Every week, some of our senior researchers in each department at ASDRP give public seminar presenting the current state of the field, and disseminating how their research at ASDRP fits into the broader context of the frontiers of modern science and engineering. Colloquia are public events, and anyone can join. Simply click the Google Calendar link to add the event to your calendar.
Department of Biological, Human & Life Sciences
Tuesdays @ 8:00 - 9:00 PM PST
Tuesday, October 27, 2020
Development of novel acrylonitrile inhibitors of TP53 Induced Glycolysis Regulatory Phosphatase (TIGAR) as a strategy for modulation of the p53 tumor suppressor pathway in anticancer therapy.
One of the cancer pathways that have gained attention in cancer research is the p53 pathway. During cancer cell proliferation, the p53 tumor suppressor protein activates the TP53-induced glycolysis and apoptosis regulator enzyme (TIGAR). Overexpression of TIGAR results in increased levels of glycolysis, which decreases the rate of apoptosis. Increase in TIGAR also decreases the reactive oxygen species (ROS) induced apoptosis. Apoptosis is a normally occurring mechanism for cells to shut down after damage or to combat cancerous cells. Thus, developing an inhibitor to TIGAR would mean inhibiting the overexpression of TIGAR, permitting apoptosis to occur, and fighting cancer cell proliferation. We worked in collaboration alongside the Brah and Njoo research groups to screen analogs, synthesize inhibitors, and run kinetic assays to understand the efficacy of the acrylonitrile inhibitors against TIGAR. Inhibitors that have been synthesized and ran through kinetic assays are ethyl (E)-2-cyano-3-(4-hydroxy-3-methoxyphenyl)acrylate and ethyl (E)-2-cyano-3-phenyl acrylate.
Sophia F., Dougherty Valley High School
Tallapaka Research Group
Department of Chemistry, Biochemistry & Physics
Fridays @ 8:00 - 9:00 PM PST
Friday, October 30, 2020
Studying the Therapeutic Potential of Natural Products against Diabetes Mellitus on C. elegans model.
Diabetes Mellitus is a high- risk chronic disease and is the seventh leading cause of death in the United States. There are several types of diabetes mellitus, including type 1 DM, type 2 DM, maturity-onset diabetes of the young, and gestational diabetes. Insulin acts like a key in a lock that opens up cells so that they can use glucose. Because of the lack of insulin production, cells are unable to use glucose in order to produce energy, resulting in hyperglycemia. Even though diabetes is one of the oldest diseases, no cure has been discovered; however, several drugs have been developed to manage it more effectively. Recently, natural products are becoming a major part of the pharmaceutical drug industry, and are widely used especially in the east. Some notable compounds include berberine, curcumin, and kaempferol. My summer project consists of isolating and synthesizing bioactive compounds from bitter melon including alpha- momorcharin, momorcharin, and charantin. These compounds are compared to Metformin, a popular drug for treating diabetic patients. Furthermore, I will discuss my fall project: isolating antidiabetic compounds from fenugreek and testing them on Caenorhabditis elegans through lifespan and egg-laying assays. ɑ- amylase and ɑ-glucosidase assays are in vitro assays used to test how effectively the compounds inhibit the respective enzymes. Along with this, the target- fishing in silico method and its potential in the drug discovery process will be further examined. One extension is to investigate dyslipidemia, a comorbid condition of Diabetes mellitus.
Anushka W., Fremont High School
Renganathan Research Group
Department of Computer Science & Engineering
Wednesdays @ 8:00 - 9:00 PM PST
Wednesday, October 28, 2020
Demographic Bias in Unemployment Across the U.S. during the 2020 COVID-19 Pandemic.
COVID-19 has been reported to disproportionately affect minorities in terms of mortality rate, so we inquired whether this apparent discrimination also applied to unemployment rate. Through data analysis and machine learning algorithms in Python, we investigated bias in unemployment rate trends across race/ethnicities and sex in the U.S. amidst the pandemic. Our findings may serve as guidance for government relief policies like the distribution of stimulus checks, and they may help predict how future waves of COVID-19 will influence the economy. With data retrieved from the U.S. Bureau of Labor Statistics and the U.S. Census Bureau, we performed data analysis and then applied K-means clustering on a greater dataset based on our initial findings. From our initial data analysis, we found that although the Black population entered the pandemic with the greatest unemployment rate, the Hispanic population surpassed them during the pandemic with the most drastic increase in unemployment rate. Unexpectedly, when clustering U.S. counties by race/ethnicities, predominately white counties actually had higher increases in unemployment rate than predominantly minority counties. However, when looking at the national population as a whole, predominantly minority counties were found to have a greater increase in unemployment rate.
Aditya Y., Washington High School
Mui Research Group