Multi-scale analyses of molecular network dynamics underlying neuronal excitability, neuroinflammation, and organismal homeostasis
Abstract
Expression patterns of molecules such as mRNA transcripts vary in both time and space. Temporal gene expression dynamics range from minutes to years, from acute to chronic disease time frames. Such dynamics vary across spatial scales, from sub-cellular compartments to organs and organisms. The research underlying this dissertation examined the contributions of dynamic molecular networks to cellular (neuron and microglia), tissue-level (CNS neuroinflammation), and organismic (multi-organ) phenotypes. At the cellular level, our studies yielded counterintuitive findings regarding feedback processes involving potassium channels in neurons and anti-inflammatory cytokines in microglia. In the context of in vivo neuroinflammation, we found that cytokine network dynamics correspond to variations in microglial morphology, in which distinct geometrical properties were associated with differential dynamics. At the organismal level, we examined the development of hypertension and found that the disease etiology involved aberrant kinetics of molecular expression across multiple organs and dysregulated network interactions. The aberrant kinetics and dysregulated network properties were prominent in the brainstem, consistent with a primary role of the autonomic nervous system in the development of hypertension. Overall these studies combined novel approaches integrating experimentation with computational analysis and mathematical modeling to generate novel results with significant implications for our understanding of neurophysiology, neuroinflammation, and organismal homeostasis.
Subject Area
Neurosciences|Biophysics|Systems science
Recommended Citation
Anderson, Warren D, "Multi-scale analyses of molecular network dynamics underlying neuronal excitability, neuroinflammation, and organismal homeostasis" (2016). ProQuest ETD Collection - Thomas Jefferson University. AAI10156617.
https://jdc.jefferson.edu/dissertations/AAI10156617
