[PAST EVENT] Hanbing Song, Applied Science - Ph.D. Dissertation Defense

August 19, 2016
1pm - 3pm
Tucker Hall, 127A
350 James Blair Dr
Williamsburg, VA 23185Map this location
Abstract: Breathing in mammals depends on rhythms that originate from the preBotzinger complex (preBotC) of the ventral medulla and a network of brainstem and spinal premotor neurons. The rhythm-generating core of the preBotC, as well as some premotor circuits, consists of interneurons derived from Dbx1-expressing precursors but the structure and function of these networks remain incompletely understood. We previously developed a cell-specific detection and laser ablation system to interrogate respiratory network structure and function in a slice model of breathing that retains the preBotC, premotor circuits, and the respiratory related hypoglossal (XII) motor nucleus such that in spontaneously rhythmic slices, cumulative ablation of Dbx1 preBotC neurons decreased XII motor output by half after only a few cell deletions, and then decelerated and terminated rhythmic function altogether as the tally increased. In contrast, cumulatively deleting Dbx1 premotor neurons decreased XII motor output monotonically, but did not affect frequency nor stop functionality regardless of the ablation tally. This dissertation presents several network modeling and cellular modeling studies that would further our understanding of how respiratory rhythm is generated and transmitted to the XII motor nucleus. First, we propose that cumulative deletions of Dbx1 preBotC neurons preclude rhythm by diminishing the amount of excitatory inward current or disturbing the process of recurrent excitation rather than structurally breaking down the topological network. Second, we establish a feasible configuration for neural circuits including an Erdos-Renyi preBotC network and a small-world reticular premotor network with interconnections following an anti-preferential attachment rule, which is the only configuration that produces consistent outcomes with previous experimental benchmarks. Last but not least, since the performance of neuronal network simulations is, to some extent, affected by the nature of the cellular model, we aim to develop a more realistic cellular model based on the one we adopted in previous network studies, which would account for recent experimental findings on rhythmogenic preBotC neurons.

Bio: Hanbing Song is an international student from Tianjin, China. He graduated from Florida State University in 2012 major in M.S Applied and Computational Mathematics. He began his graduate study in the department of applied science at William & Mary in the fall of 2012, where he worked as a research assistant in Dr. Christopher Del Negro's lab under the supervision of Dr. Christopher Del Negro and Dr. M. Drew LaMar. His research focused on mathematical modeling of mammalian respiratory rhythmogenic circuitry on both cellular and network levels. After graduating, Hanbing will begin working as a postdoc at Franklin and Marshall College in Lancaster Pennsylvania.