Peripheral neuropathies, affect approximately 20 million people in the United States and are often a complication of conditions such as diabetes that can result in amputation of affected areas such as the feet and toes. In vitro methodologies to facilitate the understanding and treatment of these disorders often lack the cellular and functional complexity required to accurately model peripheral neuropathies. In particular, they are often 2-D and functional readouts, such as electrical activity, are limited to cell bodies thereby limiting the understanding of axonopathy which often characterizes these disorders. We have developed a functional 3-D model of peripheral nerves using a capillary alginate gel (Capgel™), as a scaffold. We hypothesize that: 1) The unique microcapillary structure of Capgel™ allows for the modeling of the 3-D microstructure of the peripheral nerve, and 2) That axon bundling in the capillary allows for the detection of axonal electrical activity. In our initial studies, we demonstrate that culturing embryonic dorsal root ganglia (DRG) within the Capgel™ environment allows for the separation of cell bodies from axons and recreates many of the features of an in vivo peripheral nerve fascicle including myelinated axons and the formation of a rudimentary perineurium. To develop functionality for this model we have integrated the DRG Capgel™ culture with a microelectrode array to examine spontaneous activity in axon bundles, which we find demonstrates superiority to other widely used 2-D models of the same tissue. Furthermore, by analyzing the activity on individual electrodes, we were able to record action potentials from multiple axons within the same bundle indicating a functional complexity comparable to that observed in fascicles in vivo. This 3D model of the peripheral nerve can be used to study the functional complexities of peripheral neuropathies and nerve regeneration as well as being utilized in the development of novel therapeutics.

Traumatic brain injury (TBI) is a devastating problem which stands as a leading cause of death and disability. The elderly is significantly affected by TBI, typically as the result of falls, and recovery is especially limited. This, in part, is associated with decreased tissue-specific stem cell regeneration and replacement of damaged cells in the aged brain. The diminished ability of the aged brain to recover is especially devastating after TBI, likely leading to permanent loss of sensory, motor, and cognitive functions. Studies have shown that the mature mammalian brain contains Neural Stem Cells (NSCs), found in specific regions of the brain, which can generate functional neurons during normal and pathological conditions. Two of those regions, the Dentate Gyrus (DG) of the hippocampus as well as the Subventricular Zone (SVZ) of the lateral ventricles, have proven to be niches for these multipotent NSCs. A key regulator in the maintenance of these NSCs is the Notch signaling pathway, shown to control proliferation, differentiation, and apoptosis of NSCs during development and throughout adulthood. In the current study, we assessed the regulatory mechanisms that drive the regenerative functions of NSCs in a neuropathological state following TBI. Using the Lateral Fluid Percussion Injury model, we analyzed the diffuse effects of the injury response on 3-month old male Sprague-Dawley rats. Immediately following TBI, Notch agonist, antagonist or vehicle was infused into the lateral ventricle for 7 days to assess the role of Notch signaling on neural stem cell proliferation/survival and neurogenesis at 30 days post-TBI. Dividing cells during infusion time were labeled with BrdU via single daily intraperitoneal injections for 7 days. Animals were sacrificed at 30 days post-injury and brain tissues were processed then immunolabeling for BrdU and Doublecortin. We found a higher number of BrdU-positive cells in the FPI+Notch1 agonist group when compared to Sham and FPI+Jagged-1 Fc antagonist groups in the contralateral granular zone. A significant increase in proliferation/survival was also seen in FPI+Notch1 versus Sham/FPI+Jagged-1 Fc and for FPI+Vehicle versus Sham animals in both the ipsilateral and contralateral hilus. DCX immunolabeling did not establish a significant difference in FPI+Notch1 compared to Sham animals, nor across any other groups, which is consistent with what we know of activation of the Notch pathway. Our results demonstrate that Notch1 signaling is directly involved in cellular proliferation/survival of NSCs in the DG following TBI at 30 days post-injury, but further work must be done to understand the fate of these cells. Thus, drug treatment targeting Notch1 signaling could serve as a potential therapeutic target following TBI to preserve NSCs and limit long-term cognitive deficits.

