Colloquia Week

Every week, senior researchers in each department at ASDRP give public seminars 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. Click on the "Join the Colloquia" link to add the event to your calendar.

Research Shorts

Department of Biological, Human and Life Sciences

Tuesdays @ 8:00-9:00 PM PST

Tuesday, April 27, 2021

Inhibition of Amyloid Beta Aggregation via Natural Product Polyphenols

Neurodegenerative diseases, including Alzheimer’s, have long been the focal point of modern research. Till date, it has been proven that there is a direct correlation between β-amyloid protein aggregation and uncontrolled neural cell apoptosis, preludial to a majority of Alzheimer’s symptoms. A potential preventative and curative therapy to Alzheimer’s and related neurodegenerative diseases could be based on the inhibition of said β-amyloid and related prion proteins aggregation. Our research is meant to yield the most effective natural product polyphenols in said inhibition. Our in silico research assessed a large range of natural product polyphenol candidates and their potential to interact favorably with targeted proteins through docking softwares. Our in vitro and in vivo work continued testing on select polyphenols with the highest binding affinities.

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Researcher: Kayva P., Monte Vista High School

Advisor: Renganathan, Medicinal Biochemistry

Keywords: Polyphenols | Biology | Chemistry | Alzheimer’s Disease | C. Elegans, Assays

Department of Chemistry, Biochemistry, and Physics

Fridays @ 8:00-9:00 PM PST

Friday, April 30, 2021


In Silico, Synthetic, and In Vivo Approaches Toward Anti-cancer Small Molecule Drug Discovery.

Translational science, commonly called the “bench to bedside” approach, is the multidisciplinary process of turning laboratory discoveries into clinical applications. In our research this is through the combination of computer modeling, synthesis, and biological testing. This approach is especially beneficial in developing compounds targeting cancer. Here, analogs of two classes of small molecules — human motor protein kinesin Eg5 inhibitor monastrol and diterpenoid natural product andrographolide — are synthesized and studied for their anticancer potential through a variety of tools. In silico approaches like molecular docking to visualize protein binding and model organism comparative studies to assess ideal animals for clinical trials help determine the clinical potential of our compounds. In vivo approaches including MTT assays in human cancer cells and RT-qPCR to test gene expression serve as direct ways to probe the efficacy of these compounds. The complementary roles of in silico, synthetic, and in vivo tools thus hold an integral role in the advancement of molecules from the laboratory to the clinic..

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Researcher: Aishwarya Y., American High School

Advisor: Njoo, Organic & Medicinal Chemistry

Keywords: Medicinal Chemistry | Chemical Biology | Cancer | Organic Synthesis | Computational Modeling | Natural Products

Department of Computer Science and Engineering

Wednesdays @ 8:00-9:00 PM PST

Wednesday, April 28, 2021


The Application of the Shapley Value from Game Theory to Classify Cells as Malignant or Benign

The Shapley Value is the average marginal contribution of a feature value across all possible coalitions. Shapley Additive explanations (SHAP) is a game theoretic approach to explain the output of any machine learning model. SHAP values are a method of describing the “weight” or “importance” a model places on a particular feature when making a prediction for a specific data point, with positive or negative values indicating the direction of the effect. These values are computed by a game theoretical approach that quantifies the contribution of each feature within a model to the final prediction of observation, using predictive models that are based on all possible feature subset combinations that include a given feature. We used a large dataset of cells and specific characteristics of their nuclei, and applied the Shapley Value to discern which characteristics help more importance when it came to classifying a cell as malignant or benign.

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Researcher: Jagannath P., American High School

Advisor: McMahan, Quantum Physics

Keywords: Game Theory | Cancer | Shapley Value | Regression Models | Decision Trees