+publisher:"Virginia Tech" +contributor:("Tyson, John J.")

Virginia Tech

1. Zhang, Hang. Theoretical and Computational Studies on the Dynamics and Regulation of Cell Phenotypic Transitions.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2016, Virginia Tech

► Cell phenotypic transitions, or cell fate decision making processes, are regulated by complex regulatory networks composed of genes, RNAs, proteins and metabolites. The regulation can… (more)

▼ Cell phenotypic transitions, or cell fate decision making processes, are regulated by complex regulatory networks composed of genes, RNAs, proteins and metabolites. The regulation can take place at the epigenetic, transcriptional, translational, and post-translational levels to name a few. Epigenetic histone modification plays an important role in cell phenotype maintenance and transitions. However, the underlying mechanism relating dynamical histone modifications to stable epigenetic cell memory remains elusive. Incorporating key pieces of molecular level experimental information, we built a statistical mechanics model for the inheritance of epigenetic histone modifications. The model reveals that enzyme selectivity of different histone substrates and cooperativity between neighboring nucleosomes are essential to generate bistability of the epigenetic memory. We then applied the epigenetic modeling framework to the differentiation process of olfactory sensory neurons (OSNs), where the observed 'one-neuron-one-allele' phenomenon has remained as a long-standing puzzle. Our model successfully explains this singular behavior in terms of epigenetic competition and enhancer cooperativity during the differentiation process. Epigenetic level events and transcriptional level events cooperate synergistically in the OSN differentiation process. The model also makes a list of testable experimental predictions. In general, the epigenetic modeling framework can be used to study phenotypic transitions when histone modification is a major regulatory element in the system. Post-transcriptional level regulation plays important roles in cell phenotype maintenance. Our integrated experimental and computational studies revealed such a motif regulating the differentiation of definitive endoderm. We identified two RNA binding proteins, hnRNPA1 and KSRP, which repress each other through microRNAs miR-375 and miR-135a. The motif can generate switch behavior and serve as a noise filter in the stem cell differentiation process. Manipulating the motif could enhance the differentiation efficiency toward a specific lineage one desires. Last we performed mathematical modeling on an epithelial-to-mesenchymal transition (EMT) process, which could be used by tumor cells for their migration. Our model predicts that the IL-6 induced EMT is a stepwise process with multiple intermediate states. In summary, our theoretical and computational analyses about cell phenotypic transitions provide novel insights on the underlying mechanism of cell fate decision. The modeling studies revealed general physical principles underlying complex regulatory networks. Advisors/Committee Members: Xing, Jianhua (committeechair), Tyson, John J. (committeechair), Stremler, Mark A. (committeechair), Xie, Hehuang David (committee member).

Subjects/Keywords: Mathematical Modeling; Epigenetics; Cell Differentiation; Mono-allelic expression; Epithelial-to-Mesenchymal-Transition

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APA (6^th Edition):

Zhang, H. (2016). Theoretical and Computational Studies on the Dynamics and Regulation of Cell Phenotypic Transitions. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/65159

Chicago Manual of Style (16^th Edition):

Zhang, Hang. “Theoretical and Computational Studies on the Dynamics and Regulation of Cell Phenotypic Transitions.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/65159.

MLA Handbook (7^th Edition):

Zhang, Hang. “Theoretical and Computational Studies on the Dynamics and Regulation of Cell Phenotypic Transitions.” 2016. Web. 22 Jan 2021.

Vancouver:

Zhang H. Theoretical and Computational Studies on the Dynamics and Regulation of Cell Phenotypic Transitions. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/65159.

Council of Science Editors:

Zhang H. Theoretical and Computational Studies on the Dynamics and Regulation of Cell Phenotypic Transitions. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/65159

Virginia Tech

2. Subramanian, Kartik. Spatiotemporal Model of the Asymmetric Division Cycle of Caulobacter crescentus.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2014, Virginia Tech

URL: http://hdl.handle.net/10919/65156

► The life cycle of Caulobacter crescentus is of interest because of the asymmetric nature of cell division that gives rise to progeny that have distinct… (more)

▼ The life cycle of Caulobacter crescentus is of interest because of the asymmetric nature of cell division that gives rise to progeny that have distinct morphology and function. One daughter called the stalked cell is sessile and capable of DNA replication, while the second daughter called the swarmer cell is motile but quiescent. Advances in microscopy combined with molecular biology techniques have revealed that macromolecules are localized in a non-homogeneous fashion in the cell cytoplasm, and that dynamic localization of proteins is critical for cell cycle progression and asymmetry. However, the molecular-level mechanisms that govern protein localization, and enable the cell to exploit subcellular localization towards orchestrating an asymmetric life cycle remain obscure. There are also instances of researchers using intuitive reasoning to develop very different verbal explanations of the same biological process. To provide a complementary view of the molecular mechanism controlling the asymmetric division cycle of Caulobacter, we have developed a mathematical model of the cell cycle regulatory network. Our reaction-diffusion models provide additional insight into specific mechanism regulating different aspects of the cell cycle. We describe a molecular mechanism by which the bifunctional histidine kinase PleC exhibits bistable transitions between phosphatase and kinase forms. We demonstrate that the kinase form of PleC is crucial for both swarmer-to-stalked cell morphogenesis, and for replicative asymmetry in the predivisional cell. We propose that localization of the scaffolding protein PopZ can be explained by a Turing-type mechanism. Finally, we discuss a preliminary model of ParA- dependent chromosome segregation. Our model simulations are in agreement with experimentally observed protein distributions in wild-type and mutant cells. In addition to predicting novel mutants that can be tested in the laboratory, we use our models to reconcile competing hypotheses and provide a unified view of the regulatory mechanisms that direct the Caulobacter cell cycle. Advisors/Committee Members: Tyson, John J. (committeechair), Paul, Mark R. (committeechair), Scharf, Birgit (committee member), Cao, Yang (committee member).

Subjects/Keywords: Mathematical modeling; Caulobacter cell cycle; protein regulatory networks; reaction-diffusion models

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APA (6^th Edition):

Subramanian, K. (2014). Spatiotemporal Model of the Asymmetric Division Cycle of Caulobacter crescentus. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/65156

Chicago Manual of Style (16^th Edition):

Subramanian, Kartik. “Spatiotemporal Model of the Asymmetric Division Cycle of Caulobacter crescentus.” 2014. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/65156.

MLA Handbook (7^th Edition):

Subramanian, Kartik. “Spatiotemporal Model of the Asymmetric Division Cycle of Caulobacter crescentus.” 2014. Web. 22 Jan 2021.

Vancouver:

Subramanian K. Spatiotemporal Model of the Asymmetric Division Cycle of Caulobacter crescentus. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/65156.

Council of Science Editors:

Subramanian K. Spatiotemporal Model of the Asymmetric Division Cycle of Caulobacter crescentus. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/65156

Virginia Tech

3. Mobassera, Umme Juka. Extending Regulatory Network Modeling with Multistate Species.

Degree: MS, Computer Science, 2011, Virginia Tech

URL: http://hdl.handle.net/10919/35594

► By increasing the level of abstraction in the representation of regulatory network models, we can hope to allow modelers to create models that are beyond… (more)

Subjects/Keywords: Modeling Tool; Software; JigCell; Computational Systems Biology; Multistate Species; SBML; Rule Based Modeling

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APA (6^th Edition):

Mobassera, U. J. (2011). Extending Regulatory Network Modeling with Multistate Species. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/35594

Chicago Manual of Style (16^th Edition):

Mobassera, Umme Juka. “Extending Regulatory Network Modeling with Multistate Species.” 2011. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/35594.

MLA Handbook (7^th Edition):

Mobassera, Umme Juka. “Extending Regulatory Network Modeling with Multistate Species.” 2011. Web. 22 Jan 2021.

Vancouver:

Mobassera UJ. Extending Regulatory Network Modeling with Multistate Species. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/35594.