This thesis investigates the optimal use of intervention strategies to mitigate the spread of infectious diseases. Three main problems are addressed: (i) The optimal use vaccination and isolation resources under the assumption that these resources are limited. Specifically we address the problem of minimizing the outbreak size and we determine the optimal vaccination-only, isolation-only and mixed vaccination-isolation strategies. (ii) The optimal use of a single antiviral drug to minimize the total outbreak size, under the assumption that treatment causes de novo resistance. (iii) The optimal use of two antiviral drugs to minimize the total infectious burden. Specifically we address the situation where there are two different strains and each strain is effectively treated by only one drug. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2011-01-25 19:59:17.263

Current chemotherapeutic treatment schedule prediction methods rely heavily on PK/PD-based models and overlook the important contribution of tissue-level transport and binding. Tissue-level transport and binding phenomena are essential to understanding drug delivery and efficacy in tumors. Drugs with desirable PK/PD properties often fail in vivo due to poor tissue-level transport. We developed an in silico method to predict the effect of treatment schedule on efficacy that couples PK/PD with tissue-level transport. Treatment schedules were implemented on theoretical drugs with different PK/PD and transport properties. For each drug with a given clearance rate, diffusivity, and binding, treatment schedules consisting of one to 20 doses were simulated. Results show that at binding constants around one, high diffusivities, and high clearance rates, implementation of a treatment schedule becomes more significant. At low clearance rates, regardless of tissue-level transport and binding, one dose was predicted to be most efficacious. Tissue Drug Exposure (TDE) was shown to be to a crucial factor for treatment schedule efficacy. Efficacy was improved by increasing TDE. Implementation of a treatment schedule with more doses than one curbed the effect of poor retention with drugs. This model investigates the effect of treatment schedule on a tissue transport model and shows implementation of a proper dosing regimen is crucial to maximize TDE and chemotherapeutic efficacy.

This dissertation introduced several issues concerning the analysis of diseases by showing how modeling and simulation could be used to assist in creating health policy by estimating the effects of such policies. The first question posed was how would education, vaccination and a combination of these two programs effect the possible outbreak of meningitis on a college campus. After creating a model representative of the transmission dynamics of meningitis and establishing parameter values characteristic of the University of Central Florida main campus, the results of a deterministic model were presented in several forms. The result of this model was the combination of education and vaccination would eliminate the possibility of an epidemic on our campus. Next, we used simulation to evaluate how quarantine and treatment would affect an outbreak of influenza on the same population. A mathematical model was created specific to influenza on the UCF campus. Numerical results from this model were then presented in tabular and graphical form. The results comparing the simulations for quarantine and treatment show the best course of action would be to enact a quarantine policy on the campus thus reducing the maximum number of infected while increasing the time to reach this peak. Finally, we addressed the issue of performing the analysis stochastically versus deterministically. Additional models were created with the progression of the disease occurring by chance. Statistical analysis was done on the mean of 100 stochastic simulation runs comparing that value to the one deterministic outcome. The results for this analysis were inconclusive, as the results for meningitis were comparable while those for influenza appeared to be different.

Doctor of Philosophy / Department of Plant Pathology / Erick D. DeWolf / Stripe rust (caused by Puccinia striiformis f. sp. tritici) and leaf rust (caused by Puccinia triticina) are the top two diseases of winter wheat (Triticum aestivum) with a 20-year average yield loss of 4.9% in Kansas. Due to the significant yield losses caused by these diseases, the overall objective of this research was to identify environmental variables that favor stripe and leaf rust epidemics. The first objective was to verify the environmental conditions that favor P. triticina infections in an outdoor field environment. Wheat was inoculated with P. triticina and exposed to ambient weather conditions for 16 hours. Number of hours with temperature between 5 to 25°C and relative humidity >87% were highly correlated and predicted leaf rust infections with 89% accuracy. The results of this outdoor assay were used to develop variables to evaluate the association of environment with regional leaf rust epidemics. Before regional disease models can be developed for a forecast system, suitable predictors need to be identified. Objectives two and three of this research were to identify environmental variables associated with leaf rust and stripe rust epidemics and to evaluate these predictors in models. Mean yield loss on susceptible varieties was estimated for nine Kansas crop reporting districts (CRD’s). Monthly environmental variables were evaluated for association with stripe rust epidemics (>1% yield loss), leaf rust epidemics (>1% yield loss), severe stripe rust epidemics (>14% yield loss) and severe leaf rust epidemics (>7% yield loss) at the CRD scale. Stripe rust and leaf rust epidemics were both strongly associated with soil moisture conditions; however, the timing differed between these diseases. Stripe rust epidemics were associated with soil moisture in fall and winter, and leaf rust epidemics during winter and spring. Severe stripe rust and leaf rust epidemics were associated with favorable temperature (7 to 12°C) and temperature (15 to 20°C) with relative humidity (>87%) or precipitation in May using tree-based methods of classification, respectively. The preliminary models developed in this research could be coupled with disease observations and varietal resistance information to advise growers about the need for foliar fungicides against these rusts in Kansas winter wheat.

