Efficient Generation of Models of Fed-Batch Fermentations for Process Design and Control

Hebing, Lukas; Neymann, Tobias Claus; Thüte, Tobias; Jockwer, Alexander; Engell, Sebastian

doi:10.1016/j.ifacol.2016.07.237

Cited by 11 publications

(15 citation statements)

References 6 publications

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“…With regard to the process lifecycle, the focus of this review will be on these automated workflows for the setup of dynamic mechanistic models. A generic and strongly knowledge-driven approach is shown by the company Bayer AG ( 18 , 19 ): Based on an extensive dynamic metabolic flux model in combination with a generic algorithm, the initial complex model is reduced to the most necessary parts. The benefit of this top-down approach is the intense use of prior knowledge.…”

Section: Resultsmentioning

confidence: 99%

Model-Based Methods in the Biopharmaceutical Process Lifecycle

et al. 2017

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Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

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Section: Resultsmentioning

confidence: 99%

Model-Based Methods in the Biopharmaceutical Process Lifecycle

et al. 2017

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“…In an earlier contribution (Hebing et al, 2016; Neymann, Hebing, & Engell, 2019) we proposed to select and fit nonlinear reaction kinetics

\overset{r}{true} (Θ)

to estimated rates of EM r by solving:

\underset{normalΘ}{normalmin} \sum_{i} {true(\frac{\overset{r}{true} (t_{i}, normalΘ) - r (t_{i})}{σ (t_{i})} true)}^{2},

here,

normalΘ

is the parameter vector and

σ (t_{i})

is the standard deviation of the estimate which can be obtained from the bootstrap samples of the estimate

r (t)

. Only with a reliable estimate of

σ

, it is possible to fit and compare meaningful kinetics based on a statistical measure as, for example, the Akaike information criterion.…”

Section: Real World Example: Em Selection and Analysis Using Experimementioning

confidence: 95%

“…In an earlier contribution (Hebing et al, 2016;Neymann, Hebing, & Engell, 2019) we proposed to select and fit nonlinear reaction kinetics r ˆ(Θ) to estimated rates of EM r by solving:…”

Section: Further Modeling Stepsmentioning

confidence: 99%

“…Some elements of this procedure were already published presented in (Hebing, Neymann, Thüte, Jockwer, & Engell, 2016). This contribution extends this study by (a) adding regularization to the DMFA for EM problem, (b) adding linear constraints to the original DMFA approach, (c) showing how the bounded DMFA method can be used for the evaluation of a metabolic network, and (d) calculating confidence intervals of the specific reaction rates.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of dynamic metabolic flux analysis for process modeling: Robust flux estimation with regularization, confidence bounds, and selection of elementary modes

Hebing

Neymann

Engell

2020

Biotech & Bioengineering

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In macroscopic dynamic models of fermentation processes, elementary modes (EM) derived from metabolic networks are often used to describe the reaction stoichiometry in a simplified manner and to build predictive models by parameterizing kinetic rate equations for the EM. In this procedure, the selection of a set of EM is a key step which is followed by an estimation of their reaction rates and of the associated confidence bounds. In this paper, we present a method for the computation of reaction rates of cellular reactions and EM as well as an algorithm for the selection of EM for process modeling. The method is based on the dynamic metabolic flux analysis (DMFA) proposed by Leighty and Antoniewicz (2011, Metab Eng, 13(6), 745–755) with additional constraints, regularization and analysis of uncertainty. Instead of using estimated uptake or secretion rates, concentration measurements are used directly to avoid an amplification of measurement errors by numerical differentiation. It is shown that the regularized DMFA for EM method is significantly more robust against measurement noise than methods using estimated rates. The confidence intervals for the estimated reaction rates are obtained by bootstrapping. For the selection of a set of EM for a given st oichiometric model, the DMFA for EM method is combined with a multiobjective genetic algorithm. The method is applied to real data from a CHO fed‐batch process. From measurements of six fed‐batch experiments, 10 EM were identified as the smallest subset of EM based upon which the data can be described sufficiently accurately by a dynamic model. The estimated EM reaction rates and their confidence intervals at different process conditions provide useful information for the kinetic modeling and subsequent process optimization.

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“…Based on extensions of Dynamic Metabolic Flux Analysis introduced in [76], that only uses concentration measurements and avoids any numerical differentiation, refs. [77,78] select reduced sets of EFMs via a geometrical reduction (excluding EFMs with a cosine-similarity algorithm) followed by a multi-objective genetic algorithm that minimizes the prediction error and the size of the EFMs subset. A linear optimization problem has been formulated in [79] for selecting the best subset of EFMs based on a relaxation criterion.…”

Section: Model Reduction To Macroscopic Scalementioning

confidence: 99%

How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review

Bogaerts

Wouwer

2021

Processes

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Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.

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Efficient Generation of Models of Fed-Batch Fermentations for Process Design and Control

Cited by 11 publications

References 6 publications

Model-Based Methods in the Biopharmaceutical Process Lifecycle

Model-Based Methods in the Biopharmaceutical Process Lifecycle

Application of dynamic metabolic flux analysis for process modeling: Robust flux estimation with regularization, confidence bounds, and selection of elementary modes

How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review

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