Meet Gladstone: Reuben Thomas

Reuben Thomas (he/him) PhD, is the associate director of the Bioinformatics Core at Gladstone Institutes. He grew up in Hyderabad—a city in Southern India—before moving to Mumbai, the financial and film capital of India, where he majored in engineering at the Indian Institute of Technology.

What brought you to Gladstone?

My transition from mechanical engineering to helping decipher human disease at Gladstone may seem strange—but it was driven by what really excites me, as well as serendipity.

I’ve been motivated by applied mathematics since my undergrad. And then serendipity kicked in.

When searching around for thesis topics, a conversation with my PhD advisor led me to work on applying mathematical optimization techniques to the problem of gene regulatory network inference. This was my first ever foray into biology, which eventually led me to the NIH for a postdoc, then to UC Berkeley, where I worked on projects related to biology and human disease.

I also spent part of my time at Berkeley teaching courses in statistics.

While at Berkeley, I was looking for a more permanent position that would take advantage of my skills and also help me develop in my own career. I applied for an open position in the Bioinformatics Core at Gladstone, and I’m so grateful I got the job because now, 10 years later, I’m still here and enjoying every bit of it.

What do you like about Gladstone?

I love the fact that I’m surrounded by people who are genuinely interested in their own topic of research, not just for the sake of basic science but because they’re motivated by the possibility of curing human disease.

The nature of my position allows me to contribute to the understanding of diseases ranging from congenital heart disease to Alzheimer’s disease, Huntington’s disease, HIV, and COVID-19. As I embark on projects with different labs at Gladstone, I feel the rush and excitement of learning new aspects of human biology while also contributing my own expertise. I’m also really appreciative that I have the independence to propose solutions, and that my suggestions are valued by the people I work with.

And last but definitely not least, I’m so thankful to be among this group of really talented statisticians, bioinformaticians, and software engineers that make up the Bioinformatics Core.

How does your research contribute to understanding or treating specific diseases?

While I work on projects where the focus is often on a specific disease, my contribution is first helping design appropriate experiments and needed tools. Then, I make sure appropriate statistical models were used to answer the scientific question at hand, and I help interpret the results: Is this a real finding? Does this represent a reproducible result? Can other experiments be performed to validate initial findings?

How do advancements in technology impact the way you conduct your research?

There’s a constant flurry of developments happening in my field—whether it’s new methods to tackle old and new problems, or new ways of thinking about particular data types. Part of my role at Gladstone is also to be an educator and teach workshops throughout the year. This requires me to stay up to date and aware of what’s being published.

Beyond these methodological advances, there are other very useful developments in how computational research is practically implemented and how to ensure your findings are reproducible. I try to imbibe as many aspects of these best practices in my day-to-day work.

How do you collaborate with other researchers, both within Gladstone and externally?

The nature of my position is that, at any given moment, I’m in the midst of not one but many collaborations with groups and labs within Gladstone, externally at UC San Francisco, and also sometimes in industry.

I particularly enjoy collaborations where I’m given a lot of background knowledge, not just about what would be directly relevant to the question being asked—which is of course most important—but also about the general field: What is known? What are the consequences of answering the specific questions the researchers are asking?

I personally think that it’s such a great position to be in. It’s like you’re part of in-depth scientific discussions in multiple labs. Practically, we handle these collaborations with constant back and forth of questions, hypotheses to be tested, sharing and interpreting results, holding regular meetings to make sure we’re all on the same page, and creating write-ups of the methods used that hopefully one day make their way into a peer-reviewed journal.

Who has been your biggest influence in your scientific career?

The advisors I’ve had throughout my career have played a critical role in shaping me into the scientist I am. I learned perseverance from my PhD advisor, with whom I would have hours-long meetings in order to understand one concept or idea. I gained the ability to think about the big picture from my postdoc advisor at the NIH, and finally my advisor at UC Berkeley gave me a really intuitive way of thinking about statistics.

What do you do when you’re not working?

I read a lot of fiction and non-fiction. I recently finished re-reading this amazing novel in verse titled Golden Gate by Vikram Seth. I also love hiking on nature trails and also going on walks around our city. San Francisco is really a walker’s dream with such diverse neighborhoods, stairways, and amazing natural beauty—all within a 7x7 square mile region.

What advice would you give to young scientists or students interested in your field?

I would consider my field of expertise to encompass statistics, bioinformatics, and computational biology. My advice for someone interested in these areas is to first sincerely study, and then to never forget the theory and intuition underlying many of the methods implemented in these fields. Otherwise, you would feel inundated with a seemingly infinite number of choices of approaches you could use to tackle a given problem. People in our domain should also have the ability to objectively choose an approach, and that means being able to explain, at least intuitively, why it works for a given problem.