Career-Calling

Computational Biologist, a career at the intersection of Biology & Computing

• Pratibha Pandit • Season 2 • Episode 1

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Learn about the world of Computational Biology/Bio-Informatics from Principal Scientist Tolga Turan. This rapidly growing field is an opportunity for those who love Biology and computers and want to career that can an opportunity to enjoy the best of both worlds. 
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#Bilogycareers
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#datascience

Pratibha Pandit:

DNA. Our bodies instruction manual. That helps us grow from a single cell to the person we are today. A twisted ladder, like molecule that holds the secret code. A code that is unique to each of us. Made up of distinct sequencing of four types of building blocks. This cold holds the secret to our entire genetic makeup. Information about our health. Our cells reaction to diseases. To medicines and to aging. Unlocking this wealth of information, empower scientists and researchers. To make sense of the biological world. Drive discoveries and ultimately improve our understanding of life itself. But that's not an easy feat. Considering how much data there is in our body. According to national human genome Institute. If all the DNA from a single human cell was stretched out end to end, it would make a six foot long strand comprised of 6 billion letter code. Uh, person's DNA contains about seven 50 megabytes of information. And according to a study in 2020. A total of 1.7 megabytes of data were created per second per person in the world. The scale of data needs powerful computers. Algorithms. And experts with understanding of both biology and computing. In today's episode, we will learn about the world of bioinformatics. Uh, subdiscipline of biology and computing. That serves to acquire store and analyze biological data.

This is Career-Calling And I'm your host, Pratibha Pandit.

Pratibha Pandit:

My guest today is Toga Tron. Uh, computational biology, scientist. I specialized in cancer, immunological and metabolic diseases research. Till we'll go holds a PhD in molecular biology and biochemistry. And today he shares with us how he combined his love for computer science and biology in his career. Hi Tolga, welcome to Career Calling.

Tolga Turan:

Hi, Pratibha. Thank you for having me.

Pratibha Pandit:

I'm really excited about this topic because I've heard from a handful of parents, their children when they're in high school, they start off saying that I don't want to do anything with computer science or I don't want to learn coding and they go into the biology line and a couple of years or maybe even sooner into their undergrad, they realize they need to learn some sort of programming. So let's start by giving some insight into how computation and biology intersect. What are the applications where computation comes into picture in Biotech?

Tolga Turan:

Thank you, Pratibha. Yes, in our age, like every field biological sciences depend heavily on computation. Your audience and you might have heard the terms personalized medicine, precision medicine. These are terms indicating that we are now in a new era of medical era where the patients are treated not by their symptoms. Or the patients are not grouped by their symptoms and give one treatment fits all approach. But the patients are treated by their genomes, by their individual characteristics. And for, on the case of oncology, which my field is, each tumor is different, although you might say, this is a lung cancer patient, let's treat it as we treat lung cancer patients. But every lung cancer patient is indeed different, and it can only be disentangled by the use of personalized medicine and precision medicine, and I think it's fair to say that this is possible by computational biology, because when you sequence an individual's genome it's, a huge amount of data, and this cannot be analyzed by visually, manually, or even spreadsheets. You have to use computational biology, and that's, why computational biology comes in. This is not the only segment that computational biology is functional in the in the biological sciences, but more recently in the last 15 years with the advance of next generation sequencing technologies and computational biology is is very focused on Omics by which I mean genomics, transcriptomics, epigenomics, proteomics, et cetera. So that, that's my field, but there are other areas that computational biology plays in.

Pratibha Pandit:

As a computational biologist can you give a little bit of insight into what does the work involve? Starting with which type of organizations they work in and what does that role entail?

Tolga Turan:

Yeah, great question. The organizations I can divide maybe into roughly three buckets. One of them is academia where in academia, there are resource centers that work on cutting edge research problems where computational biology is, a very fundamental unit of the research groups. And the second is in industry and industry can be divided into a few sub buckets, and one of them is biotechnology, and in biotechnology field computational biology is used to generate tools and and platforms that either academic resource scientists or pharmaceutical resource scientists use. And these are essentially platforms and tools and databases that are useful to answer biological questions. And in the third bucket, which I am in, is the pharmaceutical area. Where we use computational biology databases, tools, systems to understand a disease and improve human health. So these are three main buckets. And in the first part of the question, what does a computational biology role involve? That can be summarized by first and foremost domain knowledge is very important in computational biology, you have to know your domain. Oncology field and this can be immunology, neuroscience, microbiome, bioinformatics, etc. But you have to know your biology well to better understand or to better reach your goals. In the day to day work we work on computer obviously. It can be surprising to hear, but, I can say quite a bit of our time is Cleaning data and making data suitable or applicable to our purpose. It might be surprising that tweaking and cleaning data can take so much time, but it is. Once you clean and perfect your data, then you can test your hypothesis.

