Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.681878
Title: Modeling bacterial dynamics in chemostats
Author: Nnaji, Chioma Frances Agatha
ISNI:       0000 0004 5922 1318
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 2016
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Abstract:
Clean water is a vital resource, which climate change and population growth conspire to make increasingly scarce. Thus it is imperative that we maintain the quality of our watercourses and recycle polluted waters. Biological treatment of wastewater is at the forefront of current strategies employed to treat domestic wastewater. The transformation of wastewater into less harmful products is performed by complex naturally forming microbial communities. Waste treatment processes are essentially a product of the ecology of these communities and yet we have a poor understanding of some of the most basic ecological processes. In particular, the coming together, or assembly of the community. Thus when failure occurs it can be baffling. In the last decade, there has been growing evidence that stochastic processes are an important component of microbial community assembly. In open biological communities, if species are functionally equivalent and it is only stochasticity that shapes the community then the dynamics are said to be neutral. It is then important to ascertain the relative importance of stochasticity in engineered systems and on that basis formulate theories to guide the successful design and operation of wastewater treatment plants. Neutral community assembly theory has been in the limelight for more than a decade because of its ability to predict species area relationship and species abundance distribution. Its underlying assumption of equivalent specific growth rates is controversial and has led to much debate, which is muddied by sampling issues, parameter estimation techniques and lack of data. In this thesis, I attempt to address these confounding factors and properly parameterize the stochastic model originally postulated by Hubbell (Hubbell, 2001) and subsequently modified by Sloan (Sloan et al., 2006) to suit microbes. This is done by conducting detailed experiments in parallel chemostats to give time series of the abundance of organisms that have been engineered to have highly tunable kinetics. Calibration using time series data affords the first opportunity to validate neutral (and near neutral) dynamics. Previous studies only use stationary abundance distributions that could have emerged from a variety of alternative mechanisms. The organisms with tunable kinetics were engineered by a genetic recombination technique, which generated two strains of E.coli with different antibiotic resistance genes placed at the same location in otherwise identical genomes. When no antibiotics are present these stains have identical growth characteristics and hence are neutral with respect to one another. In the presence of low concentrations of antibiotics the strain with the appropriate resistance gene has an advantage. Time series abundance of the two strains were obtained under three different experimental setups that were devised to give differing weight to neutral and selective processes. A model that incorporates selective and neutral effects could be calibrated in all cases, but the match between experimental and theoretical parameters could only be achieved if an ‘effective community size’ that is smaller than the real community size is used. Evidence is given for this being a phenomenon associated with spatial correlation in the demographics of the community. The consequences of this are profound. It means that even in very large microbial populations random drift will affect community composition and identically engineered systems will yield differing population dynamics.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.681878  DOI: Not available
Keywords: TA Engineering (General). Civil engineering (General)
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