Council of Science Editors:

Mobassera UJ. Extending Regulatory Network Modeling with Multistate Species. [Masters Thesis]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/35594

Virginia Tech

4. Morris, Matthew. Molecular mechanisms responsible for the dynamic modulation of macrophage responses to varying dosages of lipopolysaccharide.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2014, Virginia Tech

URL: http://hdl.handle.net/10919/64253

► The innate immune system depends for its effectiveness on the function of specialized pattern recognition receptors which enable it to target pathogens for destruction on… (more)

▼ The innate immune system depends for its effectiveness on the function of specialized pattern recognition receptors which enable it to target pathogens for destruction on the basis of conserved molecular patterns such as flagellin or lipopolysaccharide (LPS). Specifically, LPS is recognized by the Toll-like receptor 4 (TLR4), activating a signaling pathway which triggers the production of both pro- and anti-inflammatory mediators. Very low doses of LPS, however, preferentially induce pro-inflammatory cytokines, which can lead to persistent low-grade inflammation, a contributing factor in a host of chronic diseases. The mild pro-inflammatory skewing induced by super-low-dose LPS also potentiates the inflammatory response to later challenge with a higher dose of LPS in a phenomenon known as the "Shwartzman reaction" or "endotoxin priming". We investigated the mechanisms involved in pro-inflammatory skewing by super-low-dose LPS in THP-1 cells and found it to be governed by a regulatory circuit of competitive inhibition between glycogen synthase kinase 3 (GSK3) and Akt, which promote the activity of the transcription factors FoxO1 and CREB, respectively. Super-low-dose LPS mildly activated FoxO1 and pro-inflammatory gene transcription without inducing anti-inflammatory genes or activating CREB, and this pro-inflammatory skewing could be abolished by inhibition of GSK3 or direct activation of CREB. We then examined the dynamics of the LPS response at various different dosages in murine bone-marrow-derived macrophages (BMDM). The pro-inflammatory cytokine IL-12 was most strongly induced by intermediate LPS dosages, with very low or high doses inducing less robust IL-12 production. Knockout of the inhibitory TLR4 pathway molecules Lyn or IRAK-M resulted in sustained induction of IL-12 by high doses of LPS. By activating CREB, we were able to reduce inflammation in WT BMDM, and saw that this corresponded with increased phosphorylation of CREB. Overall, we are confident that this subnetwork is an important switch regulating the resolution of inflammation in response to TLR4 stimulation. Furthermore, we propose that endotoxin priming is an example of the generalized capacity of all signaling networks to recall prior states, and that an appreciation for the history and context of exposure to stimuli is critical for the understanding of signaling behavior. Advisors/Committee Members: Li, Liwu (committeechair), Lu, Chang (committee member), Yuan, Lijuan (committee member), Tyson, John J. (committee member).

Subjects/Keywords: Immunology; lipopolysaccharide; signaling; monocyte; macrophage

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APA (6^th Edition):

Morris, M. (2014). Molecular mechanisms responsible for the dynamic modulation of macrophage responses to varying dosages of lipopolysaccharide. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64253

Chicago Manual of Style (16^th Edition):

Morris, Matthew. “Molecular mechanisms responsible for the dynamic modulation of macrophage responses to varying dosages of lipopolysaccharide.” 2014. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/64253.

MLA Handbook (7^th Edition):

Morris, Matthew. “Molecular mechanisms responsible for the dynamic modulation of macrophage responses to varying dosages of lipopolysaccharide.” 2014. Web. 22 Jan 2021.

Vancouver:

Morris M. Molecular mechanisms responsible for the dynamic modulation of macrophage responses to varying dosages of lipopolysaccharide. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/64253.

Council of Science Editors:

Morris M. Molecular mechanisms responsible for the dynamic modulation of macrophage responses to varying dosages of lipopolysaccharide. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/64253

Virginia Tech

5. Fu, Yan. Computational Systems Biology Analysis of Cell Reprogramming and Activation Dynamics.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2012, Virginia Tech

URL: http://hdl.handle.net/10919/28414

► In the past two decades, molecular cell biology has transitioned from a traditional descriptive science into a quantitative science that systematically measures cellular dynamics on… (more)

▼ In the past two decades, molecular cell biology has transitioned from a traditional descriptive science into a quantitative science that systematically measures cellular dynamics on different levels of genome, transcriptome and proteome. Along with this transition emerges the interdisciplinary field of systems biology, which aims to unravel complex interactions in biological systems through integrating experimental data into qualitative or quantitative models and computer simulations. In this dissertation, we applied various systems biology tools to investigate two important problems with respect to cellular activation dynamics and reprograming. Specifically, in the first section of the dissertation, we focused on lipopolysaccharide (LPS)-mediated priming and tolerance: a reprogramming in cytokine production in macrophages pretreated with specific doses of LPS. Though both priming and tolerance are important in the immune systemâ s response to pathogens, the molecular mechanisms still remain unclear. We computationally investigated all network topologies and dynamics that are able to generate priming or tolerance in a generic three-node model. Accordingly, we found three basic priming mechanisms and one tolerance mechanism. Existing experimental evidence support these in silico found mechanisms. In the second part of the dissertation, we applied stochastic modeling and simulations to investigate the phenotypic transition of bacteria E.coli between normally-growing cells and persister cells (growth-arrested phenotype), and how this process can contribute to drug resistance. We built up a complex computational model capturing the molecular mechanism on both single cell level and population level. The paper also proposed a novel way to accelerate the phenotypic transition from persister cells to normally growing cell under resonance activation. The general picture of phenotypic transitions should be applicable to a broader context of biological systems, such as T cell differentiation and stem cell reprogramming. Advisors/Committee Members: Xing, Jianhua (committeechair), Li, Liwu (committee member), Lu, Chang-Tien (committee member), Tyson, John J. (committeecochair).

Subjects/Keywords: computational modeling; network motifs; LPS priming and tolerance; bacterial phenotypic transition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Fu, Y. (2012). Computational Systems Biology Analysis of Cell Reprogramming and Activation Dynamics. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28414

Chicago Manual of Style (16^th Edition):

Fu, Yan. “Computational Systems Biology Analysis of Cell Reprogramming and Activation Dynamics.” 2012. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/28414.

MLA Handbook (7^th Edition):

Fu, Yan. “Computational Systems Biology Analysis of Cell Reprogramming and Activation Dynamics.” 2012. Web. 22 Jan 2021.

Vancouver:

Fu Y. Computational Systems Biology Analysis of Cell Reprogramming and Activation Dynamics. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/28414.

Council of Science Editors:

Fu Y. Computational Systems Biology Analysis of Cell Reprogramming and Activation Dynamics. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/28414

Virginia Tech

6. Poirel, Christopher L. Bridging Methodological Gaps in Network-Based Systems Biology.

Degree: PhD, Computer Science and Applications, 2013, Virginia Tech

URL: http://hdl.handle.net/10919/23899

► Functioning of the living cell is controlled by a complex network of interactions among genes, proteins, and other molecules. A major goal of systems biology… (more)

▼ Functioning of the living cell is controlled by a complex network of interactions among genes, proteins, and other molecules. A major goal of systems biology is to understand and explain the mechanisms by which these interactions govern the cell's response to various conditions. Molecular interaction networks have proven to be a powerful representation for studying cellular behavior. Numerous algorithms have been developed to unravel the complexity of these networks. Our work addresses the drawbacks of existing techniques. This thesis includes three related research efforts that introduce network-based approaches to bridge current methodological gaps in systems biology. i. Functional enrichment methods provide a summary of biological functions that are overrepresented in an interesting collection of genes (e.g., highly differentially expressed genes between a diseased cell and a healthy cell). Standard functional enrichment algorithms ignore the known interactions among proteins. We propose a novel network-based approach to functional enrichment that explicitly accounts for these underlying molecular interactions. Through this work, we close the gap between set-based functional enrichment and topological analysis of molecular interaction networks. ii. Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease. To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression p-values such that two genes connected by an interaction show similar changes in their gene expression values. iii. Top-down analyses in systems biology can automatically find correlations among genes and proteins in large-scale datasets. However, it is often difficult to design experiments from these results. In contrast, bottom-up approaches painstakingly craft detailed models of cellular processes. However, developing the models is a manual process that can take many years. These approaches have largely been developed independently. We present Linker, an efficient and automated data-driven method that analyzes molecular interactomes. Linker combines teleporting random walks and k-shortest path computations to discover connections from a set of source proteins to a set of target proteins. We demonstrate the efficacy of Linker through two applications: proposing extensions to an existing model of cell cycle regulation in budding yeast and automated reconstruction of human signaling pathways. Linker achieves superior precision and recall compared to state-of-the-art algorithms from the literature. Advisors/Committee Members: Murali, T. M. (committeechair), Vullikanti, Anil Kumar S. (committee member), Grama, Ananth (committee member), Tyson, John J. (committee member), Ramakrishnan, Naren (committee member).

Subjects/Keywords: Computational Biology; Functional Enrichment; Graph Theory; Network; Random Walk; Signaling Pathways; Top-Down Analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Poirel, C. L. (2013). Bridging Methodological Gaps in Network-Based Systems Biology. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23899

Chicago Manual of Style (16^th Edition):

Poirel, Christopher L. “Bridging Methodological Gaps in Network-Based Systems Biology.” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/23899.

MLA Handbook (7^th Edition):

Poirel, Christopher L. “Bridging Methodological Gaps in Network-Based Systems Biology.” 2013. Web. 22 Jan 2021.

Vancouver:

Poirel CL. Bridging Methodological Gaps in Network-Based Systems Biology. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/23899.

Council of Science Editors:

Poirel CL. Bridging Methodological Gaps in Network-Based Systems Biology. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23899

Virginia Tech

7. Jiang, Liang. Insights on the Regulation of the PERIOD 2 Gene in the Cellular Response to DNA Damage.

Degree: MS, Biological Sciences, 2019, Virginia Tech

URL: http://hdl.handle.net/10919/100865

► Circadian rhythm is a ~24-h mechanism that keeps our physiology and behavior in synchrony with environmental changes. PERIOD2 (PER2) is a core component of the… (more)

Subjects/Keywords: Circadian rhythm; PER2; DNA damage; p53; radiation; MDM2

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APA (6^th Edition):

Jiang, L. (2019). Insights on the Regulation of the PERIOD 2 Gene in the Cellular Response to DNA Damage. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/100865

Chicago Manual of Style (16^th Edition):

Jiang, Liang. “Insights on the Regulation of the PERIOD 2 Gene in the Cellular Response to DNA Damage.” 2019. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/100865.

