Modeling microbial population dynamics

P. R. Darrah, Models of the rhizosphere. I. Microbial population dynamics around the root releasing. soluble and in.soluble carbon. Plant Soil 733 187 (1991). 

Darrah, P.R., 1991a. Models of the rhizosphere 1. Microbial population dynamics around a root releasing soluble and insoluble carbon. Plant Soil 133, 187-199. 

Darrah, P.R., 1991b. Models of the rhizosphere II. A quasi three-dimensional simulation of the microbial population dynamics around a growing root releasing soluble exudates. Plant Sod 138, 147-158. 

Figure 5.52. Conceptual representations of the interactive model, (a) is converted to Pi by an enzyme that requires S2 as a cofactor, (b) Substrates and S2 from two parallel pathways are combined by enzyme to produce a product P that is required for growth, (c) Plots of lines of constant dimensionless specific growth rate p/Prmx as a function of two dimensionless substrate concentrations for interactive models of the Megee type (cf. Equ. 5.169) with Monod kinetics (Reprinted with permission from In Microbial Population Dynamics, Bader, 1982. Copyright CRC Press, Inc., Boca Raton, FL.)...

III. MODELING THE DYNAMICS OF MICROBIAL POPULATIONS AROUND THE ROOT... 

By now, it has not been made possible to determine the levels of antimicrobials that can cause an increase of primarily resistant Enterobacteriaceae in the gut of the consumer. As a result, measuring the microbial significance of antimicrobial residues continues to be the subject of considerable discussion. Much of the discussion involves the development of model systems that will reflect the effects of residue levels of antimicrobials on human intestinal microbial populations. The consensus of opinion at a recent symposium is that no such single system is available (64). The human intestine is a very complex microbial ecosystem, about which little is known of the effects of antimicrobial residues on the population dynamics and biochenoical responses (65). 

In the previous chapter the gradostat was introduced as a model of competition along a nutrient gradient. The case of two competitors and two vessels with Michaelis-Menten uptake functions was explored in considerable detail. In this chapter the restriction to two vessels and to Michaelis-Menten uptake will be removed, and a much more general version of the gradostat will be introduced. The results in the previous chapter were obtained by a mixture of dynamical systems techniques and specific computations that established the uniqueness and stability of the coexistence rest point. When the number of vessels is increased and the restriction to Michaelis-Menten uptake functions is relaxed, these computations are inconclusive. It turns out that unstable positive rest points are possible and that non-uniqueness of the coexistence rest point cannot be excluded. The main result of this chapter is that coexistence of two microbial populations in a gradostat is possible in the sense that the concentration of each population in each vessel approaches a positive equilibrium value. The main difference with the previous chapter is that we cannot exclude the possibility of more than one coexistence rest point. 

While some models of sulfide mineral leaching in natural or heap situations have considerable utility for the management of metal recovery operations (Harris, 1969), the complexity of such situations has so far defied efforts to construct models which accommodate all the observed phenomena, and there is a need for intensification of multi-disciplinary approaches to the overall problem. Considerably greater understanding of the interactions between microbial populations and mineral arrays in the dynamic patterns of the field context is needed before this task can be accomplished. 

Wanner, O., Debus, O. and Reichert, P. 1994. Modelling the spatial distribution and dynamics of a xylene-degrading microbial population in a membrane-bound biofilm. Water Science and Technology, 29,243-251. 

The foregoing review has started from the simplest considerations and proceeded to the analysis of more complex cases in its attempt to describe the dynamics of microbial cell populations. Some of the models discussed in the review are new others have appeared in the literature but are not generally known by chemical or biochemical engineers. At any rate, treatment of these models is not to be found in any available treatise on biochemical engineering or elsewhere in the chemical engineering literature. Possibly the most important reason for this apparent lack of concern is the feeling that the models advanced are unrealistic and not useful. 

Henson, MA. (2003) Dynamic modeling of microbial cell populations. Curr. Opin. Biotechnol., 14 (5), 460—467. 

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