Dynamic Density Estimation in Heterogeneous Cell Populations

TL;DR

This paper introduces a novel characteristics-based density estimator for heterogeneous cell populations that outperforms traditional grid-based methods in accuracy and computational efficiency, aiding bioprocess monitoring.

Contribution

It presents a new sample-based density estimation method for population balance models that avoids discretization, improving accuracy and efficiency.

Findings

Outperforms grid-based methods in benchmark tests

Provides more accurate estimates of cell heterogeneity

Reduces computational demand significantly

Abstract

Multicellular systems play a key role in bioprocess and biomedical engineering. Cell ensembles encountered in these setups show phenotypic variability like size and biochemical composition. As this variability may result in undesired effects in bioreactors, close monitoring of the cell population heterogeneity is important for maximum production output, and accurate control. However, direct measurements are mostly restricted to a few cellular properties. This motivates the application of model-based online estimation techniques for the reconstruction of non-measurable cellular properties. Population balance modeling allows for a natural description of cell-to-cell variability. In this contribution, we present an estimation approach that, in contrast to existing ones, does not rely on a finite-dimensional approximation through grid based discretization of the underlying population balance model. Instead, our so-called characteristics based density estimator employs sample approximations. With two and three-dimensional benchmark examples we demonstrate that our approach is superior to the grid based designs in terms of accuracy and computational demand.