Agrobacterium tumefaciens at Oregon State University

In memory of the late Professor Walt Ream, whose passion for microbiology was infectious and whose mentorship inspired countless students, including me.

The Spark: Finding a Needle in a Haystack

My junior year at Oregon State University was a turning point. I had the incredible opportunity to join Professor Ream’s lab, and I was immediately drawn into a real-world problem: how do you get rid of a problematic microbe in a complex environmental substrate like soil? The culprit was Agrobacterium tumefaciens, the causative agent of crown gall disease and the work-horse of genetic modification of plants. Before we could even thought about eliminating it, we wanted to be able to find it, even at incredibly low levels.

This led to the first part of my project: developing a highly sensitive nested PCR assay. The goal was to create a tool so precise it could detect a single cell of A. tumefaciens in a gram of soil (although we fell short at 50 cells). It was my first taste of true research: designing an experiment to answer a question no one had an answer for yet. I was hooked. The thrill of seeing something new, of creating a tool that worked, solidified my path. I knew I wanted to pursue research, and it gave me the drive to focus on my studies, though I ultimately fell just shy of the 3.0 GPA required by many graduate schools.

Read more about the development of this nested PCR procedure in my blog post “Nested PCR to Detect Pathogenic Agrobacterium tumefaciens in Soil Samples.”

The Challenge: Can You Predict Survival in a Heat Wave?

With a reliable detection method in hand, we moved to the core challenge. Could we use soil solarization, to eliminate our target microbe? This wasn’t just about cranking up the heat; it was about building a predictive model that could tell us exactly how the population would respond to the fluctuating temperatures of a hot summer day, with soil temperatures reaching nearly 52°C.

To do this, we had to go back to the fundamentals of thermal death kinetics. I meticulously established the D-values (the time required to kill 90% of the population at a specific temperature) and the Z-value (how much the temperature needed to increase for a tenfold increase in the killing rate). This foundational work required true dedication. To capture the precise death curves, I spent several consecutive days and nights in the lab, taking samples around the clock. Sleeping on a couch in the break room, I learned firsthand the persistence required to gather clean, reliable data.

The Twist: Biology Defies the Model

Armed with our D and Z-values, we programmed our incubators to simulate an average diurnal heat cycle. The initial result was a resounding success. After the first 24-hour cycle, the die-off of A. tumefaciens matched our model’s prediction almost perfectly. It was a thrilling moment where the math beautifully described the biology.

But then, biology threw us a curveball.

The killing effect plummeted in the second and third cycles. By the fourth and fifth days, the population stabilized, refusing to drop further despite the repeated heat stress. Our model, so perfect for the initial exposure, could no longer predict the outcome.

To read more about the results of this experiment read my blog post “Thermal Death Kinetics of Agrobacterium tumefaciens in a Complex Substrate.”

The Lesson: A Foundation for the Future

This surprising result was the most valuable part of the project. It taught me a lesson that has shaped my entire career in quality assurance: a model is only as good as its assumptions, and biology is full of surprises. We hypothesized that the initial heat wave had created a powerful selection event, leaving behind a smaller, but much hardier, sub-population.

My dedication to seeing this project through was recognized by the faculty and earned me a Microbiology Faculty-Supported Undergraduate Student Scholarship. More importantly, this experience was my first deep dive into building predictive models from empirical data. The principles I learned here are the same ones I now apply to challenges like tunnel pasteurization in the beverage industry. It was the project that, despite my academic stumbles, truly forged my identity as a scientist.