Course:VANT149/2023/Capstone/Science/Team14

Abstract

The P-53 tumor suppressor is essential for preventing unwanted mutations from occurring. Researchers have studied different methodologies to find the characteristics of this gene, either by analyzing its nucleotides or DNA strands as the representation of the gene’s characteristics. This study aimed to find the characteristics of specific DNA strands in the P-53 tumor suppressor gene. The experiment was conducted in two steps, mitosis simulation (simulating regular cell division that produces two daughters) and DNA clustering to find DNA strand characteristics. The experiment began by modeling mitosis replications represented as binary trees, then addressing the cancerous evolution paths to find the cancer-leading mutation point. After collecting the points, a SOM is performed to cluster the DNA strands. This process identified seven types of DNA strands that might lead to cancer-leading cell mutation. These DNA strands are then characterized further into 6 clusters of characteristics. While the mitosis replication model used in this study might not represent the actual mitosis cycle of P-53 mutation with decent accuracy, this study may provide insightful data regarding the pattern identification and clusters of P-53 DNA trends.

Biographies

Evint Leovonzko

Evint Leovonzko is a first year student at University of British Columbia which will be a computer science major in second year. He is interested in data science and machine learning. In this experiment, he studied the correlation and characteristic of P-53 DNA strand with the help of unsupervised learning.

Dany Raihan

Dany Raihan is a first-year student at the University of British Columbia. He is passionate about pursuing a computer science major in his second year of university. He is interested in Linearts, Decentralized Computing, and Transformer!

Fathan Tavrialdi

Fathan Tavrialdi is a first year undergraduate student in The University of British Columbia he is passionate in the field of Bioinformatics, He already joins UBC undergraduate research opportunity (URO) which allows him to be a mentees at the Michael’s Smith Laboratory focusing on a computational analysis of a virology phylogenesis.

Brando Tunggul Gultom

Brando Gultom is a motivated student at UBC with a deep interest in biology, chemistry, and computer science, and he aspires to major in these diverse fields. With his enthusiasm, hard work ethic, and persistent nature, he is dedicated to acquiring a comprehensive understanding of these subjects and leveraging his knowledge to make meaningful contributions in scientific research and technological advancements.