Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Biological and Environmental Sciences


Cell and Molecular Biology


Cell & Molecular Biology

First Advisor

Ying Zhang


The study of bacterial growth has highlighted the importance of metabolism in how microorganisms have evolved and how they survive in different conditions. The introduction of next-generation sequencing methods has allowed for the study of metabolism in new ways by predicting metabolic phenotypes based on gene annotations. The development of GEnome-scale models (GEMs) of metabolism from these annotations has provided another method to investigate microbial metabolism. An overview of GEM development and simulation methods is provided in Appendix I.

In Manuscript I, a GEM was developed for the psychrotolerant, piezotolerant, deep-sea bacteria Shewanella piezotolerans WP3. Despite the broad differences in environmental adaptations across the Shewanella genus, the WP3 model provided evidence of conserved energy production strategies that may contribute to these organisms’ ability to survive in a broad range of environments. This model’s application to study energy metabolism in WP3 demonstrated the utility of these models in the study of non-model organisms.

The mechanisms of environmental adaptation were further explored in Manuscript II, where a GEM of the psychrotolerant, deep-sea bacteria Shewanella psychrophila WP2, was used to investigate metabolic changes that occur during acclimation to different growth temperatures. This study combined transcriptomic analysis with an integrated modeling approach to provide a complete illustration of the changes that occur in various metabolic pathways at different temperatures. WP2 exhibited many of the common transcriptional responses to temperature seen in other psychrophiles, and simulations with the GEM illustrated how these changes resulted in changes in energy and biomass production efficiency at non-optimal temperatures.

In Manuscript III, the concept of using metabolic reconstructions to study metabolism was extended from single organisms to the prokaryotic tree of life. In this study, the metabolic pathways of all prokaryotic organisms in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were analyzed as mathematical networks. Previously used network representation methods were compared to a new type of network generated through the use of the FindPrimaryPairs algorithm. The application of this algorithm provided insights into the influence of carbon, nitrogen, and phosphorus transfers in the metabolism. Further network analyses using networks generated from published GEMs also highlighted differences in the structure of core metabolic pathways required for growth versus non-essential pathways.

In conclusion, these studies have highlighted the use of GEMs and metabolic networks in the study of metabolism. The studies revealed metabolic features that play critical roles in the adaptations of Shewanella to different environments and may have broader implications for other deep-sea microorganisms. The broader context of metabolism structures across prokaryotes provided through a network-based approach shows promise in advancing the understanding of how metabolic pathways are organized and evolved.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.



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