Q&A with Vinu Parisutham

Vinu Parisutham is a Research Scientist in Rob Brewster’s Lab

 

 

Tell us a little bit about yourself. Where are you from? Tell us about your journey to your current position.

I come from a small town in the southern part of India and grew up with two sisters—an elder and a younger one. I was raised in a traditional family where women were not typically encouraged to pursue higher education or careers outside the home. Most women in my extended family still follow long-standing customs such as community-based arranged marriages and being homemakers.

Despite these norms, my younger sister and I chose a different path. We are the first women in our extended family to attain higher education, work outside the home, and marry beyond traditional expectations. It hasn't been an easy journey—we’ve often faced resistance—but we’ve also found moments of encouragement from within our family.

Today, I live in the U.S. with my husband and son. I continue to challenge generational expectations while staying connected to my cultural roots and values. Thanks to my incredibly supportive spouse, I was able to shift from thinking “my career will depend on where and who my spouse will be” to “my spouse is flexible, and I am in charge of what I do.” That shift has made all the difference.

What motivated you to become a scientist?

I had always aspired to become a physician and serve humanity. However, I was unable to secure a medical seat after high school—which is a highly competitive and a common outcome in India. Instead, I chose to pursue biotechnology. My first year in engineering college was a roller coaster, as I navigated through a broad range of engineering subjects.

Everything changed in my second year when I was introduced to molecular biology. Learning the intricate details of the Central Dogma opened a completely new perspective for me. It was just a few years after the first human genome had been sequenced, and reading Genome: The Autobiography of a Species in 23 Chapters was a game changer. I became deeply inspired by the work of the Kornbergs (the father-son duo), George Church, and Craig Venter—I even had their photos on my dorm wall.

I was also fortunate to attend a seminar by Spencer Wells on The Journey of Man, which introduced me to the fascinating principles of human evolution. Driven by curiosity, I dove deep into genetics and evolutionary biology, preparing rigorously for one of India’s most prestigious science exams, the CSIR-NET.

Books like Microcosm brought me closer to the emerging field of synthetic biology and the BBC documentary on After Life: the strange science of decay brought me close to microbes, which I eventually pursued for my Ph.D. in South Korea. Toward the end of my doctoral studies, a course on the Physical Biology of the Cell helped crystallize my scientific identity in quantitative biology and laid the foundation for the researcher I am today.

Why did you start working on this project? What first drew you to the question?

This paper started from a completely different perspective - to apply the general framework of gene regulation (that we have used for close to a decade to model σ⁷⁰ regulated promoters)  to alternate sigma factors. While σ⁷⁰ has a long history of research and defined parameters described in relation to gene expression changes,  it is extremely difficult to even get an estimate of these values for other sigma factors.  Thinking about how to tackle this problem I re-wrote the promoter strength in the general model in terms of constitutive expression which redefined the general framework. At that time I thought this was the dumbest, ugliest way to describe the problem, but I told myself at least we have only three parameters to worry about. When I hesitantly approached Rob with this idea he was overjoyed by the simplification and even further simplied it to what we use in the paper to describe how different responses collapse two unique behaviors.

While still trying to understand and digest this form of our model, Rob and Sunil (a former graduate student in the lab) went to a conference at Cold Spring, and I began using this new approach to fit my data and finalize some of the experiments for my already half written paper on alternate sigma factors.  During that week my slack kept ringing every few minutes in a group chat between Rob, Sunil and me on how I could use all the promoters in Suni's paper (actually just a particular figure but that is already at least 150 combinations of promoters if my memory is right) to verify this relationship and how we could get that paper done in a really short time. They even started a Dropbox folder with all the figure panels laid out. I began panicking about the depth of the experiments that I would be doing and was confused if the results would be different from the supporting experiments that were already part of Sunil’s developing manuscript. I also didn’t want to give up on my σ²⁸ paper, so I was very hesitant to even start working on the experiments. My hesitation went on for a few days after Rob and Sunil returned from the conference and I was passively pushing away from doing those experiments while still pursing my σ²⁸ paper instead of directly acknowledging my hesitation. One fine day when I showed Rob the figures I made for the σ²⁸ paper and the manuscript, he took my notebook and re-laid the figures and explained to me that even if you make a manuscript out of the σ²⁸ experiments you have, the conclusions would not be very significant. He was right about that! (That work still ended up as figure 4D(ii) of the paper.)  

