A model for growth of <i>Saccharomyces cerevisiae</i> containing a recombinant plasmid in selective media

Sardonini, Charles A.; DiBiasio, David

doi:10.1002/bit.260290410

Cited by 38 publications

(17 citation statements)

References 17 publications

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“…In order to convert metabolite or protein data measured per OD unit (as for example reported in [21], [22], [23]) into intracellular molar concentrations, often a fixed conversion is assumed for different growth conditions [7], [24]. In doing so, the changes in the cells' sizes and the OD-specific cell concentration associated with these different conditions are disregarded.…”

Section: Resultsmentioning

confidence: 99%

Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling

2011

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Systems biology modeling typically requires quantitative experimental data such as intracellular concentrations or copy numbers per cell. In order to convert population-averaging omics measurement data to intracellular concentrations or cellular copy numbers, the total cell volume and number of cells in a sample need to be known. Unfortunately, even for the often studied model bacterium Escherichia coli this information is hardly available and furthermore, certain measures (e.g. cell volume) are also dependent on the growth condition. In this work, we have determined these basic data for E. coli cells when grown in 22 different conditions so that respective data conversions can be done correctly. First, we determine growth-rate dependent cell volumes. Second, we show that in a 1 ml E. coli sample at an optical density (600 nm) of 1 the total cell volume is around 3.6 µl for all conditions tested. Third, we demonstrate that the cell number in a sample can be determined on the basis of the sample's optical density and the cells' growth rate. The data presented will allow for conversion of E. coli measurement data normalized to optical density into volumetric cellular concentrations and copy numbers per cell - two important parameters for systems biology model development.

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Section: Resultsmentioning

confidence: 99%

Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling

2011

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“…For recombinant S. cerevisiae strains, reductions in the maximum specific growth rate, biomass yield, respiratory capacity, and stability of the recombinant plasmid, due to heterologous gene expression, have been observed (Da Silva and Bailey, 1991;Dequin and Barre, 1994;Gopal et al, 1989;Giuseppin et al, 1993;Janes et al, 1990;Marquet et al, 1987;Nacken et al, 1996;Sardonini and DiBiasio, 1987;Srienc et al, 1986). A decrease in the flux through glycolysis of the host strain (Snoep et al, 1995;Van Hoek et al, 1998) and an increase in the maintenance energy requirement (Bhattacharya and Dubey, 1995;Stouthamer and Van Verseveld, 1987) have also been observed.…”

Section: Introductionmentioning

confidence: 87%

The metabolic burden of the PGK1 and ADH2 promoter systems for heterologous xylanase production by Saccharomyces cerevisiae in defined medium

Görgens

Zyl

Knoetze

et al. 2001

Biotech & Bioengineering

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Five recombinant S. cerevisiae strains were cultivated under identical conditions to quantify the molecular basis of the metabolic burden of heterologous gene expression, and to evaluate mechanisms for the metabolic burden. Two recombinant S. cerevisiae strains, producing Trichoderma reesei xylanase II under control of either the PGK1 or ADH2 promoters, were compared quantitatively with three references strains, where either the heterologous xylanase II (XYN2) gene, or the heterologous gene and the promoter and terminator were omitted from the recombinant plasmid. Neither the replication of multiple copies of the 2-microm-based YEp352 plasmid nor the replication the foreign XYN2 gene represented a metabolic burden to the cell, as the growth of the host organism was not affected. The inclusion of a glycolytic promoter on the recombinant plasmid, however, reduced the maximum specific growth rate (12% to 15%), biomass yield on glucose (8% to 11%), and specific glucose consumption rate (6% to 10%) of the recombinant strains. The presence of the heterologous XYN2 gene on the recombinant plasmid caused a further reduction in the maximum specific growth rate (11% to 14%), biomass yield (4%), and specific glucose consumption rate (12%) of the host strain during active gene expression, which was dictated by the regulatory characteristics of the promoter utilized. The metabolic effect of foreign gene expression was disproportionally large, with respect to on the amount of heterologous protein produced. This was most likely due to an increased energetic demand for the expression of a foreign gene and/or a competition for limiting amounts of transcription or translation factors, biosynthetic precursors or metabolic energy.

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“…The yeast, Saccharomyces cerevisiae, has been an important workhorse in industrial biotechnology and many valuable pharmaceutical proteins have been expressed in this host. Since the early 1980's, several mathematical models have been proposed to describe plasmid instability kinetics of recombinant S. cerevisiae [1,2,3,4]. These models often described plasmid loss without considering the metabolic pathways.…”

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confidence: 99%

Mathematical model for aerobic culture of a recombinant yeast

Zhang

Scharer

Moo‐Young

1997

Bioprocess Engineering

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A mathematical model was formulated to simulate cell growth, plasmid loss and recombinant protein production during the aerobic culture of a recombinant yeast S. cerevisiae. Model development was based on three simpli®ed metabolic events in the yeast: glucose fermentation, glucose oxidation and ethanol oxidation. Cell growth was expressed as a composite of these metabolic events. Their contributions to the total speci®c growth rate depended on the activities of the pacemaker enzyme pools of the individual pathways. The pacemaker enzyme pools were regulated by the speci®c glucose uptake rate. The effect of substrate concentrations on the speci®c growth rate was described by a modi®ed Monod equation. It was assumed that recombinant protein formation is only associated with oxidative pathways. Plasmid loss kinetics was formulated based on segregational instability during cell division by assuming constant probability of plasmid loss. Experiments on batch fermentation of recombinant S. cerevisiae C468/pGAC9 (ATCC 20690), which expresses Aspergillus awamori glucoamylase gene and secretes glucoamylase into the extracellular medium, were carried out in an airlift bioreactor in order to evaluate the proposed model. The model successfully predicted the dynamics of cell growth, glucose consumption, ethanol metabolism, glucoamylase production and plasmid instability. Excellent agreement between model simulations and our experimental data was achieved. Using published experimental data, model agreement was also found for other recombinant yeast strains. In general, the proposed model appears to be useful for the design, scale-up, control and optimization of recombinant yeast bioprocesses.

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A model for growth of Saccharomyces cerevisiae containing a recombinant plasmid in selective media

Cited by 38 publications

References 17 publications

Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling

Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling

The metabolic burden of the PGK1 and ADH2 promoter systems for heterologous xylanase production by Saccharomyces cerevisiae in defined medium

Mathematical model for aerobic culture of a recombinant yeast

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