Extending and Applying Spartan to Perform Temporal Sensitivity Analyses for Predicting Changes in Influential Biological Pathways in Computational Models

Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve- hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur

Using Emulation to Engineer and Understand Simulations of Biological Systems

Modeling and simulation techniques have demonstrated success in studying biological systems. As the drive to better capture biological complexity leads to more sophisticated simulators, it becomes challenging to perform statistical analyses that help translate predictions into increased understanding. These analyses may require repeated executions and extensive sampling of high-dimensional parameter spaces: analyses that may become intractable due to time and resource limitations. Significant reduction in these requirements can be obtained using surrogate models, or emulators, that can rapidly and accurately predict the output of an existing simulator. We apply emulation to evaluate and enrich understanding of a previously published agent-based simulator of lymphoid tissue organogenesis, showing an ensemble of machine learning techniques can reproduce results obtained using a suite of statistical analyses within seconds. This performance improvement permits incorporation of previously intractable analyses, including multi-objective optimization to obtain parameter sets that yield a desired response, and Approximate Bayesian Computation to assess parametric uncertainty. To facilitate exploitation of emulation in simulation-focused studies, we extend our open source statistical package, spartan, to provide a suite of tools for emulator development, validation, and application. Overcoming resource limitations permits enriched evaluation and refinement, easing translation of simulator insights into increased biological understanding

The Spatial Distribution and Dynamics of CXCL13 in Lymphoid Tissues

Morphogens are soluble signalling molecules that regulate a broad spectrum of biological processes. However, the distances and scales over which this regulation occurs are unclear. To date, many studies have highlighted source-sink mechanisms for morphogen gradient formation but fail to take the role of the tissue microenvironment into account. Using a systems-based approach we show that the chemokine CXCL13 is regulated by the B-cell microenvironment on distinct but interconnected levels of biological organization. CXCL13 is a key determinant of humoral immune responses, regulating the localisation of lymphocytes within lymphoid tissues. Due to a complex and dynamic interaction network occurring over broad spatiotemporal scales, mapping the spatial distribution of CXCL13 in situ is challenging. To address this we have mapped the 3-dimensional organisation of CXCL13+ stromal cells in situ using a fluorescent reporter system, identifying three distinct but interconnected stromal subsets that are unique in their network properties. We quantify CXCL13 dynamics using high-speed narrowfield microscopy in collagen matrix and lymph node tissue sections with results suggesting that diffusion is highly constrained by local tissue microanatomy. However, this data alone is insufficient to describe CXCL13 gradient formation. To consolidate this data we employ a quantitative modelling approach hybridising different techniques into a high fidelity in silico representation of the B-follicle, where immune cells can interact with stroma capable of creating and shaping complex physiological gradients. Simulation analyses and immunohistochemistry suggest that chemokine fields within the follicle are dynamic and non-uniform, with multiobjective optimization analysis suggesting that this spatial configuration is designed to promote scanning rates. Taken in concert, our data suggests that CXCL13 acts over short distances creating a complex landscape of expression. Importantly, this study provides a basis for understanding the spatial distribution of morphogens with complex binding behaviours

Proceedings of the 95th Annual Virginia Academy of Science Meeting, 2017

Full proceedings of the 95th Annual Virginia Academy of Science Meeting, May 17-19, 2017, at Virginia Commonwealth University, Richmond, VA