Pratibha Pandit:

So would you consider yourself a biologist first and then a computer science engineer or the vice versa

Tolga Turan:

Yes, great question. So there are a competition biologists who came from a software engineering background, more computer science background, and there are bioinformaticians that are coming from more biological background, molecular biology, genetics background, maybe there are a third group where they have formal education of both. So I am coming from a more molecular biology, genetics, more core biology background. And I consider myself the first that you mentioned. Each of these groups and each of these career path journeys have different things to bring in the table, and each can be really functional and successful by informatics journeys. Some of the job positions and some of the job applications can have different flavors. An employer might, have in their mind, I'm looking at a really strong biological background, but also someone who also can do data analysis. So there are different variations and nuances between these two- three groups. I think coming from a software engineering background you can have different things to bring to the table and the other way as well. Yeah, all three groups can be really successful by bio-computationalists.

Pratibha Pandit:

That's interesting that someone from general purpose software engineering background can come in. How do they layer in the biology knowledge in that case?

Tolga Turan:

Yeah, great question. So coming from a software background one example could be, entering into the workforce in a biotechnology or pharmaceutical industry and using your strengths in the software engineering background to contribute. Sometimes you learn while you're doing it and you get accustomed to the biotechnology field and pharmaceutical field and you really like the area. You really like the question you answer contributing to human health. So these are very important things and can really drive a person and to learn biology.

Pratibha Pandit:

Makes sense. Now you touched upon it a little bit about the domain expertise and all of that. What are the other skills necessary to be successful in this field? If you could also touch upon the types of data analysis, like what kind of computation skills are used, overall, what are some of the skills that are required?

Tolga Turan:

So one one important skill set that I could consider must is a foundational math and stats skills. I think you don't have to have a graduate level calculus knowledge, but I think knowing your math, like very strong foundational math and, statistic knowledge is important. I think also conceptual obviously we are working on computers and we are not necessarily interacting with something tangible the case of bench science where, I came from, you work with something tangible. You work with enzymes, you work with DNA, these are tangible things that you interact with, and in computational biology, most of the things we work are conceptual, and the algorithms we develop, the new bioinformatics we'll have to work with those conceptual ideas and sometimes visualize things. Once you start from data and your endpoint and coming from point A to point B sometimes requires both conceptual thinking and abstract thinking you could say.

Pratibha Pandit:

Can you double click on the data analysis part of it? What are the softwares, maybe the programming language, what data analysis skills are required?

Tolga Turan:

Yeah. So I mostly use high level computational languages, but usually a computational language preference is not is not strict. You can use anything, any type of language. You can use low level languages, high level languages. But in terms of data analysis, I can give some examples. For example clinical data and a patient receiving a pharmaceutical drug has, let's say if the drug is working, if the drug has has causing any response any, kind of treatment success with the patient. These kind of data points are very important in a setting where researchers use those data points to drive their next steps, to drive their drug discovery process. And so when we receive those kind of data, we look at the data, clean the data, QCD data, and and then try to list our hypotheses. I think that's very important to do a hypothesis driven exploration because if you don't know what you are asking or if you don't know what you're looking at, you can just go into the data and look at, oh, this is interesting. This question in mind you can test the hypothesis. It might be positive. It might be negative. And then if it's negative, you can find another one. If it's possible, this is very interesting. You can build upon that. That's this is a summary data analysis we do.

Pratibha Pandit:

Are the tools and coding used in the biotech the same as the rest of the field Python or

Tolga Turan:

Yeah. Yeah.

Pratibha Pandit:

same?

Tolga Turan:

We, yeah, we use Python, we use R we use C++ these are all languages that are applicable in software engineering in I don't know different high tech companies that use, and we also use the same tools. Yes.

Pratibha Pandit:

I noticed that in your education journey, you have a focus on molecular biology all along, all the way through your PhD. At what point did you pick up the data analysis skills? More generically, I wanted to understand when you when somebody studying biotech, is computation or data analysis usually offered as a coursework, or is this something that people can pick up outside of it?

Tolga Turan:

Yes. When I was in college, there were courses that were offered and I took those courses and I enjoyed them. But maybe touching upon my journey a little bit. Maybe I can start from early on when I was in high school, I didn't know a field called computational biology existed at all. What I knew was, I love computers. I love computer games. I love everything about computer science. So my plan was going into computer science field in my undergrad. And at the time, genetics was opening in the Universities back in Turkey, and I am I really like the idea of exploration of the sense of wonder that comes with the term genetics, that comes with the idea of a cell, your DNA, your genome. These are, piqued my interest. I want to do something about this. So instead of computer science, I picked molecular biology and genetics department, and then along the way, I didn't have an idea that I will work on computational biology. So if your audience doesn't know or sometimes uncertainty and an anxiety can happen when a high school student wants to choose their topic. So at that point, sometimes you don't know what's going to happen, but I think going with the flow and believing things will turn out well, I think is good. Yeah, after my undergrad, I came to United States to pursue my PhD education and molecular biology and genetics is a bench science for the most part, especially in in the training grad school and post doctoral studies, and I wasn't a good bench scientist. I wanted to be in science. I really loved conceptual ideas. I really loved discovering things. I really loved the field in general. However, I wasn't a good bench scientist. After my second postdoc, I was in a microarray lab which, necessarily involves data analysis initially I was doing it by analyzing Excel sheets, et cetera, and we have a bioinformatician in the lab. By osmosis, by slowly getting interacting with them I learned data analysis can exist in biological sciences. So in a few steps, I made my journey to towards computational biology, data analysis and to your question about what are the resources. I think a lot of resources are available, even if there's no coursework involved in a in a biological scientist education. Right now in our age, you can find anything in web. There are online courses. Several of them. I took one specialization course related to bioinformatics. So if one is interested, I think in our age there's no excuse, I don't have the coursework. Everything is available.

