Quick Q&A with NCBI’s Algorithmic Methods in Computational & Systems Biology Group

In our series of Quick Q&As with scientists who work in the NLM’s National Center for Biotechnology Information (NCBI), we’re pleased to introduce the Algorithmic Methods in Computational and Systems Biology group.

This team works in three basic areas: cancer and diseases, gene regulation, and algorithms for efficient utilization of large data sets.

“This is a very strong and dedicated group,” says Teresa Przytycka, PhD, the team’s leader. “We strive to lead in developing new methods and new ways of looking at data.”

They also shared a bit of personal information about themselves. When they’re not developing new ways of looking at data, two members of the team like to climb mountains. One meditates. One stumbled upon the field of bioinformatics before knowing what it was. One has three master’s degrees. One is settling into a new home. And another sings and plays tennis.

You’ll have to read on to find out who does what.

Quick Q&A with Przytycka, Huang, and Hoinka
Question	Teresa Przytycka, PhD	Xiaoqing Huang	Jan Hoinka, PhD

In lay terms, what is the focus of your NLM research?	I develop computational methods to study biological questions enabled by large-scale data sets. In particular, I am interested in understanding the mutational landscape of cancer, and its relation to disease manifestation and progression. I also use computational methods to study cell regulation and molecular interactions.	I study cancer biology. Specifically, how do mutations play a role in cancer development, and how do operative mutational processes define the cancer? Also, with my statistical background, I am very interested in how to correct false discovery rate (FDR) in the setting of biological multiple testing and how to improve test power and interpret results with more statistical evidence.	I am developing efficient and flexible in-silico approaches for the identification, analysis, and visualization of aptamers, RNA molecules that can be designed to interact with a specific target of interest.
Why is it significant, in your opinion?	I see the importance of this work on two levels. On a more specific level it leads to better understanding of cell function and its relation to diseases. On a more general level it provides algorithms to translate big data to knowledge.	Research in this field directly helps people, especially people with cancer. With more understanding of how this cancer is different from another cancer in terms of which different mutational processes are active at various levels of intensities, we can provide some useful information for precision cancer medicine.	Due to the universality of aptamers, they can be utilized in a large array of technologies, including novel drugs, biosensors, purification systems, and many more. Integrating computational guidance into their design process will lead to an accelerated, higher quality development of novel applications.
What or who inspired you to pursue your career?	I always liked mathematics. Initially, I saw it as a source of fun questions for play. I loved the thrill that accompanied discovering the solution. The realization that one can actually do this for a living came from reading about the first women Noble medalist, Marie Curie-Sklodowska.	The more you know, the more you don’t know. There is no other career that makes you more self-confident, but more humble simultaneously, than a researcher/scientist.	Growing up in a curiosity-encouraging environment where no questions were wrong questions.
How did you get started in your career?	I started my research career by transforming my love of puzzle solving into an interest in developing new and more efficient computational algorithms. My transition to computational biology was gradual, as I increasingly realized that large-scale biological data require mathematical and computer science methods to interpret the biological causes that shaped the patterns observed in such data.	I earned my master’s degree in statistics from Texas A&M University. Before I start my PhD, I have this valuable opportunity to work with Dr. Teresa Przytycka, who inspired me into this fantastic computational biology research field.	I have always been fascinated by nature and computers, so when it was time to choose a career, it seemed logical to combine both of these passions into one.
What really gets you jazzed about science and research?	I really like finding out how things work.	The idea that you are doing something really important for humankind really excites me.	The fact that we are constantly pushing the boundaries of the unknown, and each answer opens the door to at least one other question.
If you weren’t doing this work, what other profession might you have pursued?	I think I would be exploring an area of computer science developing algorithms in a different domain than biology. Most likely coding theory. As a child, I also considered being an engineer. Looking back, if I had chosen that path, I probably would have ended up in computer programming.	Work for a better life. If I were not here to do this important research work, I might be an event planner/organizer to serve others in a different way.	I think I would end up exactly where I am again.
Tell us something surprising about yourself.	I love mountains and used to climb them using old-fashioned gear: flat iron nails that were nailed into cracks between rocks with a heavy hammer. Fortunately, I have never seriously tested how good protection that was.	I still don’t understand why I have obtained three master’s degrees over the past few years, but not one more exciting and rewarding than a PhD!	When it was time to choose my field of study, I Googled “biology informatics study,” and enrolled in the first program that popped up without knowing what it was: Bioinformatics. Twelve years later, I do not regret it one bit.

