The Experts below are selected from a list of 131001 Experts worldwide ranked by ideXlab platform
Katrin M. Meyer - One of the best experts on this subject based on the ideXlab platform.
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Modeling Microbial Growth and dynamics
Applied Microbiology and Biotechnology, 2015Co-Authors: Daniel S. Esser, Johan H. J. Leveau, Katrin M. MeyerAbstract:Modeling has become an important tool for widening our understanding of Microbial Growth in the context of applied microbiology and related to such processes as safe food production, wastewater treatment, bioremediation, or microbe-mediated mining. Various modeling techniques, such as primary, secondary and tertiary mathematical models, phenomenological models, mechanistic or kinetic models, reactive transport models, Bayesian network models, artificial neural networks, as well as agent-, individual-, and particle-based models have been applied to model Microbial Growth and activity in many applied fields. In this mini-review, we summarize the basic concepts of these models using examples and applications from food safety and wastewater treatment systems. We further review recent developments in other applied fields focusing on models that explicitly include spatial relationships. Using these examples, we point out the conceptual similarities across fields of application and encourage the combined use of different modeling techniques in hybrid models as well as their cross-disciplinary exchange. For instance, pattern-oriented modeling has its origin in ecology but may be employed to parameterize Microbial Growth models when experimental data are scarce. Models could also be used as virtual laboratories to optimize experimental design analogous to the virtual ecologist approach. Future Microbial Growth models will likely become more complex to benefit from the rich toolbox that is now available to Microbial Growth modelers.
Dan Selisteanu - One of the best experts on this subject based on the ideXlab platform.
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Modeling of Microbial Growth bioprocesses: Equilibria and stability analysis
International Journal of Biomathematics, 2016Co-Authors: Monica Roman, Dan SelisteanuAbstract:The paper addresses the analysis of nonlinear dynamical models of some Microbial Growth processes. Equilibrium points, stability analysis, and structural properties are studied for different bioprocesses with various kinetics structures. First, a simple micro-organism Growth process on a single limiting substrate is widely analyzed. Second, a Microbial Growth process combined with an enzyme-catalyzed reaction is investigated. The analysis shows that these kinds of bioprocesses have multiple equilibria, stable or unstable, operational or non-operational. The partition of nonlinear model in linear and nonlinear parts via some structural properties leads to kinetic decoupling and facilitates the equilibria and stability analysis. The performed research is useful for model reduction and for the design of observers and control algorithms. To illustrate the study results, several numerical simulations are provided.
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Modeling of Microbial Growth bioprocesses — Equilibria and stability analysis
International Journal of Biomathematics, 2016Co-Authors: Monica Roman, Dan SelisteanuAbstract:The paper addresses the analysis of nonlinear dynamical models of some Microbial Growth processes. Equilibrium points, stability analysis, and structural properties are studied for different bioprocesses with various kinetics structures. First, a simple micro-organism Growth process on a single limiting substrate is widely analyzed. Second, a Microbial Growth process combined with an enzyme-catalyzed reaction is investigated. The analysis shows that these kinds of bioprocesses have multiple equilibria, stable or unstable, operational or non-operational. The partition of nonlinear model in linear and nonlinear parts via some structural properties leads to kinetic decoupling and facilitates the equilibria and stability analysis. The performed research is useful for model reduction and for the design of observers and control algorithms. To illustrate the study results, several numerical simulations are provided.
James K. Mitchell - One of the best experts on this subject based on the ideXlab platform.
