Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics

Visser, Diana; Heijnen, Joseph J.

doi:10.1016/s1096-7176(03)00025-9

Cited by 141 publications

(136 citation statements)

References 33 publications

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“…Here lin-log kinetics has been developed recently [28], which has its roots in the concept that the rate of a process is related to the thermodynamic driving force!! [29].…”

Section: Thermodynamic Inspired Kineticsmentioning

confidence: 99%

“…This has been studied in several simulation studies. In a first study, where the lin-log concept was introduced [28], a small branched reaction network, including cofactors, was used as a test case. The strongly non-linear kinetic model was approximated with a lin-log model using the theoretical elasticity parameters.…”

Section: In Silico Studies With Lin-log Kineticsmentioning

confidence: 99%

“…Lin-log kinetics allows a general steady state analytical solution of networks which gives fluxes as a non-linear function of enzyme activities with flux control coefficients C J as parameters [28]. This equation was successfully used to obtain C J -values from experimental data: l A linear product pathway in Penicillin synthesis using fed batch data on flux and enzymes in the penicillin pathway [37] l The lower glycolysis in a reconstituted linear three enzyme pathway [38] l A branch point for lysine or glutamate synthesis [39] Estimation of elasticities from experimental data using lin-log format requires experimental information of fluxes, enzyme levels and metabolite levels.…”

Section: Application Of Lin-log Kinetics To Experimental Datamentioning

confidence: 99%

“…This constrains the e-estimation problem. Lin-log kinetics cannot be applied for datasets where concentrations become zero [28], [30]. An analogous problem also occurs in powerlaw format where an inhibitor concentration cannot become zero.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Thermodynamic Principles in Systems Biology

Heijnen

2010

Biosystems Engineering II

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“…Here lin-log kinetics has been developed recently [28], which has its roots in the concept that the rate of a process is related to the thermodynamic driving force!! [29].…”

Section: Thermodynamic Inspired Kineticsmentioning

confidence: 99%

Section: In Silico Studies With Lin-log Kineticsmentioning

confidence: 99%

Section: Application Of Lin-log Kinetics To Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of Thermodynamic Principles in Systems Biology

Heijnen

2010

Biosystems Engineering II

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“…While most of our definitions are of general applicability, identifiability results will be developed primarily for approximate kinetic models known as linlog models [Visser and Heijnen, 2003], whose pseudo-linear form enables us to apply tools from linear algebra and estimation theory in a straightforward manner. Similar results can be derived easily for many other approximate kinetic model classes in pseudo-linear form, such as the linear, loglin and generalized mass-action kinetic models [Delgado and Liao, 1992, Hatzimanikatis and Bailey, 1997, Savageau, 1976.…”

Section: Introductionmentioning

confidence: 99%

Structural and practical identifiability of approximate metabolic network models

Berthoumieux

Kahn

Jong

et al. 2012

IFAC Proceedings Volumes

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Abstract:Parameter estimation from experimental data is a crucial problem in quantitative modeling of biochemical reaction networks. An especially important issue, raised by the complexity of the models and the challenging nature of the experimental data, is parameter identifiability. Despite several approaches proposed in the systems biology literature, no agreement exists on the analysis of structural and practical identifiability, and the relations among the two. In this paper we propose a mathematical framework for the analysis of identifiability of metabolic network models, establish basic results and methods for the structural and practical identifiability analysis of the class of so-called linlog models, and discuss the results on the basis of an artificial example.

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Bioreactor Scale‐Down

Palomares

Lara

Ramı́rez

2010

Encyclopedia of Industrial Biotechnology

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In this chapter, scale‐down methodology is described as a useful tool to study the effects of fluctuating conditions on large‐scale bioreactors. The scale‐down approach focuses on simulating, on the laboratory scale, the environmental conditions prevalent on the large scale and analyzing the effects of these fluctuations on cell physiology. The scale‐down approach consists of the following steps: regime analysis of the large‐scale conditions, their simulation on laboratory systems, optimization and modeling of the cultures, and the application of the results on the large scale. These steps are presented and discussed. A review of the published scale‐down studies in cell culture and microbial fermentation is also included. Finally, an overview of the most commonly used scale‐down systems for simulating environmental fluctuations is presented, emphasizing their advantages and limitations for their use on microbial or animal cells models.

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Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics

Cited by 141 publications

References 33 publications

Impact of Thermodynamic Principles in Systems Biology

Impact of Thermodynamic Principles in Systems Biology

Structural and practical identifiability of approximate metabolic network models

Bioreactor Scale‐Down

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