Xelerate scholar Seeja from Bangalore, India had a persistent question that she wanted to seek answers for when she applied to participate in her Xelerate Research Program. She was hoping that the Xelerate program would help her recognize her strengths and embrace her interests. On both aspects, her purpose was met.
Seeja’s interests are in the science-oriented STEM subjects, especially statistics and biology. Her curiosity about the ever-evolving nature of pathogens and their increasing resistance against antibiotic kept her awake at nights. She wanted to know the whys and what’s of mutation, the trajectories of ESBLs and whether certain mutation pairs are enriched or underrepresented. . Seeja worked closely with the Xelerate mentor from Yale University to analyze long mutation trajectories in ESBL obtained through clinical databases or experimental work. She applied statistical models to: (1) establish the order in which mutations appear and (2) determine whether certain mutation pairs are enriched or underrepresented. There are about 2 million cases of bacterial infection each year in the US, which leads to 90,000 deaths [FDA]. The resistance to antibiotic treatment is becoming a serious health problem because pathogens have the ability to evolve resistance, which can be transferred across species, and because of a diminishing number of new antibiotics that are becoming available. Resistance in Gram-negative pathogens is largely due to the evolution of beta-lactamase mutants with an extended spectrum (known as ESBLs). These mutant enzymes degrade not only their original substrate (penicillin) but also newer antibiotics that were specifically designed to be resistant to beta-lactamase action such as cephalosporins or aztreonam. She used experimental evolution and computational techniques to analyze mutation trajectories that lead to increased ESBL activity.
Among other valuable skills that Seeja acquired from her Xelerate mentor and the Research Program, in retrospect she mentions two of them very prominently: knowing when to ask for help and recognizing the value of penning down her chain of thoughts on a book immediately. She says however raw or impossible your idea might be, write it down and from there onwards strive to make it happen. Not surprisingly, this hands-on work wasn’t a bed full of roses. Problems with extracting data, conducting her research, statistical know-hows and its application to analyze long mutation trajectories in ESBL, helped Seeja learn when to ask her professor for help.
Seeja is currently pursuing her undergraduate studies at Berkeley where is continuing her research in her areas of interest.