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The influence of six pharmaceuticals on freshwater sediment Microbial Growth incubated at different temperatures and UV exposures
Biodegradation, 2012Co-Authors: Allison Veach, Melody J. Bernot, James K. MitchellAbstract:Pharmaceutical compounds have been detected in freshwater for several decades. Once they enter the aquatic ecosystem, they may be transformed abiotically (i.e., photolysis) or biotically (i.e., Microbial activity). To assess the influence of pharmaceuticals on Microbial Growth, basal salt media amended with seven pharmaceutical treatments (acetaminophen, caffeine, carbamazepine, cotinine, ibuprofen, sulfamethoxazole, and a no pharmaceutical control) were inoculated with stream sediment. The seven pharmaceutical treatments were then placed in five different culture environments that included both temperature treatments of 4, 25, 37°C and light treatments of continuous UV-A or UV-B exposure. Microbial Growth in the basal salt media was quantified as absorbance (OD_550) at 7, 14, 21, 31, and 48d following inoculation. Microbial Growth was significantly influenced by pharmaceutical treatments ( P
Kevin C. Marshall - One of the best experts on this subject based on the ideXlab platform.
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Modeling pH Effects on Microbial Growth: A Statistical Thermodynamic Approach
Biotechnology and Bioengineering, 1998Co-Authors: Yunhu Tan, Zhi-xin Wang, Kevin C. MarshallAbstract:This paper applies a statistical thermodynamic approach to the kinetics of Microbial Growth influenced by pH. A general equation is developed and shown to provide a good theoretical basis for the existing pH models that have been widely used to describe the effects of pH on Microbial Growth kinetics. Four experimental data sets are used to test the general equation developed. The four data sets exhibited a variety of functional curve shapes, for example, symmetrical and asymmetrical bell-shaped, when the specific Growth rate of microorganisms is plotted as a function of pH. All four data sets are found to be well represented by the general equation. The existing pH model was, however, found to represent only one out of four data sets, i.e., the symmetrical case.
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Modeling substrate inhibition of Microbial Growth
Biotechnology and Bioengineering, 1996Co-Authors: Yunhu Tan, Zhi-xin Wang, Kevin C. MarshallAbstract:This article presents a general equation for substrate inhibition of Microbial Growth using a statistical thermodynamic approach. Existing empirical models adapted from enzyme kinetics, for example, the Haldane-Andrews equation, often criticized for not being physically based for Microbial Growth, are shown to derive from the general equation in this article, and their empirical parameters are shown to be well defined physically. Three sets of experimental data from the literature are used to test the modeling abilities of the general equation to represent experimental data. The results are compared with those obtained by fitting the same data set to a widely used empirical model existing in the literature. The general equation is found to represent all three experimental data sets better than the alternative model tested. In addition, a graphical method existing in enzyme kinetics is successfully adapted and further developed to determine the number of inhibition sites of a basic functional unit of a bacterial cell.
Daniel S. Esser - One of the best experts on this subject based on the ideXlab platform.
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Modeling Microbial Growth and dynamics
Applied Microbiology and Biotechnology, 2015Co-Authors: Daniel S. Esser, Johan H. J. Leveau, Katrin M. MeyerAbstract:Modeling has become an important tool for widening our understanding of Microbial Growth in the context of applied microbiology and related to such processes as safe food production, wastewater treatment, bioremediation, or microbe-mediated mining. Various modeling techniques, such as primary, secondary and tertiary mathematical models, phenomenological models, mechanistic or kinetic models, reactive transport models, Bayesian network models, artificial neural networks, as well as agent-, individual-, and particle-based models have been applied to model Microbial Growth and activity in many applied fields. In this mini-review, we summarize the basic concepts of these models using examples and applications from food safety and wastewater treatment systems. We further review recent developments in other applied fields focusing on models that explicitly include spatial relationships. Using these examples, we point out the conceptual similarities across fields of application and encourage the combined use of different modeling techniques in hybrid models as well as their cross-disciplinary exchange. For instance, pattern-oriented modeling has its origin in ecology but may be employed to parameterize Microbial Growth models when experimental data are scarce. Models could also be used as virtual laboratories to optimize experimental design analogous to the virtual ecologist approach. Future Microbial Growth models will likely become more complex to benefit from the rich toolbox that is now available to Microbial Growth modelers.