Please tell us about yourself

I am a computer scientist, and a population geneticist – a computational geneticist in short. Simply put, I build computer programs to study the genetics of populations.

I am currently at the Center for Computational Genetics and Genomics at Temple University in Philadelphia, where I get to work on some very cool projects, with some even cooler researchers to answer fundamental biological questions with the help of computers.

My ultimate goal is to understand how species evolve from populations, and how their genes play a huge role in the incredible amounts of diversity we see in the natural world.

Original Link :

http://news.csusm.edu/grant-will-help-professor-gain-new-insights-into-population-genetics/

Can you tell us about your work?

Dr. Arun Sethuraman is using the power of bioinformatics to unlock the secrets of life.

“My interest is in looking at how we can use the power that we now have with computers and computer programming to make sense of this huge of amount of biological data that is now at our fingertips,” said Sethuraman, a computational biologist and an assistant professor in the Department of Biological Sciences who began teaching at Cal State San Marcos this past fall.

That means developing new computer programs and methods to study how DNA can be used in understanding the evolution of species, and how that knowledge can be used to protect endangered plants and animals. He is particularly interested in species conservation, and he is in the process of establishing both a molecular and a computational laboratory at CSUSM.

Sethuraman already is making his mark at CSUSM, having been awarded a nearly $800,000 grant from the National Science Foundation to develop computer software enabling scientists to study the diversity in the genomic DNA of organisms in an effort to forge new insights into population genetics – or how a species within a defined area evolves and adapts.

It is an area ripe for exploration. Technological advances, Sethuraman notes, have significantly lowered the cost of genome sequencing so that amounts of data unfathomable just a generation ago are now readily available. In addition, statisticians are developing new methods for analyzing such data.

“Combining the data with the new analysis approaches can let scientists answer fundamental questions about the biology of species that have never before been accessible,” he said.

“I really want to understand the fundamental genetic processes that scientists and conservation geneticists use to make predictions about life in general.”

How did you end up in such an offbeat, unconventional and uncommon career?

Like a lot of you, I grew up watching detective shows on television – ranging from Scooby Doo cartoons, to the drama on CSI, to the sometimes scary reality of The New Detectives, and The First 48.

On those shows, forensic labs could use a drop of blood or a single hair left behind at a crime scene to positively identify a criminal. I didn’t have the internet to tell me “how” they did it – it all seemed magical.

This was back in the 1990s – a time, when genetic technology was making monster steps towards better methods to nail criminals in courts. I was instantly hooked, and I wanted to learn that magic. I knew that I had to become a scientist that studies DNA (and perhaps catches the bad guys some day!). As time flew by, I decided to pursue a degree in computer science instead, as it seemed like a better option for finding jobs.

After years of studying and working in computer science, I was introduced to the miraculous world of bioinformatics during my junior year of college. Bioinformatics is the science of using computers to answer biological questions.  This field blended my skills with computers with my love of biology and I knew that I had struck gold.

He is well suited for the task. Sethuraman grew up in Bay of Bengal city of Chennai in southern India and earned his bachelor’s degree in computer science from the Birla Institute of Technology and Science in Pilani, India. He quickly landed a job as a junior research associate with Infosys, a global leader in technology services and consulting, but soon realized that kind of work was not his passion.

Intrigued at how the principles of mathematics were applied to other fields, including biology, Sethuraman returned to school, earning his Ph.D. in Bioinformatics and Computational Biology from Iowa State University. With an expertise in computer programming, population genetics, genomics, and bioinformatics of genome sequences, Sethuraman worked as a Research Assistant Professor at the Center for Computational Genetics and Genomics at Temple University for three years before being hired at CSUSM’s Department of Biological Sciences in the fall of 2016.

How did you end up in Population Genetics?

Armed with many more follow up classes in biology, genetics, and bioinformatics, I applied to graduate school to pursue a PhD. I found myself lost again in the infinite variety of questions that bioinformatics can help address.

