Browsing by Subject "systems biology"

Now showing 1 - 5 of 5
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    Complex signal regulation drives the Arabidopsis immune network’s response to bacterial flagellin stimulus
    (2016-04) Hillmer, Rachel
    Systems biology is the study of how biological systems operate as a whole. Systems become complex when interactions between system parts dominate system behavior. To uncover the mechanisms by which complex biological systems operate, those interactions must be discovered and quantified. Further, to understand dynamic system behavior, mechanistic rules for how system parts are stimulated and regulate each other must be discovered. The plant immune signaling network, which protects plants from pathogens, is an especially complex system. Pathogens disable plant immune signaling with effectors; thus plant immunity must be robust against pathogen perturbation. Thus, deciphering the mechanisms that underlie the plant immune signaling network is met by a challenge: effects of single-gene mutations, on which traditional genetic analysis depends, are also buffered by the network. In this dissertation, a network reconstitution approach was taken, where the network is disassembled and then stepwise re-assembled, to accurately assign network functions to system parts, including interactions between parts. We define the plant immune signaling network in terms of 4 major signaling sectors controlled by the plant hormones jasmonate (JA), ethylene (ET), and salicylate (SA) sectors, and the major immune regulator phytoalexin-deficient 4 (PAD4). Dynamic transcriptome and hormone profiles after plant immune stimulus with bacterial flagellin were collected across a combinatorially complete set of mutants, lacking all combinations of these four sectors. These mutant profiles were used in (1) attempts to find mechanistic mathematical models of immune network behavior and to (2) characterize the four-sector network’s control of the flg22-responsive transcriptome. The work in this dissertation produced two main discoveries. First, that delay differential equations (DDEs) can be found which provide mechanistic explanations of immune network function; additional time course detail will be needed to confirm the accuracy of these models. Second, network buffering is extensive in the flg22-responsive transcriptome. As a result of this network buffering, our network reconstitution based interpretations of gene regulation are at points quite different from the regulatory mechanisms described in the plant immunity literature.
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    Isolation, Culturing, and Nutrient Analysis of Candidatus arthromitus
    (2016-08) Reiland, Holly
    Candidatus arthromitus (CA) is a Gram positive, spore-forming segmented filamentous bacteria known to be a commensal if not symbiotic organism residing in the gastrointestinal tract. Candidatus arthromitus was first isolated and grown using the methods explained in Schnupf et al, 2015 (3). Isolated cells were cultured using brain heart infusion broth (BHI) with additional carbohydrate sources predicted by systems biology and genome-scale metabolic modeling techniques to increase growth. Methods for culturing and computational predictions are described in the materials and methods found in chapter four of this thesis. Cultured CA was used in Biolog TM assays to determine CA metabolic capabilities in comparison to in-silico predictions. Growth curves and dry cell weight experiments were completed on carbohydrate-spiked BHI broth to provide supporting evidence of CA external host viability and in vitro growth. This is the first effort to culture CA from commercial turkeys; the importance stems from an industry issue with commercial turkeys failing to reach full weight potential at an early age. This term describing this issue is Light Turkey Syndrome (LTS).
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    Modeling the dynamics of the plant immune response
    (2022-03) Liu, Xiaotong
    Dynamic modeling is essential for understanding the temporal behavior of a system. Deriving dynamic models from biological omics data can enable effective information reduction by leveraging a few interpretable parameters and capturing the hidden structure in the data. Thanks to the availability of RNA-seq, temporal transcriptomes have been widely profiled as dynamic snapshots of biological responses. My PhD study focuses on dynamic modeling of plant immunity, a plant defense response induced by pathogens. There are two well-defined modes of inducible immunity of plant to overcome pathogen attack, namely pattern triggered immunity (PTI) and effector triggered immunity (ETI). Researchers have generated rich sources of temporal transcriptome data in plants upon challenge of pathogens or pathogen derivatives during both PTI and ETI. My contribution to dynamic modeling of plant immunity comes primarily with two projects. In my main project, I developed a novel computational approach based on an ordinary differential equation system to interpreting the transcriptome dynamics during ETI. The modeling results uncovered intrigue data patterns that direct deep insights into the transcriptional regulation of transcription factors during ETI. In my other project, I developed mechanistic models based on the transcript response of CBP60g, a marker gene of pattern-triggered immunity. The model not only interpreted the dynamics of CBP60g response but also predicted the mechanistic roles of three plant immunity genes in regulating CBP60g transcription. Overall, my efforts on dynamic modeling of plant immunity bring novel mathematical frameworks for transcript/transcriptome data interpretation and derive valuable biological predictions that shed light on transcriptional mechanisms of plant immunity.
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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.
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    Systems Design and Synthetic Construction of Influenza Virus for Flu Vaccine Application
    (2021-11) Phan, Thu
    Influenza A virus (IAV) is the leading cause of annual flu epidemics, which inflicts about 250,000-500,000 deaths worldwide. The morbidity and mortality rate are much higher when a novel strain of IAV arises, resulting in flu pandemics. Vaccination has been the best prevention strategy for influenza. However, flu viruses constantly evolve and escape the established immunity, thus annual flu vaccination is required. Most current flu vaccine manufacturing platforms use multi-plasmid transfection to rescue seasonal seed viruses, the seed viruses are then used to infect either embryonic chicken eggs or cultured cells to produce viruses. Both production methods have high degrees of variability and produce viruses with a high content of non-infectious particles that reduce vaccine effectiveness. To address the need for more reliable and scalable processes, we applied systems biology and synthetic biology approaches to understand the kinetics of virus replication and to engineer cell lines that can control viral gene expression dynamics. First, we established a new data analysis pipeline using RNA sequencing to study segment-specific kinetics of all IAV RNA molecules. Using the pipeline, InVERT, to study the kinetics of IAV infection, revealed different phases of virus infection, and groups of genes whose kinetics are similar. This was the first-time IAV replication kinetics of all segments is reported. Building on that success, we then developed the second pipeline named InVERT II, which can further differentiate mRNA transcripts made by the viral replication enzyme RdRP from mRNA transcripts synthesized by host cells' RNA Polymerase II, to study the kinetics of virus rescue by transfection. With the understanding gained from the kinetics of virus infection and replication, we engineered the human cell line HEK 293T to express inducible components of IAV that not only have inducible replicative activity but also can package virus particles. This is the first proof of principle to show that mammalian cells can be engineered to produce complex negative-sense RNA viruses. Our integrative approach using both systems biology and synthetic biology has enabled the creation of a platform that could be further optimized for reliable, robust, and scalable flu vaccine manufacturing processes.