wang; McCue, Scott W.; Simpson, Matthew J.
• Is Part Of: Journal of theoretical biology. Volume 445(2018)
• Page Start: 51
• Page End: 61
• Publication Date: 2018-05-14
• Subjects: Cell proliferation -- Heterogeneity -- Population dynamics -- Logistic growth; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2018.02.027
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: New stochastic model of heterogeneous cell migration and proliferation. Continuum limit related to a novel generalisation of classical logistic growth. Parameterise model with experimentally-derived heterogeneous proliferation rates. New quantitative framework to explore the implication of neglecting heterogeneity. Perturbation solutions provide analytical insight into the role of heterogeneity. Graphical abstract: Abstract: Cell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity . Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.