Generalized differential specific rate equation for microbial growth

Kargı, Fikret; Shuler, Michael L.

doi:10.1002/bit.260211014

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…, where g is the net growth rate, s is the concentration of the limiting metabolite, gmax is the maximal growth rate, and KM is the concentration of s at which half gmax is achieved. Other growth models such as the Teisser and the Contois 5 models have also been proposed. However, like the Monod model, they do not consider cell death since they assume zero (instead of negative) growth rate at zero metabolite concentration.…”

Section: Introductionmentioning

confidence: 99%

Microscopy quantification of microbial birth and death dynamics

Hart

Skelding

Waite

et al. 2018

Preprint

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Microbes live in dynamic environments where nutrient concentrations fluctuate. Quantifying fitness (birth and death) in a wide range of environments is critical for understanding microbial evolution as well as ecological interactions where one species alters the fitness of another. Here, using high-throughput time-lapse microscopy, we have quantified how Saccharomyces cerevisiae mutants incapable of synthesizing an essential metabolite grow or die in various concentrations of the required metabolite. We establish that cells normally expressing fluorescent proteins lose fluorescence upon death and that the total fluorescence in an imaging frame is proportional to the number of live cells even when cells form multiple layers. We validate our microscopy approach of measuring birth and death rates using flow cytometry, cell counting, and chemostat culturing. For lysine-requiring cells, very low concentrations of lysine are not detectably consumed and do not support cell birth, but delay the onset of death phase and reduce the death rate. In contrast, in low hypoxanthine, hypoxanthine-requiring cells can produce new cells, yet also die faster than in the absence of hypoxanthine. For both strains, birth rates under various metabolite concentrations are better described by the sigmoidal-shaped Moser model than the well-known Monod model, while death rates depend on the metabolite concentration and can vary with time. Our work reveals how time-lapse microscopy can be used to discover non-intuitive microbial dynamics and to quantify growth rates in many environments.

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Section: Introductionmentioning

confidence: 99%

Microscopy quantification of microbial birth and death dynamics

Hart

Skelding

Waite

et al. 2018

Preprint

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“…During the tissue regeneration process cells multiple which could affect the concentration profile in the bioreactor. A variety of growth characteristics have been considered including a Monod growth kinetics and Contois equation based on microbial cultures (Kargi and Shuler, ). To account for changes in cell number, simulations, and model predictions were performed using three cell densities to obtain snap‐shots of consumption characteristics.…”

Section: Resultsmentioning

confidence: 99%

Multiple approaches to predicting oxygen and glucose consumptions by HepG2 cells on porous scaffolds in an axial‐flow bioreactor

Podichetty

Bhaskar

Singarapu

et al. 2014

Biotech & Bioengineering

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In this study, the distribution of oxygen and glucose was evaluated along with consumption by hepatocytes using three different approaches. The methods include (i) Computational Fluid Dynamics (CFD) simulation, (ii) residence time distribution (RTD) analysis using a step-input coupled with segregation model or dispersion model, and (iii) experimentally determined consumption by HepG2 cells in an open-loop. Chitosan-gelatin (CG) scaffolds prepared by freeze-drying and polycaprolactone (PCL) scaffolds prepared by salt leaching technique were utilized for RTD analyses. The scaffold characteristics were used in CFD simulations i.e. Brinkman's equation for flow through porous medium, structural mechanics for fluid induced scaffold deformation, and advection-diffusion equation coupled with Michaelis-Menten rate equations for nutrient consumption. With the assumption that each hepatocyte behaves like a micro-batch reactor within the scaffold, segregation model was combined with RTD to determine exit concentration. A flow rate of 1 mL/min was used in the bioreactor seeded with 0.6 × 10(6) HepG2 cells/cm(3) on CG scaffolds and oxygen consumption was measured using two flow-through electrodes located at the inlet and outlet. Glucose in the spent growth medium was also analyzed. RTD results showed distribution of nutrients to depend on the surface characteristics of scaffolds. Comparisons of outlet oxygen concentrations between the simulation results, and experimental results showed good agreement with the dispersion model. Outlet oxygen concentrations from segregation model predictions were lower. Doubling the cell density showed a need for increasing the flow rate in CFD simulations. This integrated approach provide a useful strategy in designing bioreactors and monitoring tissue regeneration.

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“…Dependencia de la velocidad de crecimiento específica con respecto a : Referencia bibliográfica Concentración de sustrato [24,54,[72][73][74][75][76][77][78][79][80][81][82][83]. El modelo de Haldane es referenciado en Andrews JF [84].…”

Section: Modelos Matemáticos De Bioprocesosunclassified

Enfrentando el modelado de bioprocesos: una revisión de las metodologías de modelado

Quintana

Álvarez

Castro

2017

rev.ion

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ResumenEn este artículo se presenta una revisión detallada de las diferentes metodologías para el modelado de procesos, señalando sus deficiencias y limitaciones al aplicarlas al modelado de bioprocesos. Como resultado del análisis se encuentra que, al aplicar esas metodologías a los bioprocesos, todas fallan porque no consideran explícitamente la interacción existente entre el medio ambiente y el material celular, al menos de forma descriptiva. Se resalta que hasta ahora la forma de unir estos dos mundos ha sido a través de funciones puramente predictivas. Finalmente, se describen las tendencias en el modelado de bioprocesos, concluyéndose que el enfoque está orientado al planteamiento de modelos matemáticos de base fenomenológica, con rasgos descriptivos o explicativos, para representar la relación existente entre la célula y su medio ambiente.

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Generalized differential specific rate equation for microbial growth

Cited by 16 publications

References 5 publications

Microscopy quantification of microbial birth and death dynamics

Microscopy quantification of microbial birth and death dynamics

Multiple approaches to predicting oxygen and glucose consumptions by HepG2 cells on porous scaffolds in an axial‐flow bioreactor

Enfrentando el modelado de bioprocesos: una revisión de las metodologías de modelado

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