Deterministic and Semi-Mechanistic Approaches in Predictive Fermentation Microbiology

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dc.contributor.advisor Marcia L. Gumpertz, Committee Member en_US
dc.contributor.advisor Zhilin Li, Committee Member en_US
dc.contributor.advisor Frederick Breidt Jr., Committee Co-Chair en_US
dc.contributor.advisor Sharon R. Lubkin, Committee Chair en_US
dc.contributor.author Dougherty, Daniel Patrick en_US
dc.date.accessioned 2010-04-02T18:42:33Z
dc.date.available 2010-04-02T18:42:33Z
dc.date.issued 2002-10-10 en_US
dc.identifier.other etd-06262002-111915 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/4019
dc.description.abstract Predictive fermentation microbiology utilizes deterministic and stochastic mathematical models to study the growth dynamics of microorganisms. If the components of such models represent known or hypothesized biological growth processes then these models can be used to refine existing hypotheses or generate new hypotheses about the factors controlling growth. Special techniques must be used when fitting such models to experimental data. Methods are suggested for model re-parameterization and model fitting which improve the estimation of model parameters. Once estimates of model parameters have been made, temporal and multivariate sensitivity analyses can assess important relationships among the model parameters. A deterministic dynamic model of batch growth by a homofermentative lactic acid bacterium growing in a variable temperature environment was derived. This model predicts cell growth as well as changes in the chemical composition of the medium. This model was fit to experimental data. Analysis of the model revealed a quantitative reversal in parameter sensitivities across temperatures. Although mechanistic, this model neglected the effects of pH, organic acid dissociation and ionic strength of the medium. It is shown that these chemical dynamics are important and can be modeled through a convenient semi-mechanistic approach. The ability to model these chemical dynamics appropriately allows for a modeling framework in which the acid tolerance strategies commonly exhibited by bacteria can be studied. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject variable temperature en_US
dc.subject bacterial growth en_US
dc.subject semi-mechanistic en_US
dc.subject lag phase en_US
dc.subject mathematical modeling en_US
dc.title Deterministic and Semi-Mechanistic Approaches in Predictive Fermentation Microbiology en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Biomathematics en_US


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