MLA Handbook (7^th Edition):

Jiang, Liang. “Insights on the Regulation of the PERIOD 2 Gene in the Cellular Response to DNA Damage.” 2019. Web. 22 Jan 2021.

Vancouver:

Jiang L. Insights on the Regulation of the PERIOD 2 Gene in the Cellular Response to DNA Damage. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/100865.

Council of Science Editors:

Jiang L. Insights on the Regulation of the PERIOD 2 Gene in the Cellular Response to DNA Damage. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/100865

Virginia Tech

8. Laomettachit, Teeraphan. Mathematical modeling approaches for dynamical analysis of protein regulatory networks with applications to the budding yeast cell cycle and the circadian rhythm in cyanobacteria.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2011, Virginia Tech

URL: http://hdl.handle.net/10919/29492

► Mathematical modeling has become increasingly popular as a tool to study regulatory interactions within gene-protein networks. From the modelerâ s perspective, two challenges arise in… (more)

▼ Mathematical modeling has become increasingly popular as a tool to study regulatory interactions within gene-protein networks. From the modelerâ s perspective, two challenges arise in the process of building a mathematical model. First, the same regulatory network can be translated into different types of models at different levels of detail, and the modeler must choose an appropriate level to describe the network. Second, realistic regulatory networks are complicated due to the large number of biochemical species and interactions that govern any physiological process. Constructing and validating a realistic mathematical model of such a network can be a difficult and lengthy task. To confront the first challenge, we develop a new modeling approach that classifies components in the networks into three classes of variables, which are described by different rate laws. These three classes serve as â building blocksâ that can be connected to build a complex regulatory network. We show that our approach combines the best features of different types of models, and we demonstrate its utility by applying it to the budding yeast cell cycle. To confront the second challenge, modelers have developed rule-based modeling as a framework to build complex mathematical models. In this approach, the modeler describes a set of rules that instructs the computer to automatically generate all possible chemical reactions in the network. Building a mathematical model using rule-based modeling is not only less time-consuming and error-prone, but also allows modelers to account comprehensively for many different mechanistic details of a molecular regulatory system. We demonstrate the potential of rule-based modeling by applying it to the generation of circadian rhythms in cyanobacteria. Advisors/Committee Members: Tyson, John J. (committeechair), Banerjee, Diya (committee member), Finkielstein, Carla V. (committee member), Laubenbacher, Reinhard C. (committee member), Xing, Jianhua (committee member).

Subjects/Keywords: Cyanobacteria; Mathematical Modeling; Protein Regulatory Networks; Budding Yeast Cell Cycle; Circadian Rhythm

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APA (6^th Edition):

Laomettachit, T. (2011). Mathematical modeling approaches for dynamical analysis of protein regulatory networks with applications to the budding yeast cell cycle and the circadian rhythm in cyanobacteria. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/29492

Chicago Manual of Style (16^th Edition):

Laomettachit, Teeraphan. “Mathematical modeling approaches for dynamical analysis of protein regulatory networks with applications to the budding yeast cell cycle and the circadian rhythm in cyanobacteria.” 2011. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/29492.

MLA Handbook (7^th Edition):

Laomettachit, Teeraphan. “Mathematical modeling approaches for dynamical analysis of protein regulatory networks with applications to the budding yeast cell cycle and the circadian rhythm in cyanobacteria.” 2011. Web. 22 Jan 2021.

Vancouver:

Laomettachit T. Mathematical modeling approaches for dynamical analysis of protein regulatory networks with applications to the budding yeast cell cycle and the circadian rhythm in cyanobacteria. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/29492.

Council of Science Editors:

Laomettachit T. Mathematical modeling approaches for dynamical analysis of protein regulatory networks with applications to the budding yeast cell cycle and the circadian rhythm in cyanobacteria. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/29492

Virginia Tech

9. Chen, Minghan. Stochastic Modeling and Simulation of Multiscale Biochemical Systems.

Degree: PhD, Computer Science and Applications, 2019, Virginia Tech

URL: http://hdl.handle.net/10919/90898

► Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques,… (more)

▼ Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacterium’s life cycle— Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or analytical models with a single output for a given input, and stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks that contain both species populations and reaction rates with widely varying magnitude. The populations of some species may become negative in the simulation under some circumstances. This dissertation investigates negative population estimates from the hybrid method, proposes several remedies, and tests them with several cases including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of observed data must be large enough to obtain valid results. To optimize system parameters, the quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic (budding) yeast life cycle model by matching different distributions between simulated results and observed data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental molecular binding mechanism by the stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different optimization strategies are explored targeting different features of the observed data. Advisors/Committee Members: Cao, Young (committeechair), Watson, Layne T. (committeechair), Tyson, John J. (committee member), Kang, Hye Won (committee member), Sandu, Adrian (committee member).

Subjects/Keywords: Caulobacter cell cycle model; hybrid stochastic simulation algorithm; stochastic parameter optimization

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APA (6^th Edition):

Chen, M. (2019). Stochastic Modeling and Simulation of Multiscale Biochemical Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90898

Chicago Manual of Style (16^th Edition):

Chen, Minghan. “Stochastic Modeling and Simulation of Multiscale Biochemical Systems.” 2019. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/90898.

MLA Handbook (7^th Edition):

Chen, Minghan. “Stochastic Modeling and Simulation of Multiscale Biochemical Systems.” 2019. Web. 22 Jan 2021.

Vancouver:

Chen M. Stochastic Modeling and Simulation of Multiscale Biochemical Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/90898.

Council of Science Editors:

Chen M. Stochastic Modeling and Simulation of Multiscale Biochemical Systems. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90898

Virginia Tech

10. Jalihal, Amogh Prabhav. Mathematical modeling of macronutrient signaling in Saccharomyces cerevisiae.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2020, Virginia Tech

URL: http://hdl.handle.net/10919/99306

► An important problem in biology is how organisms sense and adapt to ever changing environments. A good example of an environmental cue that affects animal… (more)

▼ An important problem in biology is how organisms sense and adapt to ever changing environments. A good example of an environmental cue that affects animal behavior is the availability of food; scarcity of food forces animals to search for food-rich habitats, or go into hibernation. At the level of single cells, a range of behaviors are observed depending on the amount of food, or nutrients present in the environment. Moreover, different types of nutrients are important for different biological functions in single cells, and each different nutrient type will have to be available in the right quantities to support cellular growth. At the subcellular level, intricate molecular machineries exist which sense the amounts of each nutrient type, and interpret this information in order to make a decision on how best to respond. This interpretation and integration of nutrient information is a complex, poorly understood process even in a simple unicellular organism like the budding yeast. In order to understand this process, termed nutrient signaling, we propose a mathematical model of how yeasts respond to nutrient availability in the environment. Our model advances the state of knowledge by presenting the first comprehensive mathematical model of the nutrient signaling machinery, accounting for a variety of experimental observations from the last three decades of yeast nutrient signaling. We use our model to make predictions on how yeasts might behave when supplied with different combinations of nutrients, which can be verified by experiments. Finally, the cellular machinery that helps yeasts respond to nutrient availability in the environment is very similar to the machinery in cancer cells that causes them to grow rapidly. Our proposed model can serve as a stepping stone towards the construction of a model of cancer's responses to its nutritional environment. Advisors/Committee Members: Tyson, John J. (committeechair), Murali, T. M. (committeechair), Kraikivski, Pavel (committee member), Hauf, Silke (committee member), Chen, Jing (committee member).

Subjects/Keywords: Yeast; Signaling; Mathematical Modeling; Boolean Models; RNAseq

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APA (6^th Edition):

Jalihal, A. P. (2020). Mathematical modeling of macronutrient signaling in Saccharomyces cerevisiae. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99306

Chicago Manual of Style (16^th Edition):

Jalihal, Amogh Prabhav. “Mathematical modeling of macronutrient signaling in Saccharomyces cerevisiae.” 2020. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/99306.

MLA Handbook (7^th Edition):

Jalihal, Amogh Prabhav. “Mathematical modeling of macronutrient signaling in Saccharomyces cerevisiae.” 2020. Web. 22 Jan 2021.

Vancouver:

Jalihal AP. Mathematical modeling of macronutrient signaling in Saccharomyces cerevisiae. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/99306.

Council of Science Editors:

Jalihal AP. Mathematical modeling of macronutrient signaling in Saccharomyces cerevisiae. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99306

Virginia Tech

11. Glaros, Trevor Griffiths. Molecular Mechanisms Governing Persistent Induction of Pro-Inflammatory Genes by Lipopolysaccharide.