Tissue engineered blood vessels (TEBVs) have great potential as tools for disease modeling and drug screening. However, existing methods for fabricating TEBVs create homogenous tissue tubes, which may not be conducive to modeling focal vascular diseases such as intimal hyperplasia or aneurysm. In contrast, our lab has a unique modular system for fabricating TEBVs. Smooth muscle cells (SMCs) are seeded into an annular agarose mold, where they aggregate into vascular tissue rings, which can be stacked and fused into small diameter TEBVs. Our goal is to create a platform technology that may be used for fabricating focal vascular disease models, such as intimal hyperplasia. Because tubes are fabricated from individual ring units, each ring can potentially be customized, enabling the creation of focal changes or regions of disease along the tube length. In these studies, we first demonstrated our ability to modulate cell phenotype within individual SMC ring units using incorporated growth factor-loaded degradable gelatin microspheres. Next, we evaluated fusion of ring subunits to form composite tissue tubes, and demonstrated that cells retain their spatial positioning within individual rings during fusion. By incorporating electrospun polycaprolactone cannulation cuffs at each end, tubes were mounted on bioreactors after only 7 days of fusion to impart luminal medium flow for 7 days at a physiological shear stress of 12 dyne/cm2. We then created focal heterogeneities along the tube length by fusing microsphere-containing rings in the central region of the tube between rings without microspheres. In the future, microspheres may be used to deliver growth factors to this localized region of microsphere incorporation and induce disease phenotypes. Due to the challenges of working with primary human SMCs, we next evaluated human mesenchymal stem cells (hMSCs) as an alternative cell source to generate vascular SMCs. We evaluated the effects of microsphere-mediated platelet-derived growth factor (PDGF), fibroblast growth factor (FGF), and transforming growth factor beta-1 (TGF-β1) delivery on ring thickness, proliferation, and contractile protein expression over a 14 day period. Finally, we created a structurally distinct region of smooth muscle within tissue tubes by fusing human aortic SMCs in a central region between hMSC rings. In summary, we developed a platform technology for creating modular tubular tissues that may be further developed into an in vitro intimal hyperplasia model. It may also be modified to model other focal vascular diseases, such as aneurysm, or to create other types of multi-tissue tubular structures, such as trachea.

The derivation of pluripotent stem cells (now termed induced pluripotent stem cells, iPSC) from mature somatic cells was a finding of seminal importance to fundamental cell biology. Thus established iPSC technology has been predicted to advance fields that previously relied on the ethically disputed use of embryonic stem cells. Being pluripotent (able to differentiate into every cell type present in the human body) and sharing most other characteristics with embryonic stem cells, but being much readier obtainable and their derivation free from ethical restraints, human induced pluripotent stem cells (hiPSC) provide access to cell types and insights into cell processes previously unattainable to researches. For this thesis, a hiPSC line was established from a skin biopsy donated by a Down’s syndrome patient. Most of what is known today about the molecular neurobiology behind this disease has been gathered from mice models or human post mortem studies, but this has a limited extrapolation potential to early human brain development in DS patients, as Down’s syndrome is an inherently human disease whose defining phenotype is established early during embryonic development. Having access to human pluripotent cells able to recapitulate the events of early neurogenesis is thus invaluable to the understanding of the mechanisms of this disorder. In parallel, work has been performed on optimizing iPSC reprogramming protocol. By exchanging one of the transcription factors used for reprogramming with a reporter gene, genomic integration of reprogramming factors has become possible to be traced visually, enabling more efficient selection of reprogrammed iPSC colonies.

