Cell cycle analysis of plasmid‐containing <i>Escherichia coli</i> HB101 populations with flow cytometry

Seo, Jin‐Ho; Bailey, James E.

doi:10.1002/bit.260300221

Cited by 27 publications

(8 citation statements)

References 33 publications

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“…We have demonstrated here a significant application of cell cycle population modeling to product formation. Since Fredrickson et al (1967) laid down the basic mathematical framework, there have been subsequent elegant analyses with respect to prokaryote populations and plasmidshedding problems Bailey, 1982, 1983;Nishimura and Bailey, 1980;Seo and Bailey, 1987). Notwithstanding, most works are restricted to balanced growth and lack a product formation model.…”

Section: Discussionmentioning

confidence: 99%

Transition probability cell cycle model with product formation

Cain

Chau

1998

Biotechnol. Bioeng.

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A cell cycle population model based on the transition probability model of Smith and Martin (1973) has been extended to include product synthesis and export. The model handles two probable mechanisms. In the direct production model, the product is the protein. In the transcription model, the product is the specific mRNA. The protein is synthesized by translation of the specific mRNA and subsequently exported. In either case, the cell density is jointly distributed in the primary product and maturity age in the cell cycle. This extended model also is capable of describing a large range of conditions, including substrate dependent batch and continuous cultures. With the use of unity maturity‐velocity (but the transition rate a function of limiting substrate), the model is shown to exhibit a negative growth association between the specific productivity of monoclonal antibodies from hybridomas and the dilution rates of a chemostat. Possibilities of maturity age dependent transcription and translation are considered, and the results show that these features can amplify the specific productivity negative association with specific growth rate. While this model may provide a partial elucidation of monoclonal antibody productivity in a chemostat, the present work provides a proper framework with which probable cell cycle dependent product formation can be analyzed rigorously with a comprehensive computational model. © 1998 John Wiley & Sons, Inc. Biotechnol Bioeng 58:387–399, 1998.

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Section: Discussionmentioning

confidence: 99%

Transition probability cell cycle model with product formation

Cain

Chau

1998

Biotechnol. Bioeng.

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“…FCM has been used to analyse the behaviour of cell populations in fermenters or bioreactors in various fermentation systems such as batch, fed-batch or continuous cultivation under different growth conditions. The physiological behaviour of several industrial microorganisms, with or without recombinant plasmids, has been analysed: the bacteria Escherichia coli [36, 71,170,172], Klebsiella pneumoniae [36], and Micrococcus luteus [99], the yeasts Saccharomyces cerevisiae [4, 118,129,171,193,208], Kluyveromyces lactbs [ 157], and Hansenula polymorpha [37], and the green algae Chlorella vulgaris [97,98]. Some unrecognized aspects of the physiology of microorganisms, such as cellular heterogeneity, have come to light.…”

Section: Biotechnologymentioning

confidence: 99%

Recent advances of flow cytometry in fundamental and applied microbiology

Fouchet

Jayat

Héchard³

et al. 1993

Biology of the Cell

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This review focuses on the recent applications of flow cytometry (FCM) in microbiological research (1987-mid 1992). It tries to give a scope of the important breakthroughs which occurred in this field during this period. The technical difficulties of microorganism analysis by flow cytometry is briefly appraised. The significance and the limits of the different microbial cell parameters attainable by flow analyses are systematically evaluated: light scatter for cell size and structure, fluorescence measurements for quantification of cellular components, microbial antigen detection and cell physiological activity estimation. Emphasis is given on the new technological advances which appeared in the last two years. The second part of the review is devoted to the analysis of the usefulness of flow cytometric approach in the different fields of microbiology: fundamental studies in microbial physiology, differentiation, microbial ecology and aquatic sciences, medical microbiology, parasitology, microbial pharmacology and biotechnology.

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“…Peretti's model explicitly calculates the quantity of actively transcribing RNA polymerase as well as the quantity of actively translating ribosomes in the cell and includes, in the case of plasmid bearing cells, the inuence of plasmids on these quantities. This model describes well the in¯uence of the plasmids on the cell parameters [31,40,41]. Later on, the in¯uence of the plasmid copy number on the decreased cloned gene stability was included into the model as well [32].…”

Section: Introductionmentioning

confidence: 98%

Mathematical simulation of the growth of a three plasmid harboring Escherichia coli JM109 strain and the production of the fusion protein EcoRI::SPA with a four-compartment model

Rhee

Schügerl

1998

Bioprocess Engineering

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In the previous papers [35,36] the process variables of plasmid-free, one-, two-and three-plasmid harboring E. coli JM109 cells were investigated in batch and continuous cultivation as a function of the medium composition, plasmid content, dilution rate and cultivation (generation) time. In the present paper the growth of the recombinant E. coli JM109 [pEcoR4, pRK248cI, pMTC48] and the production of the fusion protein EcoRI::SPA are simulated by using a four-compartment model, consisting of the active cell components (ribosomes, mRNA, tRNA, and others) (A), the structure forming materials and chromosomal DNA (Z), the plasmid-DNA (G) and the recombinant enzyme protein (E). At the ®rst time, all of the three plasmids: the production plasmid (Gp), the repressor plasmid (Gr) and the protection plasmid (Gs) are taken into account in the plasmid DNA-compartment of the model. The calculated and measured courses of the cell mass, the concentrations of glucose and acetate, and the products as well as the particular plasmids agree well. List of symbolsA active cell compartment [g (g X) A1 ] Ac acetate concentration [g l A1 ] E enzyme protein compartment; Ep for fusion protein, Er for repressor protein, Es for methylase [g (g X) A1 ] F¯ow rate [l h A1 ] G plasmid-DNA compartment; Gp for production plasmid, Gr for repressor plasmid, Gs for protection plasmidextracellular substrate concentration [g l A1 ] S Ã intracellular substrate concentration [g l A1 ] t cultivation time [h] t f the time ®lled up the second reactor with medium [h] V reactor volume [l] X cell mass [g l A1 ] X i intracellular concentration of the i-th component [g (g X) A1 ] y A,Z stoichiometric coef®cients for the formation of components Z compartment for structure forming material [g (g X) A1 ] l speci®c growth rate [h A1 ] g stoichiometric coef®cients for acetate formation x segregation coef®cient of the production plasmid 1 index for the ®rst reactor 2 index for the second reactor 20 index for the¯ow of induction medium to the second reactor + exponent for the three plasmids harbouring cells A exponent for the two plasmids harbouring cells 1 Introduction Several researcher developed mathematical models for the description of the growth and host plasmid interaction in recombinant E. coli [13, 38]. One can distinguish between small and large structured and population models [2]. In small structured models the cell mass and biological properties of the cells lumped into few compartments. A four-compartment model was presented by Nielsen et al. [24], which was later improved [26] based on the lactobacillus model [25]. An eight-compartment model was formulated by Bentley and Kompala [3]. Large scale computer models containing many variables and parameters represent an attempt to calculate, in systematic and coordinated fashion, the consequences of many simultaneous interactions within the cell, sometimes on the known molecular mechanism [2]. Such models were developed by Lee and Bailey [16±19, 21]. These models make it possible to simulate the cells growth, ...

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Cell cycle analysis of plasmid‐containing Escherichia coli HB101 populations with flow cytometry

Cited by 27 publications

References 33 publications

Transition probability cell cycle model with product formation

Transition probability cell cycle model with product formation

Recent advances of flow cytometry in fundamental and applied microbiology

Mathematical simulation of the growth of a three plasmid harboring Escherichia coli JM109 strain and the production of the fusion protein EcoRI::SPA with a four-compartment model

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