Modeling of Inducible Enzyme Biosynthesis in Microbial Cells

Kaushik, Krishna; Condo, Shinichiro; Venκatasubramanian, Κ.

doi:10.1111/j.1749-6632.1979.tb14151.x

Cited by 10 publications

(1 citation statement)

References 18 publications

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“…Others are self-confessed simulations, hardly aimed at anything other than curve-fitting (Heijnen & Roels, 1979;Koga et al, 1967). At the mechanistic level, Van Dedem & Moo-Young (1973) describe an inductor-repressor model, and Kaushik, Gondo, & Venkatasubramanian (1979) describe catabolite repression. In both these cases, enzyme synthesis happens concurrently with bacterial growth, and also depends on it.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model of Exoprotein Production in Bacteria

Coleman

Fowler

1984

Math Med Biol

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We present a simple mathematical model for the synthesis of extracellular proteins by a class of bacteria which secrete significant quantities of this exoprotein in late-exponential and stationary phases. This model is the simplest generalization of Michaelis-Menten kinetics (the Monod model) and agrees well with laboratory experiments in batch culture. The model may serve as a simple prototype for the analysis of certain virulent bacterial infections in vivo, particularly that of Pseudomonas aeruginosa in burn wounds.

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Section: Introductionmentioning

confidence: 99%

A Mathematical Model of Exoprotein Production in Bacteria

Coleman

Fowler

1984

Math Med Biol

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Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters

Moisset

Vaisman

Cintolesi

et al. 2012

Biotech & Bioengineering

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A continuous model of a metabolic network including gene regulation to simulate metabolic fluxes during batch cultivation of yeast Saccharomyces cerevisiae was developed. The metabolic network includes reactions of glycolysis, gluconeogenesis, glycerol and ethanol synthesis and consumption, the tricarboxylic acid cycle, and protein synthesis. Carbon sources considered were glucose and then ethanol synthesized during growth on glucose. The metabolic network has 39 fluxes, which represent the action of 50 enzymes and 64 genes and it is coupled with a gene regulation network which defines enzyme synthesis (activities) and incorporates regulation by glucose (enzyme induction and repression), modeled using ordinary differential equations. The model includes enzyme kinetics, equations that follow both mass‐action law and transport as well as inducible, repressible, and constitutive enzymes of metabolism. The model was able to simulate a fermentation of S. cerevisiae during the exponential growth phase on glucose and the exponential growth phase on ethanol using only one set of kinetic parameters. All fluxes in the continuous model followed the behavior shown by the metabolic flux analysis (MFA) obtained from experimental results. The differences obtained between the fluxes given by the model and the fluxes determined by the MFA do not exceed 25% in 75% of the cases during exponential growth on glucose, and 20% in 90% of the cases during exponential growth on ethanol. Furthermore, the adjustment of the fermentation profiles of biomass, glucose, and ethanol were 95%, 95%, and 79%, respectively. With these results the simulation was considered successful. A comparison between the simulation of the continuous model and the experimental data of the diauxic yeast fermentation for glucose, biomass, and ethanol, shows an extremely good match using the parameters found. The small discrepancies between the fluxes obtained through MFA and those predicted by the differential equations, as well as the good match between the profiles of glucose, biomass, and ethanol, and our simulation, show that this simple model, that does not rely on complex kinetic expressions, is able to capture the global behavior of the experimental data. Also, the determination of parameters using a straightforward minimization technique using data at only two points in time was sufficient to produce a relatively accurate model. Thus, even with a small amount of experimental data (rates and not concentrations) it was possible to estimate the parameters minimizing a simple objective function. The method proposed allows the obtention of reasonable parameters and concentrations in a system with a much larger number of unknowns than equations. Hence a contribution of this study is to present a convenient way to find in vivo rate parameters to model metabolic and genetic networks under different conditions. Biotechnol. Bioeng. 2012;109: 2325–2339. © 2012 Wiley Periodicals, Inc.

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