So, I decided to pursue my experiments for the current paper. Luckily, I had summer students and rotation students joining me and the experiments (though extensive) are simple for any beginner to learn and do. At that point the theory was elegant and simplified, we just needed more examples to fit the two different behaviors as predicted by the model. So, we thought the more hands the merrier. Every time I got a new rotation student, I gave them a couple of promoters to create a mutant library and measure it (which made it to Figure 5C of the paper – Hannah and Melina were great). When they were doing a couple of promoters, my proposed goal was some 150+ promoters in Sunil’s paper. I felt overwhelmed jumping from 96-well plates to 384-well plates for the experiment and failed keeping everything consistent. Later we decided to scale down and chose only those promoters with strongest regulation from Sunil’s paper (maybe that was a wise choice and our next paper might have answers for our simplification) and we got a good handle on the experimental work.

What was the most exciting moment for you, or was there a particular result that surprised you?

While performing the above experiments we were faced with a quandry, every promoter we tried only had one type of behavior. We were left with no choice but to either keep screening until we found transcription factors (TFs) that were destabilizing (thus far, all of the TFs we tested were stabilizing RNA polymerase (RNAP)) or use LacI because LacI was believed to function via steric hindrance AKA destabilize RNAP. We were all positive that I was going to see a destabilizing behavior for LacI and that would mark the end of our paper. However, when I did the experiment, on a Saturday morning, I was pretty much in shock! I saw a stabilizing relationship even for LacI! I had a flight to catch in two weeks (to India), and I had to walk into Rob’s office with the data that even LacI is stabilizing and all that he did with the simple repression model was wrong. Contrary to my expectation, he was glad to see that and of course asked me to do extra-extra experiments. I wanted to at least verify if the mCherry tag was causing all these effects (which would have been a bummer) before my vacation. So, I rushed to measure LacI without the mCherry tag and the data still pointed to a stabilizing effect. What a relief! I happily had a vacation with my family in India and when I came back, we had a completely new perspective for the paper. Many new panels were added to the paper as new TFs were measured. We didn’t realize the buffering effect until we started writing the paper. By the way, we still have a hard time convincing people that a decreasing line with a slope of -1 (or 1) for a plot y/x versus x means that the value of y was constant (flat). It also took us a while to realize that too.   

In 3-4 sentences can you tell us what you think are the key main findings from your work.

One of the central challenges in synthetic biology is the lack of predictability in gene regulation, which makes it difficult to design genetic circuits with consistent behavior. This unpredictability arises from the complex and often variable ways in which transcription factors (TFs) influence gene expression. To address this, we adapted a biophysical framework that quantitatively captures TF regulation through two core mechanisms: modulating RNA polymerase binding and transcription initiation. This mechanistic perspective reveals a common feature among TFs i.e. they tend to stabilize RNA Polymerase at the promoter, buffering changes to gene expression and maintaining a homeostatic expression level. Such stabilization is especially valuable in synthetic biology applications, where consistent gene expression (regardless of genetic variation or environmental changes) is essential.

Do you have any advice for other young scientists at any career stage from undergraduate through postdoc?

When your passion is shaped primarily by what's available—such as fundable projects—the ups and downs of research, including experiments and failures, can feel more draining than motivating.

What do you like to do outside of work?

Outside of work, I love spending time with my son. He’s a wonderful storyteller and a budding science enthusiast, and I enjoy capturing his world through his own voice. I’ve been documenting his stories over the years with the hope of turning them into books someday. In fact, I had my first taste of success last December when I self-published a collection of his jokes. As a family, we also share a love for travel. We take every opportunity to explore new places, try different cuisines, and experience diverse cultures together.

And finally, what’s next for you?

One of the hardest questions I often ask myself is: What do I truly want next in my career? When I began my postdoctoral journey, I was very committed to the idea of becoming a professor. But after spending over nine years as a postdoc and research scientist, I’ve had the time and space to better understand my strengths, limitations, and, most importantly, what I genuinely enjoy. I’ve realized that I’m more passionate about learning than teaching—I see myself as a lifelong learner. Right now, I’m excited to immerse myself in next generation sequencing technologies and extending our study to the Eukaryotic world. Looking further ahead, I even imagine pursuing a Ph.D. in pure mathematical sciences. My path may not be linear, but it’s deeply driven by curiosity, and I’m learning to embrace that.