Neurophysiological mechanisms of sensorimotor recovery from stroke

Ischemic stroke often results in the devastating loss of nervous tissue in the cerebral cortex, leading to profound motor deficits when motor territory is lost, and ultimately resulting in a substantial reduction in quality of life for the stroke survivor. The International Classification of Functioning, Disability and Health (ICF) was developed in 2002 by the World Health Organization (WHO) and provides a framework for clinically defining impairment after stroke. While the reduction of burdens due to neurological disease is stated as a mission objective of the National Institute of Neurological Disorders and Stroke (NINDS), recent clinical trials have been unsuccessful in translating preclinical research breakthroughs into actionable therapeutic treatment strategies with meaningful progress towards this goal. This means that research expanding another NINDS mission is now more important than ever: improving fundamental knowledge about the brain and nervous system in order to illuminate the way forward. Past work in the monkey model of ischemic stroke has suggested there may be a relationship between motor improvements after injury and the ability of the animal to reintegrate sensory and motor information during behavior. This relationship may be subserved by sprouting cortical axonal processes that originate in the spared premotor cortex after motor cortical injury in squirrel monkeys. The axons were observed to grow for relatively long distances (millimeters), significantly changing direction so that it appears that they specifically navigate around the injury site and reorient toward the spared sensory cortex. Critically, it remains unknown whether such processes ever form functional synapses, and if they do, whether such synapses perform meaningful calculations or other functions during behavior. The intent of this dissertation was to study this phenomenon in both intact rats and rats with a focal ischemia in primary motor cortex (M1) contralateral to the preferred forelimb during a pellet retrieval task. As this proved to be a challenging and resource-intensive endeavor, a primary objective of the dissertation became to provide the tools to facilitate such a project to begin with. This includes the creation of software, hardware, and novel training and behavioral paradigms for the rat model. At the same time, analysis of previous experimental data suggested that plasticity in the neural activity of the bilateral motor cortices of rats performing pellet retrievals after focal M1 ischemia may exhibit its most salient changes with respect to functional changes in behavior via mechanisms that were different than initially hypothesized. Specifically, a major finding of this dissertation is the finding that evidence of plasticity in the unit activity of bilateral motor cortical areas of the reaching rat is much stronger at the level of population features. These features exhibit changes in dynamics that suggest a shift in network fixed points, which may relate to the stability of filtering performed during behavior. It is therefore predicted that in order to define recovery by comparison to restitution, a specific type of fixed point dynamics must be present in the cortical population state. A final suggestion is that the stability or presence of these dynamics is related to the reintegration of sensory information to the cortex, which may relate to the positive impact of physical therapy during rehabilitation in the postacute window. Although many more rats will be needed to state any of these findings as a definitive fact, this line of inquiry appears to be productive for identifying targets related to sensorimotor integration which may enhance the efficacy of future therapeutic strategies

Extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models

Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve- hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur

2007 GREAT Day Program

SUNY Geneseo’s First Annual G.R.E.A.T. Day.https://knightscholar.geneseo.edu/program-2007/1001/thumbnail.jp

A Case Study on the Efficacy of STEM Pedagogy in Central New York State: Examining STEM Engagement Gaps Affecting Outcomes for High School Seniors and Post-2007 Educational Leadership Interventions to Reinforce STEM Persistence with Implications of STEM Theoretic Frameworks on Artificial Intelligence / Machine Learning

STEM (science, technology, engineering, and mathematics) has gained significant notoriety and momentum in recent years. STEM literacy highlights the vital connection between an educated STEM workforce and U.S. national prosperity and leadership. STEM educational and job placement goals have been a national priority for over the past 20 years. However, the STEM gap is widening—contributing to increasing STEM pipeline leakage and the social injustice milieu of a noncompetitive workforce— undermining efforts to create prosperity and sustain global leadership. The pace of STEM jobs filled lags the rate of technological advancement and the surges in skilled STEM labor demand. The aggregate disparity over time has troubling implications. The purpose of the study was to examine the STEM gap touchpoints for a Central New York high school during the transition period upon entering college or the workforce. A qualitative case study used Lesh’s translation model as a research framework. A semi-structured, focus group protocol was employed to gain a fresh perspective on the STEM gap problem and identify purposeful interventions. A major finding was the slow pace of adopting institutional reforms that replaces standardscompetency-based learning with progressive application- and outcome-based pedagogy. The study has implications for school districts, secondary schools, and higher education teacher preparedness programs in STEM pedagogy and curriculum development. A knowledge-based, progressive STEM theoretic framework with pedagogical scaffolding is conceptualized rooted in artificial intelligence and machine learning. The study presents recommendations for school districts, secondary education teachers, state education and legislative leaders, higher education institutions, and future research