Distributed modeling of human influenza a virus–host cell interactions during vaccine production

Abstract

This contribution is concerned with population balance modeling of virus-host cell interactions during vaccine production. Replication of human influenza A virus in cultures of adherent Madin-Darby canine kidney (MDCK) cells is considered as a model system. The progress of infection can be characterized by the intracellular amount of viral nucleoprotein (NP) which is measured via flow cytometry. This allows the differentiation of the host cell population and gives rise to a distributed modeling approach. For this purpose a degree of fluorescence is introduced as an internal coordinate which is linearly linked to the intracellular amount of NP. Experimental results for different human influenza A subtypes reveal characteristic dynamic phenomena of the cell distribution like transient multimodality and reversal of propagation direction. The presented population balance model provides a reasonable explanation for these dynamic phenomena by the explicit consideration of different states of infection of individual cells. Kinetic parameters are determined from experimental data. To translate the emerging infinite dimensional parameter estimation problem to a finite dimension the parameters are assumed to depend linearly on the internal coordinate. As a result, the model is able to reproduce all characteristic dynamic phenomena of the considered process for the two examined virus strains and allows deeper insight into the underlying kinetic processes. Thus, the model is an important contribution to the understanding of the intracellular virus replication and virus spreading in cell cultures and can serve as a stepping stone for optimization in vaccine production.

Approximate Moment Methods for Population Balance Equations in Particulate and Bioengineering Processes

“…(I) In the top-down-modeling approach, only a few particle properties are taken into account (often only one). The unknown fluxes and source/sink mechanics of the corresponding PBEs are either described by semi-empirical approaches or adapted to experimental data with suitable methods (see, e.g., [61][62][63][64][65][66]). (II) In contrast, the bottom-up approach is based on detailed first-principle modeling of the individual particle behavior, e.g., ordinary or stochastic differential equations, which is adapted to single-particle experimental data.…”

A Novel Framework for Parameter and State Estimation of Multicellular Systems Using Gaussian Mixture Approximations

“…Multicellular systems are not only found at the core of many fundamental biomedical processes like cell differentiation [1] and wound healing, but also play an essential role in a wide spectrum of biotechnological processes ranging from pharmaceutical manufacturing [2,3] to biopolymer production [4] to biological waste-water treatment [5]. The individual cells do not only interact with each other, but also with their environment.…”

Hybrid simulation algorithm for efficient numerical solution of population balance equations

“…Multicellular systems are found in many bioprocesses, for example vaccine production (Müller et al, 2013;Tapia et al, 2016), biopolymer production (Franz et al, 2011(Franz et al, , 2012, biological waste water treatment (Nopens et al, 2015)), and they play an important part in many physiological processes like cell differentiation (Bessonnard et al, 2014). Individual cells can be characterized by certain properties, e.g.…”