A two-dimensional population balance model for cell growth including multiple uptake systems

Quedeville, Vincent; Ouazaite, Hicham; Polizzi, Bastien; Fox, Rodney O.; Villedieu, Philippe; Fede, Pascal; Létisse, Fabien; Morchain, Jérôme

doi:10.1016/j.cherd.2018.02.025

Cited by 16 publications

(9 citation statements)

References 39 publications

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“…In this form, it is visible that the model describes the adaptation of the glucose uptake capacity toward the arithmetic mean of the external supply and the internal demand. This resembles the empirical correlation for the uptake rate, as exposed by Zeng in a series of work in the 1990s (Xiu et al, 1998;Zeng & Deckwer, 1995) and formerly used in our previous work without any theoretical justification (equation 2.12 in V. Quedeville et al, 2018).…”

Section: Supporting Informationsupporting

confidence: 56%

“…Equation ( 11) indicates that the actual growth rate is either set by the cell growth capacity, μ, or by the actual glucose uptake rate ϕ a . Equation ( 12) describes the rate of specific growth-rate adaptation inspired by a previous works (Morchain & Fonade, 2009;Quedeville et al, 2018). In the present version, the cell demand is estimated from the current growth capacity and compared with the actual uptake rate to dynamically adapt the cell growth capacity.…”

Section: Multiscale Dynamic Modelmentioning

confidence: 99%

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The closure issue related to liquid–cell mass transfer and substrate uptake dynamics in biological systems

Morchain

Quedeville

Fox

et al. 2021

Biotech & Bioengineering

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An original dynamic model for substrate uptake under transient conditions is established and used to simulate a variety of biological responses to external perturbations. The actual uptake and growth rates, treated as cell properties, are part of the model variables as well as the substrate concentration at the cell-liquid interface. Several regulatory loops inspired by the structure of the glycolytic chain are considered to establish a set of ordinary differential equations. The uptake rate evolves so as to reach an equilibrium between the cell demand and the environmental supply. This model does not contain any of the usual algebraic closure laws relating to the instantaneous uptake, growth rates, and the substrate concentration, nor does it enforce the continuity of mass fluxes at the liquid-cell interface. However, these relationships are found in the steady-state solution. Previously unexplained experimental observations are well reproduced by this model. Also, the model structure is suitable for further coupling with flux-based metabolic models and fluid-flow equations.

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Section: Supporting Informationsupporting

confidence: 56%

Section: Multiscale Dynamic Modelmentioning

confidence: 99%

The closure issue related to liquid–cell mass transfer and substrate uptake dynamics in biological systems

Morchain

Quedeville

Fox

et al. 2021

Biotech & Bioengineering

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“…3) exhibit a fairly similar pattern once exponential growth is reached. In this context, ν m is retrieved from the population’s growth in mass (that is tantamount to its growth in number as mentioned in 13,25 ) over a certain time interval:with t , t + Δ t belonging to the so-called “log phase”. In the Monte–Carlo simulation, Δ t = 3.28925 hr, cell mass was multiplied by 2.471 to three decimal places, and .…”

Section: Analytical and Numerical Resultsmentioning

confidence: 99%

A critical analysis of Powell’s results on the interdivision time distribution

Quedeville

Morchain

Villedieu

et al. 2019

Sci Rep

Self Cite

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The cell-age and interdivision-time probability density functions (PDFs) have been extensively investigated since the 1940s due to their fundamental role in cell growth. The pioneering work of Powell established the first relationship between the interdivision-time and cell-age PDFs. In the literature, two definitions for the interdivision-time PDF have been proposed. One stands for the age-at-rupture PDF and is experimentally observable, whereas the other is the probability density that a cell divides at a certain age and is unobservable. From Powell’s results pertaining to the unobservable interdivision-time PDF, Painter and Marr derived an inequality that is true but is incorrectly used by experimentalists to analyse single-cell data. Unfortunately, the confusion between these two PDFs persists. To dissipate this confusion, exact relationships between the cell-age and the interdivision-time PDFs are derived in this work from an age-structured model, which can be used by experimentalists to analyse cell growth in batch and continuous culture modes.

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“…Y n classinteractions may occur for n pools with Y classes, posing a potential computational challenge. Models with two compositional variables have been reported [44]. How the PBM framework performs with higher-dimensional metabolic models is a development to be followed with interest.…”

Section: Coupling Methodsmentioning

confidence: 99%

From industrial fermentor to CFD-guided downscaling: what have we learned?

Haringa

Mudde

Noorman

2018

Biochemical Engineering Journal

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• A comprehensive overview of the Euler-Lagrange bioreactor simulation approach. • Application of Euler-Lagrange CFD to three different case studies. • Different strategies to design scale-down experiments using CFD data are discussed. • Approach selection chart based on hydrodynamic characteristics of modeled reactor. • The potential of combining Euler-Lagrange CFD with microfluidics is discussed.

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A two-dimensional population balance model for cell growth including multiple uptake systems

Abstract: Jérôme. A twodimensional population balance model for cell growth including multiple uptake systems. (2018) Chemical Engineering Research and Design, 132. 966-981.

Cited by 16 publications

References 39 publications

The closure issue related to liquid–cell mass transfer and substrate uptake dynamics in biological systems

The closure issue related to liquid–cell mass transfer and substrate uptake dynamics in biological systems

A critical analysis of Powell’s results on the interdivision time distribution

From industrial fermentor to CFD-guided downscaling: what have we learned?

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