A computational model for proliferation dynamics of division- and label-structured populations

TL;DR

This paper introduces a novel computational framework combining discrete age structure and continuous label dynamics to model proliferating cell populations, enabling efficient analysis and comparison with labeling experiments.

Contribution

It presents a new model that integrates division number dependence and label dynamics, with analytical solutions reducing computational complexity.

Findings

Model can predict label distributions in complex systems

Analytical PDE solutions simplify computations

Linking fluorescence to autofluorescence enhances experimental relevance

Abstract

In most biological studies and processes, cell proliferation and population dynamics play an essential role. Due to this ubiquity, a multitude of mathematical models has been developed to describe these processes. While the simplest models only consider the size of the overall populations, others take division numbers and labeling of the cells into account. In this work, we present a modeling and computational framework for proliferating cell population undergoing symmetric cell division. In contrast to existing models, the proposed model incorporates both, the discrete age structure and continuous label dynamics. Thus, it allows for the consideration of division number dependent parameters as well as the direct comparison of the model prediction with labeling experiments, e.g., performed with Carboxyfluorescein succinimidyl ester (CFSE). We prove that under mild assumptions the resulting system of coupled partial differential equations (PDEs) can be decomposed into a system of ordinary differential equations (ODEs) and a set of decoupled PDEs, which reduces the computational effort drastically. Furthermore, the PDEs are solved analytically and the ODE system is truncated, which allows for the prediction of the label distribution of complex systems using a low-dimensional system of ODEs. In addition to modeling of labeling dynamics, we link the label-induced fluorescence to the measure fluorescence which includes autofluorescence. For the resulting numerically challenging convolution integral, we provide an analytical approximation. This is illustrated by modeling and simulating a proliferating population with division number dependent proliferation rate.