Lipid Regulation as a Critical Factor in the Development of Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the most common form of dementia in the United States, representing around eighty percent of all cases. For more than two decades, researchers have been led by the amyloid cascade hypothesis, which assumes that accumulation of the amyloid peptide Aβ, derived by proteolytic processing from the amyloid precursor protein (APP), is the key pathogenic trigger in AD. To date, therapies have largely focused on removing Aβ from the brain, an approach that has produced disappointing clinical outcomes. I present an alternative hypothesis in which Aβ production and aggregation is a symptom of a larger, systemic disease affecting the regulation of lipids, including cholesterol. In addition to assigning a physiological function for APP and Aβ generation, my hypothesis suggests that lipid dysregulation would likely occur early in the disease process, making it an ideal target for identification of disease risk or even intervention. Using a mouse model, I show that expression of APP is involved in the regulation of cholesterol synthesis, endocytosis, and myelination pathways. Using human cell culture models, I demonstrate that fibroblasts and peripheral blood mononuclear cells taken from AD patients show signs of lipid dysregulation, and that neuron-like cells develop this dysregulation when exposed to oxysterols. Finally, I developed and characterized a method of quantifying these detrimental changes using a fluorescence compound, filipin, which could form the basis of a commercial test to evaluate the potential risk of conversion from mild cognitive impairment to AD

Best practices to maximize the use and reuse of quantitative and systems pharmacology models: recommendations from the United Kingdom quantitative and systems pharmacology network

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment

Biosimulation of Vocal Fold Inflammation and Healing

Personalized, pre-emptive and predictive medicine is the capstone of contemporary medical care. The central aim of this dissertation is to address clinical challenges in prescribing personalized therapy to patients with acute phonotrauma. Inflammation and healing, which are innate tissue responses to mechanical stress/ trauma, are regulated by a complex dynamic system. A systems biology approach, which combines empirical, mathematical and computational tools, was taken to study the biological complexity of this dynamic system in vocal fold injury.Computational agent-based models (ABMs) were developed to quantitatively characterize multiple cellular and molecular interactions around inflammation and healing. The models allowed for tests of various hypothetical effects of motion-based treatments in individuals with acute phonotrauma. A phonotrauma ABM was calibrated and verified with empirical data of a panel of inflammatory mediators, obtained from laryngeal secretions in individuals following experimentally induced phonotrauma and a randomly assigned motion-based treatment. A supplementary ABM of surgically induced vocal fold trauma was developed and subsequently calibrated and verified with empirical data of inflammatory mediators and extracellular matrix substances from rat studies, for the purpose of gaining insight into the &ldquo net effect &rdquo of cellular and molecular responses at the tissue level.ABM simulations reproduced and predicted trajectories of inflammatory mediators and extracellular matrix as seen in empirical data of phonotrauma and surgical vocal fold trauma. The simulation results illustrated a spectrum of inflammatory responses to phonotrauma, surgical trauma and motion-based treatments. The results suggested that resonant voice exercise may optimize the combination of para- and anti-inflammatory responses to accelerate healing. Moreover, the ABMs suggested that hyaluronan fragments might be an early molecular index of tissue damage that is sensitive to varying stress levels - from relatively low phonatory stress to high surgical stress.We propose that this translational application of biosimulation can be used to quantitatively chart individual healing trajectories, test the effects of different treatment options and most importantly provide new understanding of laryngeal health and healing. By placing biology on a firm mathematical foundation, this line of research has potential to influence the contour of scientific thinking and clinical care of vocal fold injury

At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine