Extended logistic growth model for heterogeneous populations

9 Jul 2018, 18:00
2h
Holme Building/--The Refectory (University of Sydney)

Holme Building/--The Refectory

University of Sydney

20
Board: 101
Poster Presentation Biochemistry and Cell Biology Poster Session

Speaker

Dr Wang Jin (School of Mathematical Sciences, Queensland University of Technology)

Description

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 with a constant proliferation rate, and this approach 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.

Primary author

Dr Wang Jin (School of Mathematical Sciences, Queensland University of Technology)

Co-authors

Prof. Scott McCue (School of Mathematical Sciences, Queensland University of Technology) Prof. Matthew Simpson (School of Mathematical Sciences, Queensland University of Technology)

Presentation Materials

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