During my undergraduate genetics course, I was asked to present the flow of genetic information.

Explain the central dogma clearly to classmates with varied backgrounds.

I described how DNA is transcribed into RNA, which is then translated into protein, emphasizing the unidirectional flow and exceptions such as reverse transcription in retroviruses.

My peers grasped the concept, and the professor highlighted my explanation as a model for the class.

In a lab interview, the panel asked me to compare cell types quickly.

Provide a concise yet comprehensive comparison.

I listed structural, genetic, and metabolic distinctions, using a table format in my mind.

The interviewers noted my organized response and moved on to deeper questions.

During my senior thesis, I wanted to assess the impact of invasive plant species on native pollinator visitation rates.

Design a field experiment that isolates the effect of the invasive species while controlling for confounding variables.

I selected paired plots (invaded vs. non‑invaded) across three habitats, standardized flower abundance, used timed observations for pollinator visits, and randomized plot order each day to reduce observer bias.

Statistical analysis (ANOVA) showed a 35% reduction in pollinator visits in invaded plots (p<0.01), supporting my hypothesis and earning a departmental award.

In a lab project on enzyme kinetics, my data showed no increase in reaction rate with substrate concentration, contrary to Michaelis‑Menten expectations.

Determine why the results differed and decide next steps.

I re‑checked reagent concentrations, calibrated the spectrophotometer, consulted literature for possible inhibitor presence, and repeated the assay with fresh reagents.

The issue traced to a contaminated substrate; after correction, the expected kinetic curve reappeared. I documented the troubleshooting process in the lab notebook and presented it to the team as a learning case.

For a collaborative project, I received raw FASTQ files from an RNA‑seq experiment comparing treated vs. control cells.

Process, normalize, and identify differentially expressed genes using appropriate statistical methods.

I used FastQC for quality checks, trimmed adapters with Trimmomatic, aligned reads with STAR, generated count matrices with featureCounts, imported data into DESeq2 in R, performed variance stabilizing transformation, and applied the Wald test with Benjamini‑Hochberg correction.

The analysis revealed 1,200 up‑regulated and 950 down‑regulated genes (adjusted p<0.05), which we validated by qPCR for key targets.

At a community science fair, I needed to explain my research on antibiotic resistance to high school students and their parents.

Translate technical results into an engaging, understandable story.

I created a short animated video using analogies (e.g., bacteria as 'invaders' and antibiotics as 'defense weapons'), highlighted key findings with simple graphs, and used everyday language to describe mechanisms.

Visitors spent extra time at my booth, asked follow‑up questions, and several parents expressed interest in supporting local stewardship programs.