Degree: PhD, Biology, 2011, Virginia Tech

URL: http://hdl.handle.net/10919/73001

► Low dose endotoxemia is caused by several health conditions including smoking, alcohol abuse, high fat diets, and aging. Several studies have correlated low dose endotoxemia… (more)

▼ Low dose endotoxemia is caused by several health conditions including smoking, alcohol abuse, high fat diets, and aging. Several studies have correlated low dose endotoxemia with increased risks of atherosclerosis, diabetes, and Parkinson's disease. Unlike high doses of endotoxin which induce a strong but transient induction of pro-inflammatory mediators, low doses of endotoxin result in a mild but chronic induction of pro-inflammatory genes. The central hypothesis of our study was that if low doses of endotoxin are capable of inducing mild prolonged inflammation, then a unique signaling circuit must be utilized. In the first study, the molecular mechanisms for the persistent induction of lipocalin 2 (LCN2) in response to 100 ng/mL of lipopolysaccharide (LPS) in kidney fibroblasts was examined. It appears that the intracellular signaling network responsible for the persistent induction of LCN2 requires both activator protein-1 (AP-1) and CCAAT/enhancer binding protein delta (C/ebpÎ´). Interleukin-1 receptor-associated kinase 1 (IRAK-1) is critical for LCN2 expression. In the second study, the molecular mechanisms governing the persistent induction of interleukin 6 (IL-6) upon a 50 pg/mL challenge of LPS in macrophages was examined. At this dose, only the persistent activation of cJun N-terminal kinase (JNK) and C/ebpÎ´ was observed. IL-6 transcription requires the transient recruitment of activating transcription factor 2 (ATF2) and the persistent recruitment of C/ebpÎ´ to the IL-6 promoter. In the third study, the molecular mechanisms that mediate LPS-induced priming was examined. The results demonstrate that macrophages are able to sense their prior history of exposure to LPS that result in either a priming or tolerance phenotype upon a secondary challenge of LPS. Results suggest that this sensing mechanism involves cross-talk between IRAK-1 and phosphoinositide-3-kinase (PI3K). Collectively, these studies indicate that JNK and C/ebpÎ´ are the primary players responsible for the persistent expression of pro-inflammatory genes during low dose endotoxemia. IRAK-1 is a key intracellular signaling kinase that mediates signaling at low doses of LPS. IRAK-1 is not only critical for low dose induced expression, but also for LPS-induced priming. This research has revealed a novel signaling pathway that could provide new molecular targets for drug development against chronic inflammatory diseases. Advisors/Committee Members: Li, Liwu (committeechair), Capelluto, Daniel G. S. (committee member), Liu, Dongmin (committee member), Tyson, John J. (committee member).

Subjects/Keywords: Endotoxemia; Inflammation; Interleukin-1 Receptor-Associated Kinase 1; Lipopolysaccharide; Low Dose; Macrophage; Priming

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APA (6^th Edition):

Glaros, T. G. (2011). Molecular Mechanisms Governing Persistent Induction of Pro-Inflammatory Genes by Lipopolysaccharide. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73001

Chicago Manual of Style (16^th Edition):

Glaros, Trevor Griffiths. “Molecular Mechanisms Governing Persistent Induction of Pro-Inflammatory Genes by Lipopolysaccharide.” 2011. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/73001.

MLA Handbook (7^th Edition):

Glaros, Trevor Griffiths. “Molecular Mechanisms Governing Persistent Induction of Pro-Inflammatory Genes by Lipopolysaccharide.” 2011. Web. 22 Jan 2021.

Vancouver:

Glaros TG. Molecular Mechanisms Governing Persistent Induction of Pro-Inflammatory Genes by Lipopolysaccharide. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/73001.

Council of Science Editors:

Glaros TG. Molecular Mechanisms Governing Persistent Induction of Pro-Inflammatory Genes by Lipopolysaccharide. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/73001

Virginia Tech

12. Singhania, Rajat. Modeling Protein Regulatory Networks that Control Mammalian Cell Cycle Progression and that Exhibit Near-Perfect Adaptive Responses.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2011, Virginia Tech

URL: http://hdl.handle.net/10919/37722

► Protein regulatory networks are the hallmark of many important biological functionalities. Two of these functionalities are mammalian cell cycle progression and near-perfect adaptive responses. Modeling… (more)

▼ Protein regulatory networks are the hallmark of many important biological functionalities. Two of these functionalities are mammalian cell cycle progression and near-perfect adaptive responses. Modeling and simulating these functionalities are crucial stages to understanding and predicting them as systems-level properties of cells. In the context of the mammalian cell cycle, the timing of DNA synthesis, mitosis and cell division is regulated by a complex network of biochemical reactions that control the activities of a family of cyclin-dependent kinases. The temporal dynamics of this reaction network is typically modeled by nonlinear differential equations describing the rates of the component reactions. This approach provides exquisite details about molecular regulatory processes but is hampered by the need to estimate realistic values for the many kinetic constants that determine the reaction rates. To avoid this problem, modelers often resort to â qualitativeâ modeling strategies, such as Boolean switching networks, but these models describe only the coarsest features of cell cycle regulation. In this work, we describe a hybrid approach that combines features of continuous and discrete networks. The model is evaluated in terms of flow cytometry measurements of cyclin proteins in asynchronous populations of human cell lines. Using our hybrid approach, modelers can quickly create quantitatively accurate, computational models of protein regulatory networks found in various contexts within cells. Large-scale protein regulatory networks, such as the one that controls the progression of the mammalian cell cycle, also contain small-scale motifs or modules that carry out specific dynamical functions. Systematic characterization of smaller, interacting, network motifs whose individual behavior is well known under certain conditions is therefore of great interest to systems biologists. We model and simulate various 3-node network motifs to find near-perfect adaptation behavior. This behavior entails that a system responds to a change in its environmental cues, or signals, by coming back nearly to its pre-signal state even in the continued presence of the signal. We let various topologies evolve in their parameter space such that they eventually stumble upon a region where they score well under a pre-defined scoring metric. We find many such parameter sample sets across various classes of topologies. Advisors/Committee Members: Tyson, John J. (committeechair), Cao, Yang (committee member), Bevan, David R. (committee member), Kulkarni, Rahul V. (committee member), Sible, Jill C. (committee member).

Subjects/Keywords: adaptation; motifs; cell cycle regulation; mathematical modeling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Singhania, R. (2011). Modeling Protein Regulatory Networks that Control Mammalian Cell Cycle Progression and that Exhibit Near-Perfect Adaptive Responses. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/37722

Chicago Manual of Style (16^th Edition):

Singhania, Rajat. “Modeling Protein Regulatory Networks that Control Mammalian Cell Cycle Progression and that Exhibit Near-Perfect Adaptive Responses.” 2011. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/37722.

MLA Handbook (7^th Edition):

Singhania, Rajat. “Modeling Protein Regulatory Networks that Control Mammalian Cell Cycle Progression and that Exhibit Near-Perfect Adaptive Responses.” 2011. Web. 22 Jan 2021.

Vancouver:

Singhania R. Modeling Protein Regulatory Networks that Control Mammalian Cell Cycle Progression and that Exhibit Near-Perfect Adaptive Responses. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/37722.

Council of Science Editors:

Singhania R. Modeling Protein Regulatory Networks that Control Mammalian Cell Cycle Progression and that Exhibit Near-Perfect Adaptive Responses. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/37722

Virginia Tech

13. Chen, Chun. Systems Biology Study of Breast Cancer Endocrine Response and Resistance.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2013, Virginia Tech

URL: http://hdl.handle.net/10919/51965

► As a robust system, cells can wisely choose and switch between different signaling programs according to their differentiation stages and external environments. Cancer cells can… (more)

▼ As a robust system, cells can wisely choose and switch between different signaling programs according to their differentiation stages and external environments. Cancer cells can hijack this plasticity to develop drug resistance. For example, breast cancers that are initially responsive to endocrine therapy often develop resistance robustly. This process is dynamically controlled by interactions of genes, proteins, RNAs and environmental factors at multiple scales. The complexity of this network cannot be understood by studying individual components in the cell. Systems biology focuses on the interactions of basic components, so as to uncover the molecular mechanism of cell physiology with a systemic and dynamical view. Mathematical modeling as a tool in systems biology provides a unique opportunity to understand the underlying mechanisms of endocrine response and resistance in breast cancer. In Chapter 2, I focused on the experimental observations that breast cancer cells can switch between estrogen receptor α (ERα) regulated and growth factor receptor (GFR) regulated signaling pathways for survival and proliferation. A mathematical model based on the signaling crosstalk between ERα and GFR was constructed. The model successfully explains several intriguing experimental findings related to bimodal distributions of GFR proteins in breast cancer cells, which had been lacking reasonable justifications for almost two decades. The model also explains how transient overexpression of ERα promotes resistance of breast cancer cells to estrogen withdrawal. Understanding the non-genetic heterogeneity associated with this survival-signaling switch can shed light on the design of more efficient breast cancer therapies. In Chapter 3, I utilized a novel strategy to model the transitions between the endocrine response and resistance states in breast cancer cells. Using the experimentally observed estrogen sensitivity phenotypes in breast cancer (sensitive, hypersensitive, and supersensitive) as example, I proposed a useful framework of modeling cell state transitions on the energy landscape of breast cancer as a dynamical system. Grounded on the most possible routes of transitions on the breast cancer landscape, a state transition model was developed. By analyzing this model, I investigated the optimum settings of two intuitive strategies, sequential and intermittent treatments, to overcome endocrine resistance in breast cancer. The method used in this study can be generalized to study treatment strategies and improve treatment efficiencies in breast cancer as well as other types of cancer. Advisors/Committee Members: Tyson, John J. (committeechair), Li, Liwu (committee member), Baumann, William T. (committee member), Xing, Jianhua (committee member).

