Output uncertainty of dynamic growth models: Effect of uncertain parameter estimates on model reliability

Abstract

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A model‐based framework for parallel scale‐down fed‐batch cultivations in mini‐bioreactors for accelerated phenotyping

“…The model calculations and robotic executions also helped to establish and trace cause‐and‐effect relationships between process conditions and physiological responses, which are important for the interpretation of the results. Although a fully integrated genome‐scale model of the strain (Weaver, Keseler, Mackie, Paulsen, & Karp, ) coupled with a hydrodynamic model of the larger scale (Haringa et al, ) may give more accurate results, the simplified mechanistic model used here was adequate for the purpose of calculating the concentration gradients, without the constraints of higher computational burden and issues of parameter identifiability in larger models, as discussed by Anane et al (). The further development of the system to simultaneously carry out 21 parallel cultivations ensured the generation of a large amount of data within the shortest possible time and eliminated batch‐to‐batch variability in inoculum and media components.…”

Making sense of parameter estimation and model simulation in bioprocesses

“…As it is known, every experimental data set possesses some level of uncertainty related to sampling procedure, measuring methodology, and measuring instrument. Although it is possible to add such uncertainty into the least square regression methodology for the parameters estimation and propagate it into the model outputs (Anane et al, 2019), to estimate the measurements uncertainty is per se a difficult task. For example, in the sampling procedure during A. vinelandii fermentation, the higher apparent viscosity of the culture medium may hinder the homogenization of the tank, hampering the take of a representative sample, even more toward the end of the process when the alginate concentration is higher.…”

“…propagate it into the model outputs (Anane et al, 2019), to estimate the measurements uncertainty is per se a difficult task. For example, in the sampling procedure during A. vinelandii fermentation, the higher apparent viscosity of the culture medium may hinder the homogenization of the tank, hampering the take of a representative sample, even more toward the end of the process when the alginate concentration is higher.…”

“…Partially motivated by this regulation, in recent years, some articles have been published in the field of bioprocess to address this issue. Anane et al (2019) proposed a methodology, based on frequentist statistic, to detect nonidentifiable parameters and significantly reduce the output uncertainty of a mechanistic growth model. On the other hand, Hernández Rodríguez et al (2019) addressed the uncertainty on parameters and model outputs applying a Bayesian approach.…”

“…Partially motivated by this regulation, in recent years, some articles have been published in the field of bioprocess to address this issue. Anane et al (2019) coding for their implementation. The authors believe that, to date, not many studies complement their results with CI analysis due to the difficulties of understanding and using statistical functions, even when they are already implemented and well documented.…”