Mentor: Dr. David Prescott
Through twenty-five years of research, the tumor suppressor protein, p53, has been found to be at the center of a complex biochemical web of pathways to serve as one of the body’s natural defenses against cancer. It is encoded by the gene, p53 . The gene is located on the chromosome 17 within a 20kb region containing 11 exons. The most well understood functions of the wild-type p53 gene include induction of apoptosis and cell-cycle arrest, but it also functions to induce cellular senescence, differentiation, DNA repair, and serves as a transcriptional activator for other genes. Known as the ‘gatekeeper of the cell,’ p53 gets its name from the fact that it is a protein that weighs approximately 53 kDa.
Normally, cellular levels of p53 remain relatively low, but under conditions of stress and DNA damage, however, p53 levels surge to bring about the halt of the cell cycle to repair the damage or it induces apoptosis, when the DNA is damaged beyond repair. In some cancers, p53 is present but not functional. In either case, without the presence of functional p53, these cells grow unchecked and could potentially lead to cancer. It is necessary, therefore, to maintain high levels of functional p53 in the cell under conditions of stress to control proliferated growth of dysfunctional cells.
The main research that is done in Professor David Prescott’s laboratory is to detect mutations in p53 from malignant human ovarian cells and to make a statistical inference to their frequency in a given location. Since a majority of the mutations occur in areas known as hot spots, we look specifically at the sequences of the hot spots, which are exons 5-9. Through the analysis, we hope to make a substantial contribution to confirm the statistical data that is currently available.
As a mathematics and biology major, I am interested in using mathematical methods to solve biological problems or explain biological phenomena. Currently, we are determining mutational states of p53 , but I am interested in the approach from a computational perspective. To this end, we are continuing PCR-based sequence alalysis that was done by my predecessors but I am also researching how databases such as GenBank process and assimilate these kinds of data. I doing so, I hope to define a meaningful measure of distance between two sequences of DNA from the mathematical patterns that we find in the DNA sequences that have been analyzed previously. With the use of computers and algorithms with parameters that are tailored to the kinds of mutations in the locations that are of significance, we could potentially be able to sort through the vast amount of information in our sequence database. This information has the potential to determine the prognosis of a patient or to tailor drug therapy regimens to the specific needs of a patient in a matter of seconds.