Browsing by Subject "metabolic engineering"

Now showing 1 - 2 of 2
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    Physical and Biochemical Strategies for Improving the Yield and Material Properties of Polyhydroxyalkanoate Biopolymers
    (2014-10) Barrett, John
    Polyhydroxyalkanoates (PHAs) are a diverse class of microbially synthesized biopolymers that are valued for their synthesis from renewable feedstocks and rapid biodegradation. As such, the commercial development of PHA plastics has potential to reduce the environmental impact of many, current polymers, which are non-biodegradable and rely on the use of unsustainable petroleum feedstocks. But despite the desirable traits of PHAs, the proliferation of these materials into commercial markets remains slow. Part of this is due to the greater cost of the renewable substrates used for PHA production versus the artificially low cost of petroleum-derived feedstocks. The other part of the challenge of promoting PHA utilization owes to the relatively limited diversity of physical and mechanical properties of PHAs that are currently available. As such, additional work is needed to develop new PHAs, which can satisfy the performance characteristics of many polymers already in use. Motivated by these two main challenges, 1.) to lower the production cost of PHAs and 2.) to broaden the range of unique PHAs materials available, the thesis presented herein details the development of new technologies to increase the substrate-to-product yield of PHA production and to expand the range of physical and mechanical properties of PHA-based materials. Chapter1 gives a broad introduction to polyhydroxyalkanoates and discuss various aspects of their production and application. Chapter 2 highlights the value of block-copolymers as a rich source for scientific discovery and technological development of PHAs. Methods are detailed in Chapter 3. The experimental results are presented in Chapters 4, 5, and 6, which focus generally upon: 4.) production of PHA copolymers in recombinant E. coli , 5.) fabrication and testing of PHA-graphene nanocomposites and 6.) production of PHA copolymers and block-copolymers directly from CO2 using Ralstonia eutropha. Finally, conclusions and prospects for future PHA research and development are given in Chapter 7. Taken all together, this thesis provides a solid foundation in theory and practice, for several technological approaches, which have great potential
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    Structure in Biological Networks: Comparative Analyses and Novel Interpretations
    (2024-05) Jones, Victoria
    The complexity and size of biological systems often necessitates network-based analyses; however, tools from network science, such as community detection, have seldom been well-characterized for such systems. In this work, this characterization was conducted for two types of biological systems: metabolic networks and functional human brain networks. For the former, community detection methods of different mathematical bases were applied to genome scale metabolic models of E. coli and K. pneumoniae. Communities were detected for both pruned and unaltered versions of two graph representations of the metabolic networks: the metabolite graph and the reaction adjacency graph. A comprehensive comparative analysis revealed three primary insights: 1. quantitative metrics which summarize community structure, such as modularity, do not reflect community membership, which varies greatly for communities detected via different methods; 2. these same metrics can be consistent across methods, yet vary with network representation, pruning method, and, in some cases, organism; and 3. probabilistic methods show promise for functional module detection, unlike methods of other formulations. These insights were used to inform an investigation of how network structure relates to antibiotic resistance. Statistically significant correlations with notable method/metric/representation dependence were identified, exemplifying not only the capacity for community detection methods to reveal fundamental relationships between network structure and clinically relevant phenotypes, but also the importance of applying such methods in an informed fashion. For the latter, similarly disparate community detection methods were applied to both real, statistically thresholded connectivity matrices representing the functional brain networks of over 5,000 adolescent subjects and data-driven synthetic brain networks. Comparative analyses revealed that statistically significant relationships between demographic/behavioral metadata and quantitative metrics for detected partitions vary by method. Moreover, in the case of synthetic brain networks, partitions detected by probabilistic methods were most similar to ground truth partitions. These probabilistic methods were subsequently shown to be promising avenues for characterizing hierarchical organization in the brain, investigating the evolution of said hierarchy with neural maturation, and reconstructing a consensus network based on multiple subjects. In summary, comprehensive comparisons of community detection methods were conducted for two distinct biological systems. Method dependence was revealed in both cases, and these methods were subsequently applied in informed, novel ways, elucidating key relationships between network structure and biological function.