Quick Q&A with Kim and Pal
Question	Yoo-Ah Kim, PhD	Soumitra Pal, PhD

In lay terms, what is the focus of your NLM research?	My primary research focuses on computational analysis to better understand complex biological mechanisms. In particular, I work on developing and applying various computational tools to analyze whole genome profiles of a large number of cancer patients. Using genome-wide systematic analysis, we aim to uncover genotypic causes of the disease, understand the mechanism, and apply the findings to classify disease subtypes.	Designing algorithms and software tools for analyzing biological big data.
Why is it significant, in your opinion?	Identifying the cause of cancer is challenging because of the heterogeneous and complex nature of the disease. Different patients can have different genomic causes while there are similarities among them. Computational methods can make it possible to systematically analyze genomic profiles of thousands of cancer patients and help understand similarities and differences of cancer patients, which is the key to developing appropriate treatment and care plans for individual cancer patients.	Nowadays, biologists are relying more on computers for the efficient analysis of the data gathered by the instruments in their labs. This is so they can better utilize their time and energy on the crucial experiments in their scientific projects. Efficient algorithms and software tools are a must in this data analysis process.
What or who inspired you to pursue your career?	As a child, I always enjoyed thinking about complex problems and coming up with right solutions. Naturally, I loved mathematics and computer theory, which is why I pursued a PhD in theoretical computer science.	I do not recall anyone specific. All my teachers and guides starting from my early education through my graduate and postgraduate studies inspired me to pursue a career in science and technology.
How did you get started in your career?	I started my career as a theoretical computer scientist, working on network theory. I got my PhD in the area of network algorithms in 2005 and worked as a faculty member until 2008 at the University of Connecticut. I joined NIH in 2008 to work on biological network analysis, inspired by the fact that genes are interacting with each other and people started creating networks of gene interactions. Due to the large size of the networks, it has been challenging to develop efficient algorithms for biological network analysis, and I felt that my background on network theory would be extremely useful in this research.	I come from a rural area in India. In my high school, I used to like biology. However, the IT revolution and the prospect of getting good jobs made me enroll in computer science and engineering, first as an undergraduate and subsequently as a doctorate in computer algorithms. However, I turned to bioinformatics when I came to the USA for a postdoc at the University of Connecticut and subsequently at NCBI.
What really gets you jazzed about science and research?	I am excited to work in science research as this field is full of challenging problems. As the saying goes, “Choose a job that you love, and you will never have to work a day in your life.”	Reproducibility of experimental results guided by solid scientific theory.
If you weren’t doing this work, what other profession might you have pursued?	While I was a faculty member at the University of Connecticut, I really enjoyed teaching and interacting with students. If I was not here in NIH, I think I would still be teaching kids and have fun doing it there.	Maybe a mechanical engineer devising new tools for helping my father with his farming. Or may be an entrepreneur in spirituality.
Tell us something surprising about yourself.	I’ve moved a lot. In total, I have lived in 20 different places, including 11 in Korea and nine in the US. My family recently moved into our new home, and my hope is that I don’t have to move again at least for a decade.	I meditate. Cause and effect relationships and reproducible results have made me believe that there is a profound science in meditation, which needs a lot more exploration and experimentation!

Quick Q&A with Wang and Wojtowicz
Question	Yijie Wang, PhD	Damian Wojtowicz, PhD

In lay terms, what is the focus of your NLM research?	Transforming biological problems into mathematical problems and then solving the problems by efficient algorithms.	My research focuses on DNA mechanics by studying DNA structure and topology and their relationship with topoisomerase activity and DNA transactions. I am working on identifying and categorizing non-Watson-Crick DNA structures and their role in chromatin reorganization, mutagenesis, and regulation of gene transcription.
Why is it significant, in your opinion?	Biological data are huge and complex. We can only find biological insight through math and computers.	Alternative DNA structures are believed to play an important role in biological processes that cannot be achieved by canonical DNA duplex. Recent discoveries revealed that these structures can also stimulate genomic instability linked to many human diseases. Understanding the interlinks between DNA structure and topology with transcription can lead to new approaches in controlling the physiological and pathological expression of regulatory genes.
What or who inspired you to pursue your career?	My grandpa, who was the vice dean of Dalian University of Technology, a prestigious university where I got my bachelor’s and master’s degrees. He is legendary, according to my father. He was a spy who worked for the Red Army in the Chinese Civil War. He got his master’s in Russia, but was unable to pursue a PhD for political reasons.	My math and science (as well as English and French) teacher in primary school, Jacek Haduch, encouraged me to pursue my interest in science. Even during regular classes he inspired me to learn beyond the required curriculum. And then my computational biology teacher (and later PhD advisor) in university, Jerzy Tiuryn, directed me into my current career.
How did you get started in your career?	I just started at NLM after graduating.	I studied computer science and mathematics, but my research interests originated 15 years ago with a facultative course of computational biology in university that was really fascinating. This turned into master and doctoral degrees, and then postdoctoral training in computational biology and bioinformatics.
What really gets you jazzed about science and research?	Solving hard problems and finding new insights.	Discovery. Exploration. Being first in understanding something new.
If you weren’t doing this work, what other profession might you have pursued?	Probably a software engineer.	I guess I would be a computer systems analyst or a software developer.
Tell us something surprising about yourself.	I’m a good singer and a good tennis player.	The highest altitudes I hiked are about 17,200 feet (5,250 m) and 18,550 feet (5,650 m) during a trek to the north base camp of Mount Everest and around Mount Kailash in Tibet. I am also a mountain guide.