Modelling is a powerful method for understanding complex systems, which works by simplifying them to their most essential components. The choice of the components is driven by the aspects studied. The tool chosen to perform this task will determine what can be modelled, the maximum number of components which can be represented, as well as the analyses which can be performed on the system. Performance Evaluation Process Algebra (PEPA) was initially developed to tackle computer systems issues. Nevertheless, it possesses some interesting properties which could be exploited for the study of epidemiological systems. PEPA's main advantage resides in its capacity to change scale: the assumptions and parameter values describe the behaviour of a single individual, while the resulting model provides information on the population behaviour. Additionally, stochasticity and continuous time have already proven to be useful features in epidemiology. While each of these features is already available in other tools, to find all three combined in a single tool is novel, and PEPA is proposed as a useful addition to the epidemiologist's toolbox. Moreover, an algorithm has been developed which allows converting a PEPA model into a system of Ordinary Differential Equations (ODEs). This provides access to countless additional software and theoretical analysis methods which enable the epidemiologist to gain further insight into the model. Finally, most existing tools require a deep understanding of the logic they are based on and the resulting model can be difficult to read and modify. PEPA's grammar, on the other hand, is easy to understand since it is based on few, yet powerful concepts. This makes it a very accessible formalism for any epidemiologist. The objective of this thesis is to determine precisely PEPA's ability to describe epidemiological systems, as well as extend the formalism when required. This involved modelling two systems: the bubonic plague in prairie dogs, and measles in England and Wales. These models were chosen as they exhibit a good range of typical features, allowing to thoroughly test PEPA. All features required in each of these models have been analysed in detail, and a solution has been provided for representing each of these features. While some of them could be expressed in a straightforward manner, PEPA did not provide the tools to express others. In those cases, we determined methods to approach the desired behaviour, and the limitations of said methods were carefully analysed. In the case of models with a structured population, PEPA was extended to simplify their expression and facilitate the writing process of the PEPA model. The work also required the development of an algorithm to derive ODEs adapted to the type of models encountered. Finally, the PEPAdum software was developed to assist the modeller in the generation and analysis of PEPA models, by simplifying the process of writing a PEPA model with compartments, performing the average of stochastic simulations and deriving and explicitly providing the ODEs using the Stirling Amendment.

Novel technologies for genome analysis have provided almost unlimited opportunities to uncover structural gene variants behind human disorders. Whole exome sequencing (WES) is especially useful for understanding rare Mendelian conditions, because it reduces the requirements for a priori clinical data, and can be applied on a small number of patients. However, supporting functional data on the effect of specific gene variants are often required to power these findings. A variety of methods and biological model systems exists for this purpose. Among those, induced pluripotent stem cells (iPSCs), which are capable of self-renewal and differentiation, stand out as an alternative to animal models. In papers I and II we took advantage of WES to identify gene variants underlying autosomal recessive pure hair and nail ectodermal dysplasia (AR PHNED) as well as autosomal dominant familial visceral myopathy (FVM). We identified a homozygous variant c.821T>C (p.Phe274Ser) in the KRT74 gene as the causative mutation in AR PHNED, supported by the fact that Keratin-74 was undetectable in hair follicles of an affected family member. In a family segregating FVM we found a heterozygous tandem base substitution c.806_807delinsAA (p.(Gly269Glu)) in the ACTG2 gene in the affected members. This novel variant is associated with a broad range of visceral symptoms and a variable age of onset. In Paper III we explored the similarity between clonally derived iPSC lines originating from a single parental fibroblast line and we highlighted the necessity to use lines originating from various donors in disease modeling because of biological variation. Paper IV focused on how the genomic integrity of iPSCs is affected by the choice of reprogramming methods. We described several novel cytogenetic rearrangements in iPSCs and we identified a chromosome 5q duplication as a candidate aberration for growth advantage. In summary, this doctoral thesis brings novel findings on unreported disease-causing variants, as supported by extensive genetic analysis and functional data. A novel molecular mechanism behind AR PHNED is presented and the phenotypic spectrum associated with FVM is expanded. In addition, the thesis brings novel understanding of benefits and limitations of the iPSC technology to be considered for disease modeling.