Subjects/Keywords: Mathematical modeling; breast cancer; endocrine resistance; signaling switch; breast cancer landscape

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, C. (2013). Systems Biology Study of Breast Cancer Endocrine Response and Resistance. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51965

Chicago Manual of Style (16^th Edition):

Chen, Chun. “Systems Biology Study of Breast Cancer Endocrine Response and Resistance.” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/51965.

MLA Handbook (7^th Edition):

Chen, Chun. “Systems Biology Study of Breast Cancer Endocrine Response and Resistance.” 2013. Web. 22 Jan 2021.

Vancouver:

Chen C. Systems Biology Study of Breast Cancer Endocrine Response and Resistance. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/51965.

Council of Science Editors:

Chen C. Systems Biology Study of Breast Cancer Endocrine Response and Resistance. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/51965

Virginia Tech

14. Tavassoly, Iman. Dynamics of Cell Fate Decisions Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells: Mathematical Modeling and Experimental Observations .

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2013, Virginia Tech

URL: http://hdl.handle.net/10919/79557

► Autophagy is a conserved biological stress response in mammalian cells that is responsible for clearing damaged proteins and organelles from the cytoplasm and recycling their… (more)

Subjects/Keywords: Apoptosis; Autophagy; Cancer; Cell Death; Dynamic Modeling; Live Cell Imaging; Quantitative Fluorescence Microscopy; Single-Cell

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APA (6^th Edition):

Tavassoly, I. (2013). Dynamics of Cell Fate Decisions Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells: Mathematical Modeling and Experimental Observations . (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79557

Chicago Manual of Style (16^th Edition):

Tavassoly, Iman. “Dynamics of Cell Fate Decisions Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells: Mathematical Modeling and Experimental Observations .” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/79557.

MLA Handbook (7^th Edition):

Tavassoly, Iman. “Dynamics of Cell Fate Decisions Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells: Mathematical Modeling and Experimental Observations .” 2013. Web. 22 Jan 2021.

Vancouver:

Tavassoly I. Dynamics of Cell Fate Decisions Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells: Mathematical Modeling and Experimental Observations . [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/79557.

Council of Science Editors:

Tavassoly I. Dynamics of Cell Fate Decisions Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells: Mathematical Modeling and Experimental Observations . [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/79557

Virginia Tech

15. Liu, Jingjing. Identification and Regulatory Role of E3 Ligases in the Time-Dependent Degradation of the Circadian Factor Period 2.

Degree: PhD, Biological Sciences, 2016, Virginia Tech

URL: http://hdl.handle.net/10919/81179

► Circadian rhythms are self-sustained, 24h, biological oscillatory processes that are present in organisms ranging from bacteria to human. Circadian rhythms, which can be synchronized by… (more)

▼ Circadian rhythms are self-sustained, 24h, biological oscillatory processes that are present in organisms ranging from bacteria to human. Circadian rhythms, which can be synchronized by external cues, are important for organisms to adjust their behavior, physiological activity, and metabolic reactions to changes in environmental conditions. Another well-established oscillatory mechanism that shares common organizational and regulatory features with the circadian system, is the cell division cycle. Recent findings reveal that some essential regulators are common to both the cell cycle and the circadian clock. The first half of my thesis (Chapter 2-3) focuses on the function of Period 2 (Per2), a key regulatory component of the negative feedback arm of the clock and tumor suppressor protein, as a modulator of cell cycle response. We found that Per2 binds the C-terminus end of the tumor suppressor p53 thus forming a trimeric complex with p53's negative regulator Mdm2 and preventing Mdm2-mediated p53's ubiquitination and degradation. Thus, Per2 stabilizes p53 under unstressed conditions allowing for basal levels of the protein to exist and be available for a rapid response to take place in case of any stressed signals. Our experiments prove that Per2 plays an indispensible role in p53 signaling pathway. The second half of my thesis (Chapter 4-5) focuses on how Mdm2 and Per2 interplay regulate Per2 availability and its impact on circadian clock function. My research found that Mdm2 targets Per2 for ubiquitination as Mdm2 depletion stabilizes Per2 and, conversely, Mdm2 ectopic expression shorten Per2's half-life. Accordingly, association of Per2 to Mdm2 maps C-terminus of the p53 binding region in Mdm2 and thus, the RING domain remains accessible. Next, we tested the hypothesis that Mdm2-dependent ubiquitination of Per2 directly impacts circadian clock period length. Accordingly, addition of sempervirine nitrate (SN), a specific molecular inhibitor of Mdm2, to MEF cells abrogated Per2 ubiquitination leading to the accumulation of a stable pool of Per2. By recording the oscillatory behavior of the Per2:Luc reporter system in MEF cells treated with SN at different circadian times, we found that inhibition of Mdm2 E3 ligase activity promoted phase advance only when treatment took place during the degradation period. This is in agreement with our findings that radiation, but not light pulses, causes the same phase behavior. Considering the established role of both Mdm2 and p53 in the response of cells to genotoxic stress and Per2 in modulating the clock, the existence of the Mdm2-Per2-p53 complex opens the possibility of various stimuli triggering regulatory mechanisms converging in a critical node. Overall, our work provides a holistic view of how signals are integrated at multiple levels to ensure that environmental signals are sense and responses triggered timely. Advisors/Committee Members: Finkielstein, Carla V. (committeechair), Tyson, John J. (committee member), Cassera, Maria Belen (committee member), Yang, Zhaomin (committee member).

Subjects/Keywords: Circadian rhythm; Period 2; ubiquitination; Mdm2; Protein stability; p53

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, J. (2016). Identification and Regulatory Role of E3 Ligases in the Time-Dependent Degradation of the Circadian Factor Period 2. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81179

Chicago Manual of Style (16^th Edition):

Liu, Jingjing. “Identification and Regulatory Role of E3 Ligases in the Time-Dependent Degradation of the Circadian Factor Period 2.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/81179.

MLA Handbook (7^th Edition):

Liu, Jingjing. “Identification and Regulatory Role of E3 Ligases in the Time-Dependent Degradation of the Circadian Factor Period 2.” 2016. Web. 22 Jan 2021.

Vancouver:

Liu J. Identification and Regulatory Role of E3 Ligases in the Time-Dependent Degradation of the Circadian Factor Period 2. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/81179.

Council of Science Editors:

Liu J. Identification and Regulatory Role of E3 Ligases in the Time-Dependent Degradation of the Circadian Factor Period 2. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/81179

Virginia Tech

16. Ahmadian, Mansooreh. Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks.

Degree: PhD, Computer Science and Applications, 2020, Virginia Tech

URL: http://hdl.handle.net/10919/99481

► Cell cycle is a process in which a growing cell replicates its DNA and divides into two cells. Progression through the cell cycle is regulated… (more)

Subjects/Keywords: Cell Cycle Modeling; Hybrid Stochastic Modeling; Cell size control; Parameter estimation; Neural network; Theory-guided machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ahmadian, M. (2020). Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99481

Chicago Manual of Style (16^th Edition):

Ahmadian, Mansooreh. “Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks.” 2020. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/99481.

MLA Handbook (7^th Edition):

Ahmadian, Mansooreh. “Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks.” 2020. Web. 22 Jan 2021.

Vancouver:

Ahmadian M. Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/99481.

Council of Science Editors:

Ahmadian M. Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99481

Virginia Tech

17. Lux, Matthew William. Estimation of gene network parameters from imaging cytometry data.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2013, Virginia Tech

URL: http://hdl.handle.net/10919/23082

► Synthetic biology endeavors to forward engineer genetic circuits with novel function. A major inspiration for the field has been the enormous success in the engineering… (more)

▼ Synthetic biology endeavors to forward engineer genetic circuits with novel function. A major inspiration for the field has been the enormous success in the engineering of digital electronic circuits over the past half century. This dissertation approaches synthetic biology from the perspective of the engineering design cycle, a concept ubiquitous across many engineering disciplines. First, an analysis of the state of the engineering design cycle in synthetic biology is presented, pointing out the most limiting challenges currently facing the field. Second, a principle commonly used in electronics to weigh the tradeoffs between hardware and software implementations of a function, called co-design, is applied to synthetic biology. Designs to implement a specific logical function in three distinct domains are proposed and their pros and cons weighed. Third, automatic transitioning between an abstract design, its physical implementation, and accurate models of the corresponding system are critical for success in synthetic biology. We present a framework for accomplishing this task and demonstrate how it can be used to explore a design space. A major limitation of the aforementioned approach is that adequate parameter values for the performance of genetic components do not yet exist. Thus far, it has not been possible to uniquely attribute the function of a device to the function of the individual components in a way that enables accurate prediction of the function of new devices assembled from the same components. This lack presents a major challenge to rapid progression through the design cycle. We address this challenge by first collecting high time-resolution fluorescence trajectories of individual cells expressing a fluorescent protein, as well as snapshots of the number of corresponding mRNA molecules per cell. We then leverage the information embedded in the cell-cell variability of the population to extract parameter values for a stochastic model of gene expression more complex than typically used. Such analysis opens the door for models of genetic components that can more reliably predict the function of new combinations of these basic components. Advisors/Committee Members: Peccoud, Jean (committeechair), Tyler, Brett M. (committee member), Tyson, John J. (committee member), Baumann, William T. (committee member).

