Dr. Wanding Zhou is a postdoctoral fellow and informatics expert in the labs of Drs. Peter Laird, Hui Shen and Peter Jones at Van Andel Research Institute (VARI). Most recently, his efforts have led to a new way to measure cellular age, which could one day lead to improved screening and monitoring methods for cancer (read more about it here).
Originally from Shanghai, Wanding completed his undergraduate studies at Fudan University before completing his Ph.D. in bioengineering at Rice University in Houston, Texas. Following a postdoctoral fellowship at University of Texas MD Anderson Cancer Center, he joined VARI in 2015. As an expert in informatics, he excels in using high-level computational techniques to analyze massive datasets, teasing out discoveries that otherwise would remain hidden.
We caught up with Wanding to talk about his work, what keeps him motivated and how “Big Data” fuels discovery.
How did you get into the field of cancer epigenetics?
I didn’t know much about the field when I chose my undergraduate major, but I knew that I liked informatics and genomics. Because my Ph.D. was largely focused on computer science, I didn’t have much exposure to the bench research part of biology.
That changed when I joined VARI. Before, I was primarily focused on genomics rather than epigenetics but I had some experience in informatics and the techniques required to analyze high-throughput data. Drs. Laird, Shen and Jones were the main reason I came to the Institute — they are extremely knowledgeable, not only about science but also the technologies used to study it. They’ve taught me a lot, particularly about cancer and DNA methylation, an important epigenetic modification to the DNA.
How do all of the different pieces of your scientific background work together?
I think the accumulation of experience is very important; you have to have the patience to see it all come together. It takes time to gradually or incrementally gain all of this knowledge and develop a deep scientific understanding in the field.
Sometimes I look back and many of our ideas don’t seem so profound, but it’s important to remember that it takes time to develop or integrate them with other knowledge.
How did the rise of “Big Data” impact your ability to do this research?
I think the timing of this work is actually pretty crucial; over the past five years, we’ve really seen an explosion of publicly available data from large-scale projects. Most of the data we utilized in our paper came out during this period.
This work was primarily an informatics project, and my role was to analyze and interpret all of this information — how do you visualize the data to clearly validate or negate a hypothesis? To do this, we used public datasets, including those generated by The Cancer Genome Atlas, and many others that were non-cancer related. We tried to connect the dots from multiple sources.
This research project is a great example of combining our own data with externally available datasets to discover something new. If the project had been completed three years ago, the storyline would have been slightly different. Now, thanks to access to broader data, we can see that our method reveals a more general principle that extends all the way back to early development
What is your biggest motivation?
My internal motivation is to find out truth behind phenomena that scientists have studied for decades. Maintaining your curiosity is crucial, because science can be full of frustrations. I think most of the time, you just have to press on until you see something meaningful. It’s very gratifying when you discover something new.
Curious about their findings? You can read more here.