Student Colloquia

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

Medicinal Biochemistry

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

Computer Science

Past Colloquia

Life Science Research & Development Laboratory:

46307 Warm Springs Blvd. Fremont, CA 94539

Engineering Research Laboratory:

46249 Warm Springs Blvd. Fremont, CA 94539

General Inquiries:


Telephone: 1(510)371-4831

© Aspiring Scholars Directed Research Program 2020, All rights reserved.

ASDRP is a production of Olive Children Foundation, a 501(c)(3) nonprofit organization in Fremont, California.

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