Carving a Niche in the Intersection of Computer Engineering and Molecular Biology

Abstract

Research at the intersection of Molecular Biology and Computer Science is evolving at a rapid pace. With newer tools like CRISPR-Cas9 making biotechnology more accessible and computational demands scaling to smaller sizes, DNA has become an ideal substrate for molecular computing. Here, a parallelized model of computing using Single input, multiple data DNA is explored. Numerous algorithms such as binary sorting, shifting, searching, and XOR are developed. An additional approach to computing through chemical reaction networks is proposed, one that allows for computing stochastically using DNA concatemers. With improvements in computing power and large data volumes being generated in bioinformatics, machine learning has taken the forefront in problems such as protein-peptide modeling. For fields such as computational immunology, reducing erroneous predictions from machine learning based tools is vital. Here the vulnerability of such tools not utilizing biochemical attributes such as hydrophobicity in their predictions is outlined. This would increase their impact in applications such as vaccine design, Furthermore, these tools were used to investigate the evolution of SARS-CoV-2 virus and its impact on Human T cell immunity. A novel approach to designing experimental controls was used to validate the preservation of T Cell epitopes in the various SARS-CoV-2 variants. These results suggest that T Cell immunity mounts a strong defense against COVID-19.