Subjects/Keywords: synthetic biology; computational modeling; parameter estimation; systems biology

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lux, M. W. (2013). Estimation of gene network parameters from imaging cytometry data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23082

Chicago Manual of Style (16^th Edition):

Lux, Matthew William. “Estimation of gene network parameters from imaging cytometry data.” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/23082.

MLA Handbook (7^th Edition):

Lux, Matthew William. “Estimation of gene network parameters from imaging cytometry data.” 2013. Web. 22 Jan 2021.

Vancouver:

Lux MW. Estimation of gene network parameters from imaging cytometry data. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/23082.

Council of Science Editors:

Lux MW. Estimation of gene network parameters from imaging cytometry data. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23082

Virginia Tech

18. Hong, Tian. A framework for understanding heterogeneous differentiation of CD4⁺ T cells.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2013, Virginia Tech

URL: http://hdl.handle.net/10919/51228

► CD4+ T cells are a group of lymphocytes that play critical roles in the immune system. By releasing cytokines, CD4+ T cells regulate other immune… (more)

▼ CD4+ T cells are a group of lymphocytes that play critical roles in the immune system. By releasing cytokines, CD4+ T cells regulate other immune cells for maximizing the efficiency of the system. Naive CD4+ T cells are activated and become mature upon engagement with antigens, and the mature CD4+ T cells have several subsets, which play diverse regulatory functions. For the past two decades, our understanding of CD4+ T cells has been advanced through the studies on the differentiation process and the lineage specification of various subsets of these cells. Although in most experimental studies of CD4+ T cells, researchers focused on how transcription factors and signaling molecules influence the differentiation of a particular subset of these cells, many evidence have shown that the differentiation of CD4+ T cells can be heterogeneous in terms of the phenotypes of the cells involved. This dissertation describes a framework that uses mathematical models of the dynamics of the signaling pathways to explain heterogeneous differentiation. We show that the mutual inhibitions among the master regulators govern the formation of multi-stability behavior, which in turn gives rise to heterogeneous differentiation. The framework can be applied to systems with two or more master regulators, and models based on the framework can make specific predictions about heterogeneous differentiations. In addition, this dissertation describes an experimental study on CD4+ T cell differentiation. Being part of the adaptive immune system, the differentiation of CD4+ T cells was previously known to be induced by the signals from the innate immune cells. However, the expression of Toll-like receptor in CD4+ T cells suggests that microbial products can also influence the differentiation directly. Using an in vitro cell differentiation approach, we show that the differentiation and proliferation of CD4+ T cells can be influenced by lipopolysaccharide under the condition that would favor the differentiation of induced regulatory T cells. These theoretical and experimental studies give novel insights on how CD4+ T cells differentiate in response to pathogenic challenges, and help to gain deeper understanding of regulatory mechanisms of the complex immune system. Advisors/Committee Members: Tyson, John J. (committeechair), Li, Liwu (committee member), Xing, Jianhua (committee member), Yuan, Lijuan (committee member), Murali, T. M. (committee member).

Subjects/Keywords: CD4+ T cells; mathematical model; cell differentiation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hong, T. (2013). A framework for understanding heterogeneous differentiation of CD4⁺ T cells. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51228

Chicago Manual of Style (16^th Edition):

Hong, Tian. “A framework for understanding heterogeneous differentiation of CD4⁺ T cells.” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/51228.

MLA Handbook (7^th Edition):

Hong, Tian. “A framework for understanding heterogeneous differentiation of CD4⁺ T cells.” 2013. Web. 22 Jan 2021.

Vancouver:

Hong T. A framework for understanding heterogeneous differentiation of CD4⁺ T cells. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/51228.

Council of Science Editors:

Hong T. A framework for understanding heterogeneous differentiation of CD4⁺ T cells. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/51228

Virginia Tech

19. Adam, Laura. Mapping Genotype to Phenotype using Attribute Grammar.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2013, Virginia Tech

URL: http://hdl.handle.net/10919/51768

► Over the past 10 years, several synthetic biology research groups have proposed tools and domain-specific languages to help with the design of artificial DNA molecules.… (more)

▼ Over the past 10 years, several synthetic biology research groups have proposed tools and domain-specific languages to help with the design of artificial DNA molecules. Community standards for exchanging data between these tools, such as the Synthetic Biology Open Language (SBOL), have been developed. It is increasingly important to be able to perform in silico simulation before the time and cost consuming wet lab realization of the constructs, which, as technology advances, also become in themselves more complex. By extending the concept of describing genetic expression as a language, we propose to model relations between genotype and phenotype using formal language theory. We use attribute grammars (AGs) to extract context-dependent information from genetic constructs and compile them into mathematical models, possibly giving clues about their phenotypes. They may be used as a backbone for biological Domain-Specific Languages (DSLs) and we developed a methodology to design these AG based DSLs. We gave examples of languages in the field of synthetic biology to model genetic regulatory networks with Ordinary Differential Equations (ODEs) based on various rate laws or with discrete boolean network models. We implemented a demonstration of these concepts in GenoCAD, a Computer Assisted Design (CAD) software for synthetic biology. GenoCAD guides users from design to simulation. Users can either design constructs with the attribute grammars provided or define their own project-specific languages. Outputting the mathematical model of a genetic construct is performed by DNA compilation based on the attribute grammar specified; the design of new languages by users necessitated the generation on-the-fly of such attribute grammar based DNA compilers. We also considered the impact of our research and its potential dual-use issues. Indeed, after the design exploration is performed in silico, the next logical step is to synthesize the designed construct's DNA molecule to build the construct in vivo. We implemented an algorithm to identify sequences of concern of any length that are specific to Select Agents and Toxins, helping to ensure safer use of our methods. Advisors/Committee Members: Peccoud, Jean (committeechair), Bevan, David R. (committee member), Garner, Harold Ray (committee member), Ramakrishnan, Naren (committee member), Kepes, Francois (committee member), Tyson, John J. (committee member).

Subjects/Keywords: Synthetic Biology; Genotype; Phenotype; Formal Language; Attribute Grammar; Compilation; Compiler Generation; Prolog; SBML

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Adam, L. (2013). Mapping Genotype to Phenotype using Attribute Grammar. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51768

Chicago Manual of Style (16^th Edition):

Adam, Laura. “Mapping Genotype to Phenotype using Attribute Grammar.” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/51768.

MLA Handbook (7^th Edition):

Adam, Laura. “Mapping Genotype to Phenotype using Attribute Grammar.” 2013. Web. 22 Jan 2021.

Vancouver:

Adam L. Mapping Genotype to Phenotype using Attribute Grammar. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/51768.

Council of Science Editors:

Adam L. Mapping Genotype to Phenotype using Attribute Grammar. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/51768

Virginia Tech

20. Ravi, Janani. Mathematical modeling of pathways involved in cell cycle regulation and differentiation.

Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2011, Virginia Tech

URL: http://hdl.handle.net/10919/73006

► Cellular processes critical to sustaining physiology, including growth, division and differentiation, are carefully governed by intricate control systems. Deregulations in these systems often result in… (more)

▼ Cellular processes critical to sustaining physiology, including growth, division and differentiation, are carefully governed by intricate control systems. Deregulations in these systems often result in complex diseases such as cancer. Hence, it is crucial to understand the interactions between molecular players of these control systems, their emergent network dynamics, and, ultimately, the overall contribution to cellular physiology. In this dissertation, we have developed a mathematical framework to understand two such cellular systems: an early checkpoint (START) in the budding yeast cell cycle (Chapter 1), and the canonical Wnt signaling pathway involved in cell proliferation and differentiation (Chapter 2). START transition is an important decision point where the cell commits to one round DNA replication followed by cell division. Several years of experimental research have gone into uncovering molecular details of this process, but a unified understanding is yet to emerge. In chapter one, we have developed a comprehensive mathematical model of START transition that incorporates several findings including information about the phosphorylation state of key START proteins and their subcellular localization. In the second chapter, we focus on modeling the canonical Wnt signaling pathway, a cellular circuit that plays a key role in cell proliferation and differentiation. The Wnt pathway is often deregulated in colon cancers. Based on some evidence of bistability in the Wnt signaling pathway, we proposed the existence of a positive feedback loop underlying the activation and inactivation of the core protein complex of the pathway. Bistability is a common feature of biological systems that toggle between ON and OFF states because it ensures robust switching back and forth between the two states. To study and explain the behavior of this dynamical system, we developed a mathematical model. Based on experimentally determined interactions, our simple model recapitulates the observed phenomena of bimodality (bistability) and hysteresis under the effects of the physiological signal (Wnt), a Wnt-mimic (LiCl), and a stabilizer of one of the key members of core complex (IWR-1). Overall, we believe that cell biologists and molecular geneticists can benefit from our work by using our model to make novel quantitative predictions for experimental verification. Advisors/Committee Members: Tyson, John J. (committeechair), Baumann, William T. (committee member), Chen, Katherine C. (committee member), Finkielstein, Carla V. (committee member), Hannsgen, Kenneth B. (committee member), Xing, Jianhua (committee member).

