Dynamic estimations of metabolic fluxes with constraint-based models and possibility theory

Llaneras, Francisco; Sala, Antonio; Picó, Jesús

doi:10.1016/j.jprocont.2012.09.001

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…These methods originally provide static insights into metabolic routes, what allows to study cell cultures at a state of particular interest. Even though several extensions were proposed to include extracellular dynamics in the framework of MFA and FBA, 81 precise mathematical representation of reaction kinetics and regulation mechanisms remains a challenge. See the review for a concise overview of metabolic models.…”

Section: Figurementioning

confidence: 99%

Metabolic Network Modelling of Chinese Hamster Ovary (CHO) Culture Bioreactors Operated as Microbial Cell Factories

Gašperšič

Kastelic

Novak

et al. 2018

ACSi

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Chinese hamster ovary (CHO) epithelial cells are one of the most used therapeutic medical lines for the production of different biopharmaceutical drugs. They have a high consumption rate with a fast duplication cycle that makes them an ideal biological clone. The higher accumulated amounts of toxic intracellular intermediates may lead to lower organism viability, protein productivity and manufactured biosimilar, so a careful optimal balance of medium, bioreactor operational parameters and bioprocess is needed. A precise phenomenological knowledge of metabolism's chemical transformations can predict problems that may arise during batch, semi-continuous fed batch and continuous reactor operation. For a better detailed understanding (and relations), future performance optimization and scaling, mechanistic model systems have been built. In this specific work, the main metabolic pathways in mammalian structured CHO cultures are reviewed. It starts with organic biochemical background, controlling associated phenomena and kinetics, which govern the sustaining conversion routes of biology. Then, individual turnover paths are described, overviewing standard mathematical formulations that are commonly applied in engineering. These are the core of black box modeling, which relates the substrates/products in a simplified relationship manner. Moreover, metabolic flux analysis (MFA)/flux balance analysis (FBA), that are traditionally characterizing mechanisms, are presented to a larger portion extent. Finally, similarities are discussed, illustrating the approaches for their structural design. Stated variables' equations, employed for the description of the growth in the controllable environmental conditions of a vessel, the researched reaction series of proliferating dividing CHO population, joint with the values of maximal enzymatic activity, and solutions are outlined. Processes are listed in a way so that a reader can integrate the state-of-the-art. Our particular contribution is also denoted.

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Section: Figurementioning

confidence: 99%

Metabolic Network Modelling of Chinese Hamster Ovary (CHO) Culture Bioreactors Operated as Microbial Cell Factories

Gašperšič

Kastelic

Novak

et al. 2018

ACSi

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“…In addition to traditional MPA methods, extreme admissible values of individual internal fluxes can be determined through computation of flux variability analysis (FVA) which consists in solving a simple linear programming problem ( Leighty and Antoniewicz, 2011;Llaneras et al, 2012;Vercammen et al, 2014 ). While the usual analysis provides an insight into the cell activity under specific growth conditions, in the course of the exponential phase or the stationary phase for example, several studies provide a dynamic overview over the whole experiment using FBA ( Mahadevan et al, 2002 ), MPA ( Fernandes et al, 2016;Zamorano et al, 2013 ) or FVA ( Richelle et al, 2016;Bogaerts et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Adaptive flux variability analysis of HEK cell cultures

Abbate

Dewasme

Wouwer

et al. 2020

Computers & Chemical Engineering

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Measured external fluxes impose constraints to under-determined metabolic networks that narrow the internal flux intervals obtained using Flux Variability Analysis. Nevertheless, these constraints often lead to systems that do not admit a feasible solution. Measurement noise and data smoothing are among the sources of uncertainties that can cause system infeasibility. These constraints are classically released using interval representation of fluxes. This study investigates the use of Adaptive Flux Variability Analysis (AFVA), which allows determining a minimal coefficient of variation of the external fluxes along the time course of the experiment. Especially, AFVA is applied to a medium-size metabolic network and a rich dataset relative to HEK-293 cells cultured in batch, encompassing all 20 amino acids and less commonly measured metabolites, such as urea and pyruvate. AFVA appears as an effective tool for metabolic flux analysis. The impact of data-smoothing and the information provided by the cell growth are thoroughly analyzed.

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“…When the environmental conditions of a culture change, dynamics should be considered to investigate cellular metabolism. To this end, the development of dynamic metabolic flux analysis (DMFA) techniques has been addressed (Leighty and Antoniewicz, 2011;Lequeux et al, 2010;Llaneras et al, 2012;Niklas et al, 2010;Vercammen et al, 2014). For instance, Lequeux et al (2010) extended MFA based on the transformation of time series of concentration measurements into flux values.…”

Section: Introductionmentioning

confidence: 99%

“…This method was illustrated with a fed‐batch fermentation of E. coli , but is presented by the authors as a generic method (e.g., useful for microbial, mammalian and plant cells). In the work of Llaneras et al (), dynamic extracellular concentration measurements are taken into account into a possibilistic MFA strategy by approximating the derivatives of the concentrations. This approach provides solution intervals, where the ranges of possible solutions are obtained by solving a set of minimum‐maximum linear programming problems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic metabolic flux analysis using a convex analysis approach: Application to hybridoma cell cultures in perfusion

Sousa

Bastin

Jolicœur

et al. 2015

Biotech & Bioengineering

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In recent years, dynamic metabolic flux analysis (DMFA) has been developed in order to evaluate the dynamic evolution of the metabolic fluxes. Most of the proposed approaches are dedicated to exactly determined or overdetermined systems. When an underdetermined system is considered, the literature suggests the use of dynamic flux balance analysis (DFBA). However the main challenge of this approach is to determine an appropriate objective function, which remains valid over the whole culture. In this work, we propose an alternative dynamic metabolic flux analysis based on convex analysis, DMFCA, which allows the determination of bounded intervals for the fluxes using the available knowledge of the metabolic network and information provided by the time evolution of extracellular component concentrations. Smoothing splines and mass balance differential equations are used to estimate the time evolution of the uptake and excretion rates from this experimental data. The main advantage of the proposed procedure is that it does not require additional constraints or objective functions, and provides relatively narrow intervals for the intracellular metabolic fluxes. DMFCA is applied to experimental data from hybridoma HB58 cell perfusion cultures, in order to investigate the influence of the operating mode (batch and perfusion) on the metabolic flux distribution.

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Dynamic estimations of metabolic fluxes with constraint-based models and possibility theory

Cited by 14 publications

References 23 publications

Metabolic Network Modelling of Chinese Hamster Ovary (CHO) Culture Bioreactors Operated as Microbial Cell Factories

Metabolic Network Modelling of Chinese Hamster Ovary (CHO) Culture Bioreactors Operated as Microbial Cell Factories

Adaptive flux variability analysis of HEK cell cultures

Dynamic metabolic flux analysis using a convex analysis approach: Application to hybridoma cell cultures in perfusion

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