Browsing by Subject "Computational modeling"

Now showing 1 - 3 of 3
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    Application of molecular modeling techniques to study the structure, dynamics, and interactions of membrane proteins.
    (2011-08) Shi, Lei
    Membrane proteins constitute ~30% of all the genomes and ~70% of the drug targets. However, less than 1% of the entries in the protein data bank are membrane proteins. The underrepresentation of membrane protein structures limits our understanding of their functions. This thesis summarizes my effects to apply theoretical methods to understand the structure and function relationships of membrane proteins. Specifically, we developed computational techniques to interpret solution and solid-state NMR data of membrane proteins and determine their high resolution structures. We further performed molecular dynamics simulations to study their dynamics, interaction with other proteins and the lipid bilayer environment. We applied these approaches to phospholamban, which is a membrane protein that is involved in cardiac muscle relaxation by regulating Ca2+-ATPase activity. Our results provide new insights to understand how membrane proteins elicit their function.
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    Contextual Influences on Cognitive and Psychophysiological Mechanisms of Learning in Early Adolescence
    (2023-06) DeJoseph, Meriah
    Learning is a central mechanism through which early experiences shape biological and behavioral development across the lifespan. One type of learning, called reinforcement learning, is posited to support youth’s ability to engage and adapt to their unique worlds with links to long-term social and emotional outcomes. Yet, individual differences in reinforcement learning across diverse environmental and experimental contexts remains poorly characterized in developmental samples. The current dissertation study integrated reinforcement learning and dynamic systems frameworks and drew upon newly adapted methodologies to capture how cognitive and psychophysiological processes of learning are modulated by socioemotional context. In a sample of 56 youth aged 12-15-years-old, this study leveraged a within-person experimental design and quantified continuous behavior and heart rate (~700 observations per system, per person) during an adapted reinforcement learning task with stimuli that varied in socioemotional relevance. Findings revealed that compared to traditionally-used benign or non-emotional stimuli, learning from stimuli high in socioemotional arousal enhanced behavioral performance. The use of computational modeling afforded valuable insights into the differential cognitive processes and strategies youth recruited to achieve such a behavioral advantage, demonstrating that socioemotional salience may have elicited faster value-updating processes and qualitative shifts in more exploitative decision-making. Underlying psychophysiological engagement seemed to be particularly modulated not by socioemotional salience as hypothesized, but by heightened sensitivity to learning from rewards, such that faster value-updating in the context of rewards aligned with more optimal psychophysiological flexibility and organization. Taken together, this study provides an important step in clarifying the contexts and modulatory processes that serve to enhance and support the unique ways youth learn and make decisions. Open questions remain about the adaptive utility of these various patterns of behavior, cognition, and psychophysiology across a variety of learning contexts, how they are shaped by prior lived experiences across development, and how they predict later psychosocial adjustment outcomes. Such work will shed light on how youth learn from–and adapt to–different contextual demands, with the potential to inform programs and policies that support youth’s ability to adjust to their dynamically changing ecologies.
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    Development of Computational Models of Pedunculopontine Nucleus Stimulation for Clinical Trials and Mechanistic Studies
    (2016-03) Zitella Verbick, Laura
    Deep brain stimulation (DBS) in the pedunculopontine nucleus (PPN), a component of the mesencephalic locomotor region in the brainstem, has been proposed to alleviate gait and balance disturbances associated with Parkinson’s disease; however, clinical trials results have been highly inconsistent. Such variability may stem from inaccurate targeting in the PPN region, modulation of fiber pathways implicated in side effects, and lack of understanding of the modulatory effects of DBS in the brainstem. Here, we describe the development and refinement of computational models that can predict the neuromodulatory effects of PPN-DBS in both the non-human primate and human. These models included (1) brain atlas-based models that combined detailed biophysically realistic neuron and axon models with a finite element model simulating the voltage distribution in the brain during DBS, (2) high-field 7T MRI techniques to visualize and create volumetric morphologies of structures in the brainstem for use in the models, and (3) clinically relevant subject-specific computational models that incorporate the anisotropic conductivity of the brain tissue. Based on the validated results of these models, we can conclude that the neuronal pathways modulated by DBS in the brainstem are highly sensitive to both lead location and stimulation parameters. These computational models of DBS will be useful in future clinical trials, both prospectively to plan DBS lead trajectories and improve stimulation titration and retrospectively to investigate the underlying mechanisms of therapy and side effects of stimulation.