Subjects/Keywords: START transition; Wnt signaling; bistability; cell size control; theoretical biology

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APA (6^th Edition):

Ravi, J. (2011). Mathematical modeling of pathways involved in cell cycle regulation and differentiation. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73006

Chicago Manual of Style (16^th Edition):

Ravi, Janani. “Mathematical modeling of pathways involved in cell cycle regulation and differentiation.” 2011. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/73006.

MLA Handbook (7^th Edition):

Ravi, Janani. “Mathematical modeling of pathways involved in cell cycle regulation and differentiation.” 2011. Web. 22 Jan 2021.

Vancouver:

Ravi J. Mathematical modeling of pathways involved in cell cycle regulation and differentiation. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/73006.

Council of Science Editors:

Ravi J. Mathematical modeling of pathways involved in cell cycle regulation and differentiation. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/73006

Virginia Tech

21. Pratapa, Aditya. Algorithms for regulatory network inference and experiment planning in systems biology.

Degree: PhD, Computer Science and Applications, 2020, Virginia Tech

URL: http://hdl.handle.net/10919/99378

► A small number of key molecules can completely change the cell's state, for example, a stem cell differentiating into distinct types of blood cells or… (more)

▼ A small number of key molecules can completely change the cell's state, for example, a stem cell differentiating into distinct types of blood cells or a healthy cell turning cancerous. How can we uncover the important cellular events that govern complex biological behavior? One approach to answering the question has been to elucidate the mechanisms by which genes and proteins control each other in a cell. These mechanisms are typically represented in the form of a gene or protein regulatory network. The resulting networks can be modeled as a system of mathematical equations, also known as a mathematical model. The advantage of such a model is that we can computationally simulate the time courses of various molecules. Moreover, we can use the model simulations to predict the effect of perturbations such as deleting one or more genes. A biologist can perform experiments to test these predictions. Subsequently, the model can be iteratively refined by reconciling any differences between the prediction and the experiment. In this thesis I present two novel solutions aimed at dramatically reducing the time and effort required for this build-simulate-test cycle. The first solution I propose is in prioritizing and planning large-scale gene perturbation experiments that can be used for validating existing models. I then focus on taking advantage of the recent advances in experimental techniques that enable us to measure gene activity at a single-cell resolution, known as scRNA-seq. This scRNA-seq data can be used to infer the interactions in gene regulatory networks. I perform a systematic evaluation of existing computational methods for building gene regulatory networks from scRNA-seq data. Based on the insights gained from this comprehensive evaluation, I propose novel algorithms that can take advantage of prior knowledge in building these regulatory networks. The results underscore the promise of my approach in identifying cell-type specific interactions. These context-specific interactions play a key role in building mathematical models to study complex cellular processes such as a developmental process that drives transitions from one cell type to another Advisors/Committee Members: Murali, T. M. (committeechair), Heath, Lenwood S. (committee member), Prakash, Bodicherla Aditya (committee member), Tyson, John J. (committee member), Kececioglu, John D. (committee member).

Subjects/Keywords: network biology; experiment planning; gene regulatory networks; deep learning; single cell transcriptomics

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APA (6^th Edition):

Pratapa, A. (2020). Algorithms for regulatory network inference and experiment planning in systems biology. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99378

Chicago Manual of Style (16^th Edition):

Pratapa, Aditya. “Algorithms for regulatory network inference and experiment planning in systems biology.” 2020. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/99378.

MLA Handbook (7^th Edition):

Pratapa, Aditya. “Algorithms for regulatory network inference and experiment planning in systems biology.” 2020. Web. 22 Jan 2021.

Vancouver:

Pratapa A. Algorithms for regulatory network inference and experiment planning in systems biology. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/99378.

Council of Science Editors:

Pratapa A. Algorithms for regulatory network inference and experiment planning in systems biology. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99378

22. Jones, Thomas Carroll Jr. JigCell Model Connector: Building Large Molecular Network Models from Components.

Degree: MS, Computer Science and Applications, 2017, Virginia Tech

URL: http://hdl.handle.net/10919/78277

► The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions… (more)

Subjects/Keywords: Computational Systems Biology; Hierarchical Model Composition; SBML; Modeling Tool; Software; JigCell

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APA (6^th Edition):

Jones, T. C. J. (2017). JigCell Model Connector: Building Large Molecular Network Models from Components. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78277

Chicago Manual of Style (16^th Edition):

Jones, Thomas Carroll Jr. “JigCell Model Connector: Building Large Molecular Network Models from Components.” 2017. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/78277.

MLA Handbook (7^th Edition):

Jones, Thomas Carroll Jr. “JigCell Model Connector: Building Large Molecular Network Models from Components.” 2017. Web. 22 Jan 2021.

Vancouver:

Jones TCJ. JigCell Model Connector: Building Large Molecular Network Models from Components. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/78277.

Council of Science Editors:

Jones TCJ. JigCell Model Connector: Building Large Molecular Network Models from Components. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/78277

23. Li, Fei. Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems.

Degree: PhD, Computer Science and Applications, 2016, Virginia Tech

URL: http://hdl.handle.net/10919/64913

► Reaction Diffusion Master Equation (RDME) framework, characterized by the discretization of the spatial domain, is one of the most widely used methods in the stochastic… (more)

Subjects/Keywords: stochastic simulation; reaction-diffusion systems; Caulobacter crescentus

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APA (6^th Edition):

Li, F. (2016). Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64913

Chicago Manual of Style (16^th Edition):

Li, Fei. “Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/64913.

MLA Handbook (7^th Edition):

Li, Fei. “Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems.” 2016. Web. 22 Jan 2021.

Vancouver:

Li F. Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/64913.

Council of Science Editors:

Li F. Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/64913

Virginia Tech

24. Zwolak, Jason Walter. Parameter Estimation in Biological Cell Cycle Models Using Deterministic Optimization.

Degree: MS, Computer Science, 2001, Virginia Tech

URL: http://hdl.handle.net/10919/31354

► Cell cycle models used in biology can be very complex. These models have parameters with initially unknown values. The values of the parameters vastly aect… (more)

Subjects/Keywords: Cell Cycle Models; Parameter Estima- tion.; Ordinary Di erential Equations

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APA (6^th Edition):

Zwolak, J. W. (2001). Parameter Estimation in Biological Cell Cycle Models Using Deterministic Optimization. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/31354

Chicago Manual of Style (16^th Edition):

Zwolak, Jason Walter. “Parameter Estimation in Biological Cell Cycle Models Using Deterministic Optimization.” 2001. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/31354.

MLA Handbook (7^th Edition):

Zwolak, Jason Walter. “Parameter Estimation in Biological Cell Cycle Models Using Deterministic Optimization.” 2001. Web. 22 Jan 2021.

Vancouver:

Zwolak JW. Parameter Estimation in Biological Cell Cycle Models Using Deterministic Optimization. [Internet] [Masters thesis]. Virginia Tech; 2001. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/31354.

Council of Science Editors:

Zwolak JW. Parameter Estimation in Biological Cell Cycle Models Using Deterministic Optimization. [Masters Thesis]. Virginia Tech; 2001. Available from: http://hdl.handle.net/10919/31354

Virginia Tech

25. Dufour, Yann Serge. Experimental Methods in Support of the Development of a Computational Model for Quorum Sensing in Vibrio fischeri.

Degree: MS, Biology, 2004, Virginia Tech

URL: http://hdl.handle.net/10919/34290

► The quorum sensing signaling system based on intercellular exchange of N-acyl-homoserine lactones is used by many proteobacteria to regulate the transcription of essential genes in… (more)

▼ The quorum sensing signaling system based on intercellular exchange of N-acyl-homoserine lactones is used by many proteobacteria to regulate the transcription of essential genes in a signal density-dependent manner. It is involved in a number of processes including the development of highly organized bacterial communities, e.g., biofilms, the regulation of expression of virulence factors, production of antibiotics, and bioluminescence. The extensive genetic and biochemical data available on the quorum sensing system in Vibrio fischeri allows the development of a systems biology approach to undertake a spatial and dynamical analysis of the regulation throughout the population. The quorum sensing regulated lux genes are organized in two divergent transcriptional units: luxR and luxICDABEG. The latter contains the genes required for luminescence and the luxI gene necessary for synthesis of an N-acyl-homoserine lactone commonly called autoinducer (AI). The luxR gene codes for a transcriptional regulatory protein that activates the transcription of both operons at a threshold concentration of AI. The positive feedback loop induces a rapid increase of transcription level of the lux genes when a critical population density is reached (reflected by the concentration of AI in the environment). With a combination of molecular biology tools, physiological analysis, and mathematical modeling we identified critical characteristics of the system and expect to assign parameter values in order to achieve a comprehensive understanding of the dynamics. An ordinary differential equation mathematical model is used to investigate the dynamics of the system and derive parameter values. In parallel a novel microfluidic cell culture experimental set-up is used to carefully control environmental parameters as well as to achieve chemostatic conditions for high-density cell populations. An unstable variant of the green fluorescent protein was used as a reporter to follow the time response at a single cell level. Thus spatial organization and noise across the population can be analyzed. Plasmids carrying different genetic constructs were transformed in a recombinant Escherichia coli strain to specifically identify genetic and biochemical elements involved in the regulation of the lux genes under diverse conditions. Then the quantitative data extracted from batch culture and microfluidic assays were used to assign parameter values in the models. The particular question being investigated first is the nature of the regulation to increasing concentration of the signal. The hypothesis tested is that the regulation of the production of the signal by individual cells is biphasic and, therefore, quorum sensing should be robust to global and local variations in cell density. Advisors/Committee Members: Stevens, Ann M. (committeechair), Popham, David L. (committee member), Tyson, John J. (committee member).

