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Graduate Student Seminar

Wednesday, September 27, 2017
11:00 AM - 12:00 PM
Engineering : 027
Session Chair:Randy Price
Discussant:Dr. Hye-Won Kang

Speaker 1: Janita Patwardhan
Title
Diabetes and beta cells
Abstract
Diabetes is characterized by high levels of blood glucose. Insulin, which acts as a signal to cells to absorb glucose and thus lower its levels in the blood, is secreted in a pulsatile manner by pancreatic beta cells. In diabetic patients, some of this synchronized oscillatory behavior is lost. To better understand the synchronization of insulin secretion, we studied a dynamical system modeling the electrical behavior of an islet of beta cells. Hexagonal-close-packed lattices were used to organize the cells in an islet, allowing for more neighboring cell connections compared to more often used cubic-close-pack. By correlating the calcium traces, a functional network of connections was developed. Using graph theory, small worldness was detected in the simulated islets, leading to some cells behaving as hubs for the network. This is in agreement with recent published experimental results. By electrically silencing the hub cells as done in those experiments, hub dysfunction decreases the synchronization within the islet.

Speaker 2: Iris Gauran
Title
Empirical null estimation using Zero-inflated Discrete Mixture distributions and its Application to Protein Domain Data
Abstract
In recent mutation studies, analyses based on protein domain positions are gaining popularity over gene-centric approaches since the latter have limitations in considering the functional context that the position of the mutation provides.  This presents a large-scale simultaneous inference problem, with hundreds of hypothesis tests to consider at the same time.  This presentation aims to select significant mutation counts while controlling a given level of Type I error via False Discovery Rate (FDR) procedures.  One main assumption is that there exists a cut-off value such that smaller counts than this value are generated from the null distribution.  We present several data-dependent methods to determine the cut-off value.  Simulated and protein domain data sets are used to illustrate this procedure in estimation of the empirical null using a mixture of discrete distributions.  Overall, while maintaining control of the FDR, the proposed two-stage testing procedure has superior empirical power.