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, February 9, 2021
Effects of Curcumin On Known Protein Interactions in Human Cell Lines.
Malignancy, better known as cancer and one of the most infamous diseases worldwide, is propelled by an abnormal growth of cells. The mutated p53 gene is considered to be the pathogenic hallmark of cancer cells, as it leads to an, sometimes unstoppable, aggregation of cell growth. Furthermore, the MDM2 oncogene is a critical negative regulator of the p53 gene, causing the protein to degrade and inhibit its activity. Most human cancers consist of high levels of MDM2, and thus, targeting the oncoprotein is a significant therapeutic strategy when treating the disease. While there is currently no known cure for many types of cancer, chemotherapy and radiotherapy have been two prominent approaches at attempting it. Curcumin, a dietary element, is a major constituent in the process of these therapies by down-regulating the MDM2 protein through means of the PI3K/mTOR/ETS2 Pathway as determined by in silico interactive modeling and in vitro validation via cell culture. Understanding the mechanisms of curcumin will allow for further development and improvement of various cancer therapies.
Dhruv K., BASIS Independent High School
Tallapaka Research Group
Department of Chemistry, Biochemistry & Physics
Fridays @ 8:00 - 9:00 PM PST
Friday, February 12, 2021
Semi-synthesis of penicillin-type analogs and β-lactam antibiotics and structure-activity relationship of N-acyl β-lactam broad-spectrum antibiotics.
β-lactam antibiotics have been used for centuries since Alexander Flemming first isolated the natural product, Penicillin G, from a fungus. As bacteria have evolved, they have started to develop resistance to these antibiotics. Therefore, continuously developing new antibiotics is important to fight bacterial resistance. Penicillin antibiotics fight bacteria by mimicking the D-Ala-D-Ala active site region of penicillin-binding proteins (PBP). PBPs are transpeptidases used in the synthesis of bacterial cell walls. New β-lactam antibiotics can be developed through the semi-synthesis of 6-aminopenicillanic acid or by synthesizing the core β-lactam ring. Semi-synthesis efforts include acid chloride reactions with 6-aminopenicillanic acid that can be used to create a variety of β-lactam antibiotics. A Staudinger 2+2 cycloaddition or a Ugi multicomponent one-pot synthesis can also be utilized to develop novel β-lactam antibiotics with the core ring. Additionally, commercially available penicillin antibiotics can help us understand the structure-activity relationship for antibiotic efficacy. Using Kirby-Bauer assays we can understand how structure affects the antibacterial efficacy of different penicillin antibiotics. The synthesis of the β-lactam ring or penicillin-type analogs opens up the field of organic chemistry to the development of new antibiotics.
Ayeeshi P., Mission San Jose High School
Njoo Research Group
Organic, Medicinal Chemistry
Department of Computer Science & Engineering
Wednesdays @ 8:00 - 9:00 PM PST
Wednesday, February 10, 2021
Fake Review Detection with Machine Learning.
E-commerce and online shopping is a booming industry, especially during the COVID-19 pandemic. Online shopping allows customers to browse and purchase products from home using just a phone or laptop. Customers often never end up seeing their product in real life before the purchase, and are instead dependent on photos and descriptions uploaded by the seller. Thus comes the need for customer reviews: an evaluation of a product by other buyers which can influence other shoppers’ decisions to make the purchase. However, reviews can be tainted to provide a fake or unrealistic depiction of a product. Sellers can pay people or robots to leave fake reviews under competitors or their own stores to increase/decrease sales turnout. Such reviews can be harmful to the buyer or other sellers, often ending up with an unhappy customer. Using supervised datasets consisting of real and fake reviews, we can train a variety of machine learning and deep learning models to recognize attributes differentiating between the two types of reviews. Big e-commerce platforms such as Amazon, Yelp, and Tripadvisor are all common targets of fake reviews, and the implementation of fake review detection could create a more assuring shopping experience for customers. In this paper, we analyze and break down customer review data and attempt to build models that form conclusions from it.
Vajraang P. & Aaryan R., Irvington High School
Subramaniam Research Group