Subjects/Keywords: microfluidics; quorum sensing; Vibrio fischeri; mathematical modeling; luminescence; gene regulation

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APA (6^th Edition):

Dufour, Y. S. (2004). Experimental Methods in Support of the Development of a Computational Model for Quorum Sensing in Vibrio fischeri. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/34290

Chicago Manual of Style (16^th Edition):

Dufour, Yann Serge. “Experimental Methods in Support of the Development of a Computational Model for Quorum Sensing in Vibrio fischeri.” 2004. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/34290.

MLA Handbook (7^th Edition):

Dufour, Yann Serge. “Experimental Methods in Support of the Development of a Computational Model for Quorum Sensing in Vibrio fischeri.” 2004. Web. 22 Jan 2021.

Vancouver:

Dufour YS. Experimental Methods in Support of the Development of a Computational Model for Quorum Sensing in Vibrio fischeri. [Internet] [Masters thesis]. Virginia Tech; 2004. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/34290.

Council of Science Editors:

Dufour YS. Experimental Methods in Support of the Development of a Computational Model for Quorum Sensing in Vibrio fischeri. [Masters Thesis]. Virginia Tech; 2004. Available from: http://hdl.handle.net/10919/34290

Virginia Tech

26. Calzone, Laurence. Mathematical Modeling of the Budding Yeast Cell Cycle.

Degree: MS, Mathematics, 2000, Virginia Tech

URL: http://hdl.handle.net/10919/31988

► The cell cycle of the budding yeast, Saccharomyces cerevisiae, is regulated by a complex network of chemical reactions controlling the activity of the cyclin-dependent kinases… (more)

Subjects/Keywords: cell cycle; Budding Yeast; CDK

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APA (6^th Edition):

Calzone, L. (2000). Mathematical Modeling of the Budding Yeast Cell Cycle. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/31988

Chicago Manual of Style (16^th Edition):

Calzone, Laurence. “Mathematical Modeling of the Budding Yeast Cell Cycle.” 2000. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/31988.

MLA Handbook (7^th Edition):

Calzone, Laurence. “Mathematical Modeling of the Budding Yeast Cell Cycle.” 2000. Web. 22 Jan 2021.

Vancouver:

Calzone L. Mathematical Modeling of the Budding Yeast Cell Cycle. [Internet] [Masters thesis]. Virginia Tech; 2000. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/31988.

Council of Science Editors:

Calzone L. Mathematical Modeling of the Budding Yeast Cell Cycle. [Masters Thesis]. Virginia Tech; 2000. Available from: http://hdl.handle.net/10919/31988

Virginia Tech

27. Petrus, Matthew J. Mechanisms of cell cycle remodeling at the MBT during the development of Xenopus laevis embryos.

Degree: MS, Biology, 2002, Virginia Tech

URL: http://hdl.handle.net/10919/32359

► During the early development of Xenopus laevis embryos, cells divide without checkpoints. At the midblastula transition (MBT), the cell cycle is remodeled as the… (more)

Subjects/Keywords: cyclin E/Cdk2; XChk1; Xenopus; Xic1; developmental timer; Wee1

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APA (6^th Edition):

Petrus, M. J. (2002). Mechanisms of cell cycle remodeling at the MBT during the development of Xenopus laevis embryos. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/32359

Chicago Manual of Style (16^th Edition):

Petrus, Matthew J. “Mechanisms of cell cycle remodeling at the MBT during the development of Xenopus laevis embryos.” 2002. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/32359.

MLA Handbook (7^th Edition):

Petrus, Matthew J. “Mechanisms of cell cycle remodeling at the MBT during the development of Xenopus laevis embryos.” 2002. Web. 22 Jan 2021.

Vancouver:

Petrus MJ. Mechanisms of cell cycle remodeling at the MBT during the development of Xenopus laevis embryos. [Internet] [Masters thesis]. Virginia Tech; 2002. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/32359.

Council of Science Editors:

Petrus MJ. Mechanisms of cell cycle remodeling at the MBT during the development of Xenopus laevis embryos. [Masters Thesis]. Virginia Tech; 2002. Available from: http://hdl.handle.net/10919/32359

Virginia Tech

28. Panning, Thomas D. Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle.

Degree: MS, Computer Science, 2006, Virginia Tech

URL: http://hdl.handle.net/10919/33360

► Two parallel deterministic direct search algorithms are combined to find improved parameters for a system of differential equations designed to simulate the cell cycle of… (more)

Subjects/Keywords: computational biology; MADS algorithm; direct search; DIRECT algorithm

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APA (6^th Edition):

Panning, T. D. (2006). Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/33360

Chicago Manual of Style (16^th Edition):

Panning, Thomas D. “Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle.” 2006. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/33360.

MLA Handbook (7^th Edition):

Panning, Thomas D. “Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle.” 2006. Web. 22 Jan 2021.

Vancouver:

Panning TD. Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle. [Internet] [Masters thesis]. Virginia Tech; 2006. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/33360.

Council of Science Editors:

Panning TD. Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle. [Masters Thesis]. Virginia Tech; 2006. Available from: http://hdl.handle.net/10919/33360

Virginia Tech

29. Hong, Christian I. Mathematical Modeling of Circadian Rhythms in Drosophila melanogaster.

Degree: MS, Biology, 1999, Virginia Tech

URL: http://hdl.handle.net/10919/42168

► Circadian rhythms are periodic physiological cycles that recur about every 24 hours, by means of which organisms integrate their physiology and behavior to the daily… (more)

Subjects/Keywords: Mathematical biology; Phase response curve; Phase plane; Hopf bifurcation

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APA (6^th Edition):

Hong, C. I. (1999). Mathematical Modeling of Circadian Rhythms in Drosophila melanogaster. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/42168

Chicago Manual of Style (16^th Edition):

Hong, Christian I. “Mathematical Modeling of Circadian Rhythms in Drosophila melanogaster.” 1999. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/42168.

MLA Handbook (7^th Edition):

Hong, Christian I. “Mathematical Modeling of Circadian Rhythms in Drosophila melanogaster.” 1999. Web. 22 Jan 2021.

Vancouver:

Hong CI. Mathematical Modeling of Circadian Rhythms in Drosophila melanogaster. [Internet] [Masters thesis]. Virginia Tech; 1999. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/42168.

Council of Science Editors:

Hong CI. Mathematical Modeling of Circadian Rhythms in Drosophila melanogaster. [Masters Thesis]. Virginia Tech; 1999. Available from: http://hdl.handle.net/10919/42168

Virginia Tech

30. Auckland, Ian. Quantitative Analysis of a Cell Cycle Checkpoint in Xenopus laevis Cell-Free Egg Extracts.

Degree: MS, Biology, 2005, Virginia Tech

URL: http://hdl.handle.net/10919/34734

► In somatic cells, checkpoint pathways trigger cell cycle arrest in response to unreplicated or damaged DNA by inhibiting the activity of cyclin-dependent kinases (Cdks). In… (more)

Subjects/Keywords: cell cycle; Xenopus laevis; nucleocytoplasmic ratio; cyclin-dependent kinases (Cdk)

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APA (6^th Edition):

Auckland, I. (2005). Quantitative Analysis of a Cell Cycle Checkpoint in Xenopus laevis Cell-Free Egg Extracts. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/34734

Chicago Manual of Style (16^th Edition):

Auckland, Ian. “Quantitative Analysis of a Cell Cycle Checkpoint in Xenopus laevis Cell-Free Egg Extracts.” 2005. Masters Thesis, Virginia Tech. Accessed January 22, 2021. http://hdl.handle.net/10919/34734.

MLA Handbook (7^th Edition):

Auckland, Ian. “Quantitative Analysis of a Cell Cycle Checkpoint in Xenopus laevis Cell-Free Egg Extracts.” 2005. Web. 22 Jan 2021.

Vancouver:

Auckland I. Quantitative Analysis of a Cell Cycle Checkpoint in Xenopus laevis Cell-Free Egg Extracts. [Internet] [Masters thesis]. Virginia Tech; 2005. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10919/34734.

Council of Science Editors:

Auckland I. Quantitative Analysis of a Cell Cycle Checkpoint in Xenopus laevis Cell-Free Egg Extracts. [Masters Thesis]. Virginia Tech; 2005. Available from: http://hdl.handle.net/10919/34734

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