After many months of pondering my choices, I decided to revisit the questions of my childhood –

  • How do we tell two people apart using DNA (e.g. me from my brother, or my uncle, a culprit from an innocent) despite us all sharing common ancestry (we are all relatives)?
  • Why do species exhibit so much diversity even though they are also related?

I developed new computer programs to do exactly this – studying differences and similarities in DNA, among individuals (humans), populations (of endangered turtles, and highly invasive lady beetles), or species (working with different species of turtles), and earned my PhD in the Summer of 2013.

Since then, my research has spanned into more specific questions about what happens when a population splits up, and two groups of individuals go their own ways – for example, a large river suddenly splits up two groups of chimpanzees (chimps aren’t the best swimmers!) What happens next?

Tell us about some of your Bioinformatics experiments

Assume that you are a biologist observing a small population of red and white mice. There are 8 white mice and 2 red mice (1 data). You are interested in knowing approximately how many generations it would take for the mice population to be entirely red or entirely white.

A traditional biologist will have to wait for every new generation and count the mice.. Even for mice, this could take a very, very long time.

Well, we already know a fair bit about how mouse color is passed from parents to babies. Therefore, we also know that the number of red mice in the next generation will depend on the number of red mice in the previous generation. In other words, if there are more red mom and dad mice now, it is more likely that their babies will also be red.  But how can we demonstrate this?

That is when the bioinformatician will “simulate” this situation using a computer. I did exactly this using a simple computer program that I wrote which tracks the percentage of red mice over time (part 2, model).

I ran this same experiment on a computer five times, and within seconds, an interesting pattern began to emerge, which can be seen in the graph.

What do you see? In four out of five cases, the number of red mice went to 0 within a span of a few generations (here, an average of about 10 generations). Only in one case (shown by the red line), did the number of red mice go up to 10 (shown here by a ratio R/N = 1.0).

There are several conclusions we can make (part 4, inference). First, it is likely that in less than 20 generations, the mice will likely be either all white or all red. Secondly, I would interpret this to mean that there is an 80% chance that the population will become all white.

Indeed, this phenomenon is very common in nature. We call it “genetic drift”, or the random change in frequency of an observation (here color of mice) in a population. As we have shown in this simple example, it only depends on the frequency in the previous generation. Touché, mouse biologist!

What do Bioinformaticians really do?

We conduct experiments in the field – we sample species in their natural habitat, and collect DNA samples (obtained from blood, skin, hair, feces), bring them back to the laboratory, extract genetic material from these samples, then “sequence” DNA using modern techniques. Sequencing refers to the process of identifying the smallest building blocks of the DNA, and their exact arrangement. This gives us a giant “instruction booklet.”

This booklet (“genome”) contains crucial information about how the species lives, grows, reproduces, and dies. For example, if we sequence DNA from the mice, we would expect to find information about the “genes” that are responsible for their coat color – why are some mice red, while others white? We then write computer programs to study similarities and differences in these DNA sequences.

So, where do we go from here? There are yet many unanswered questions – for example, can the red and white mice mate with each other to produce babies with lighter coats, and thus escape being spotted by crows? Or can a new “white” genetic mutation in the red mice increase their chances of survival?

We are still working on developing new methods to understand these. Importantly, bioinformaticians like me work hand-in-glove with biologists and computer scientists to come up with these solutions.

Can you think of an unanswered question in your mind that can be solved using a computer program? Leave your comments, and let’s come up with solutions!

What do you like about CSUSM (California State University, San Marcos)?

Sethuraman said he was drawn to CSUSM in large part because of the culture.

“I’m very impressed with how the faculty here, both in our department and at the university as a whole, teaches,” he said. “The emphasis is on having students learn from doing rather than having them learn from just reading a book. The focus is on incorporating inquiry-based learning in the classroom.”

Indeed, Sethuraman now has a half dozen students collaborating with him on his research projects, a number that will grow once the new molecular and computational laboratories are established.

“My goal is to provide my students with a fundamental knowledge of the theory and methods to analyze whatever biological and genomic data sets they may have,” he said.