Systematic methodology for bioprocess model identification based on generalized kinetic functions

Richelle, Anne; Bogaerts, Philippe

doi:10.1016/j.bej.2015.04.003

Cited by 16 publications

(12 citation statements)

References 23 publications

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“…Note that once the macroscopic reactions have been deduced from the metabolic network, it remains to identify their kinetic models. To that purpose, general kinetic models and systematic identification procedures can be very useful [81,82]. Model reduction methodologies based on subsets of balanced metabolites interconnected via linking metabolites that may accumulate within cells [64,65], have also been introduced in the previous section.…”

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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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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“…However, sometimes it is necessary to model a bioprocess that has not been extensively studied, and experimentation must be used to quantify some parameters. This method in dealing with parameter identification can be observed in ref ( 4 ), where a bioprocess identification strategy was proposed based on the generalized bioprocess model to improve the parameter adjustment associated with the reaction kinetics used in the modeling stage, or in refs ( 5 ) and ( 6 ), which use genetic algorithms to perform a global search of the parameters describing bioreactor behavior for the production of ethanol and culture of E. coli MC4110 in a semibatch reactor, respectively. Finally, in ref ( 7 ) was proposed a methodology for modeling the enzymatic hydrolysis process, considering the estimation of some parameters selected arbitrarily, using the mean square error as a cost function.…”

Section: Introductionmentioning

confidence: 99%

Modeling Based Identifiability and Parametric Estimation of an Enzymatic Hydrolysis Process of Amylaceous Materials

et al. 2022

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This work presents the modeling of an enzymatic hydrolysis process of amylaceous materials considering the parameter identification problem as a basis for the construction of the model. For this, a modeling methodology is modified in order to apply the identifiability property and improve the proposed model structure. A brief theoretical explanation of the identifiability is described. This concept is based on the observability property of a nonlinear dynamic system. The used methodology is based on the phenomenological based semiphysical model (PBSM). This methodology visualizes that the structure of a dynamic model can only improve with new mass or energy balances suggested by model suppositions. Additionally, a computer algorithm is included in the methodology to validate if the model is structurally locally identifiable or know if the parameters are unidentifiable. Also, an optimization algorithm is used to obtain the numeric values of the identifiable parameters and, hence, guarantee the validity of the result. The methodology focuses on the liquefaction and saccharification stages of an enzymatic hydrolysis process. The results of the model are compared with experimental data. The comparison shows low errors of 7.96% for liquefaction and 7.35% for saccharification. These errors show a significant improvement in comparison with previous models and validate the proposed modeling methodology.

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“…Instead of using the classical Monod type models, Bogaerts et al (1999) developed an exponential model, later improved by Grosfils et al (2007), for a systematic identification approach which allows to obtain maximum likelihood estimates of parameters after linearization. Following the same idea, Richelle and Bogaerts (2015) transform the identified exponential models to the classical Monod type models for better biological interpretations.…”

Section: Introductionmentioning

confidence: 99%

Identification of nonlinear kinetics of macroscopic bio-reactions using multilinear Gaussian processes

Wang

Risuleo

Jacobsen

et al. 2020

Computers & Chemical Engineering

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In biological systems, nonlinear kinetic relationships between metabolites of interest are modeled for various purposes. Usually, little a priori knowledge is available in such models. Identifying the unknown kinetics is, therefore, a critical step which can be very challenging due to the problems of (i) model selection and (ii) nonlinear parameter estimation. In this paper, we aim to address these problems systematically in a framework based on multilinear Gaussian processes using a family of kernels tailored to typical behaviours of modulation effects such as activation and inhibition or combinations thereof. Using one such process as a model for each modulation effect leads to a much more flexible model than conventional parametric models, e.g., the Monod model. The resulting models of the modulation effects can also be used as a starting point for estimating parametric kinetic models. As each modulation effect is modeled separately, this task is greatly simplified compared to the conventional approach where the parameters in all modulation functions have to be estimated simultaneously. We also show how the type of modulation effect can be selected automatically by way of regularization, thus bypassing the model selection problem. The resulting parameter estimates can be used as initial estimates in the conventional approach where the full model is estimated. Numerical experiments, including fed-batch simulations, are conducted to demonstrate our methods.

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Systematic methodology for bioprocess model identification based on generalized kinetic functions

Cited by 16 publications

References 23 publications

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

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

Modeling Based Identifiability and Parametric Estimation of an Enzymatic Hydrolysis Process of Amylaceous Materials

Identification of nonlinear kinetics of macroscopic bio-reactions using multilinear Gaussian processes

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