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Shireesh A

- Research Program Mentor

PhD Doctor of Philosophy


Pharmaceuticals, Inorganic Chemistry, Genetic engineering, Chemical engineering, Renewable energy, Microbiology, Food science, Public health, Cancer research, Immunology, Colloid Science, Experimental design, Biomedical innovation, Plant biology, Environmental engineering, cryptocurrency, decentralized finance and related topics

Project ideas

Project ideas are meant to help inspire student thinking about their own project. Students are in the driver seat of their research and are free to use any or none of the ideas shared by their mentors.

A model to rank viruses based on their probability to cause the next pandemic.

A list of 106 zoonotic viruses was compiled. The receptor binding domain (RBD) as well as the host entry receptor(s) (HER) for the 106 zoonotic viruses, 46 human infective viruses and 54 human/animal host entry receptors were obtained from the literature and/or from the UNIPROT database. Ten training virus pairs whose vaccines were cross-reactive (for each pair) were BLASTed to find similarities. The values of the maximum score, total score, query cover, percent identity, and E-value for each virus pair were taken as input parameters to formulate an equation whose output; the cross reactivity value (CV); was indicative of the extent of cross reactivity between the virus pair. A CV < 1 indicated that the RBDs’ of test virus or entry receptor pairs were similar and hence the pair would have cross-reactive antibodies. The CV between each zoonotic virus and 46 human infective viruses was determined. For those pairs where the CV < 1, a Virus Component Number (VCN) was calculated. The VCN was the product of the affinity; i.e the inverse of the average of CV values for those pairs whose CV <1 and the avidity; i.e. the number of virus pairs for which the CV < 1. Similarly, the probability of a virus infecting the host was determined by using HER similarity scores obtained through BLAST for the receptors of 57 viruses along with obtaining the Receptor Component Number (RCN). A relationship was identified between the number of receptors for a virus and its RCN, and this equation was used to find all subsequent RCNs. The VCN and RCN for each virus were added, resulting in a Total Score that was ranked from greatest to least. Viruses that are highly prevalent in the human population were excluded, and the top ranked 11 emerging viruses were selected because they had individual case fatality rates of > 50%. Based on the VCN and RCN values, our model can predict which existing vaccines would be cross reactive against the emerging viruses, hence pre-empting and preventing pandemic genesis by vaccinating geographically susceptible populations. Additionally, the list is small enough that resources can be mustered to research, manufacture and stockpile vaccines and/or antiviral drugs against these eleven viruses. The model is a much needed valuable tool in the as yet sparse toolkit of predicting and preventing pandemics caused by lethal zoonotic viruses. This project is complete and has been published in the Journal of High School Science at

Increasing the resistant starch content of French Fries using dehydration and retrogradation.

French fries are a high-glycemic food that is ubiquitously available and excessively consumed. This study utilized the processes of dehydration and retrogradation of potatoes before frying to increase the content of resistant starch (RS) in French fries. RS is not digested or absorbed in the upper gastro-intestinal tract and passes to the large intestine where it is fermented into largely beneficial products by the microbiome. An increase in RS potentially translates into a lower-glycemic index and calorific value, thereby mitigating the onset and symptoms of metabolic disease. This project is complete and is published in the Journal of High School Science at

Expression of an antimalarial peptide by plasmid transfected Yogurt bacteria I: feasibility, experimental design and measurement

The feasibility of making ‘antimalarial yogurt’ was explored. A plasmid containing the gene for the antimalarial peptide, NK2, linked to a nonapeptide expressing linker and a gene expressing a signal peptide USP45 to facilitate the export of NK2 out of the bacterial cell; an antibiotic resistance gene as well as a gene expressing the protein UL16 to render the bacterial cell resistant to the expressed NK2 was designed to be cloned into Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus; the two bacterial species present in the yogurt of choice. An inexpensive electroporation apparatus was identified for transformation of the yogurt bacteria with the plasmid. Yield calculations showed that an adult would need to ingest two 5.3 ounce servings of recombinant yogurt per day to achieve a therapeutic concentration of NK2 in blood. Expression of NK2 was demonstrated by performing preliminary hemolysis experiments using sheep blood with and without incorporation of yogurt. A literature search indicated that Plasmodium bergheii, a species that is non-infective in humans, could be cultured in vitro using sheep blood thus eliminating the need to procure and house live rodents. The school chemistry laboratory was classified as being biosafety level 1, thereby allowing material transfer agreements with vendors shipping the plasmodium parasite, plasmids and blood. This project is complete and is published in the Journal of High School Science at

Project 4

Fungible crypto-token to reduce the interest rate on Student Loans

The crypto-token addresses students' need to lessen their debt burden by multiple mechanisms: The token will allow students to decrease the interest rate on their loan amount thereby reducing the cost of servicing the debt . This is effectively analogous to buying points to reduce mortgate rates, with the exception that points can be bought anytime within the term of the loan; the reduction in interest rate applying to the remaining loan term. The air-dropped tokens (equivalent to 0.01% of the loan amount per year for the term of the loan) assure students of decreasing the amount of interest rate dollars they have to pay back by 38% - even if they do nothing with the tokens but exchange them for decreases in interest rates. Because the tokens are air-dropped every year, they also act as a hedge against cryptocurrency appreciation being able to buy lesser reduction in interest rates. The present buyers of the token are effectively 'locking in' the interest rate at today's prices and/or giving their children an opportunity to fund part of their own education so that they - in their turn - borrow less. Hence, present buyers of debt fund the education of students currently in college. Supply and demand adjusts the price of the cryptotoken. A greater demand increases the price and is beneficial to currently enrolled students because they can convert their tokens into cash and pay off substantial amount of outstanding student loans, or can self- fund part of their education so that they need borrow less money. Since the government holds 10% of all issued cryptotokens, it effectively makes money when the price of the token goes up. This is in stark contrast to all the suggested plans so far - where the government loses money, adds more debt and decreases the buying power of the dollar for future generations. Existing debt need not be forgiven since future debt - starting immediatly will be passed on to people higher - up in the tax bracket This project is not yet started.

Coding skills

None, I know BASIC, but that is a dead language now

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