Pratibha Pandit:

So it's not necessary that in the process of your formal education, whether it is your master's or PhD, you must have taken coursework. As long as you have acquired the skill, you still have a path to become

Tolga Turan:

computational biologist. That is correct and that I am living evidence to that. And also, there are nowadays computational courses and bioinformatics courses are more and more embedded in biological curriculums, more than my time.

Pratibha Pandit:

That's true. Now you touched upon it. I wanted to get a little bit of more understanding. Maybe people who are already in the biotech may get this better, but for the younger audience, I want to the different career paths. Like I've heard from my other friends in biotech as well, these terms bench work or informatics or research. Can you touch a little bit upon what are the different paths? Like, how do they differ for the layman?

Tolga Turan:

Yeah, so benchwork, as I mentioned, is dealing with experimental component of biological research, biological sciences, and that's a fundamental aspect of biological domain. However, it's not for everyone. And in my opinion, even if you choose the route to go to a real full fledged monitor biologist on the bench, et cetera, I think knowing data analysis could help every bench scientist, every biological scientist, because not everything can be analyzed through Excel, not everything can be analyzed visually, so even if you have a bioinformatician in your group, even if you have someone to ask your computational biology questions. I think knowing a certain level of data analysis would help a biological scientist and other areas are like research and like in general, you can divide biological sciences, maybe roughly as bench science and computation.

Pratibha Pandit:

You have specialized in molecular biology. What are the other possibilities? What are the other areas one can go into?

Tolga Turan:

Yes. Molecular biology and genetics could be one and other majors that one can pursue and end up in bioinformatics could include mathematical sciences, statistics, software engineering, computer science and even medical doctors end up doing data analysis. Even physical sciences, chemistry gives you a foundation later you can build upon to become a informatician.

Pratibha Pandit:

In other fields, say software industry or anywhere, a typical path from college education, you have acquired your formal degree, you have acquired all these skills. Typically the path to workforce is doing internships. In some fields, you also have to have some portfolio if you're in, let's say, some sort of a design or those type of work, including even data analysis. I've seen people build their portfolio. How does it work, if somebody wants to be a computational biologist, what are the things other than the formal education they should build up that helps them get into the workforce?

Tolga Turan:

A young student in high school or in college, I would certainly advise to do internships, to do co-op opportunities. First they let the students to interact with the pharmaceutical industry, biotechnology industry, how is it in reality to become a bioinformatician? And sometimes this is missing from the coursework and you don't actually get actual the field study where, what's happening in a biotechnology research group, pharmaceutical research group. So to experience that firsthand can be done you with your internship and second advantage is and if you prove yourself during your internship, then the company is very interested in you compared to someone that they don't have any interaction. I think internships can help you go into the workforce quite easily.

Pratibha Pandit:

You must have hired people especially juniors from college. What do you look for? There is a education component, but are there any personality traits for somebody to be successful in this field, what do you look for?

Tolga Turan:

So growing up I considered in the field of science communication is not that vital. You might think, okay, I know my science well, I know my math well, I know my algorithms coding well. I don't need communication, and that is not true. And I learned that quite intensely where even if you do, like a really good work, really million dollar work in your job, if you cannot communicate it to the other side it doesn't have a value. I think communication can be learned, even if you're an introvert, I think those skills also can be learned. Other than that being able to work with others. And one thing I might add is actually might be the most important is the learning attitude like lifetime learning attitude because, in the coursework, although it is very up to date, you go to a top notch university, do your PhD, you cannot learn everything through coursework. Being open to learning, being open to new things, because every other day there's a new computation analysis package comes along. There's new ways to analyze data and sometimes in your job your team says, okay, there's a new area of you need to work on, you're assigned to that. And at that point, if you say, I don't know this, and if you say, I don't know this, but I can learn, that brings you forward.

Pratibha Pandit:

Yes, absolutely. That makes sense. I think this is something that emerges in every field as I speak to a lot of professionals,

Tolga Turan:

yeah,

Pratibha Pandit:

and then being able to keep them updated.

Tolga Turan:

Yeah

Pratibha Pandit:

I had related to education was, I have often seen professionals in the biotech field having PhD for someone to be computational biologists is it a must, or can they start a career even after, let's say, masters or even undergrad?

Tolga Turan:

I think, to be a computational biologist PhD is not a must. There are very successful bio informaticians, computational biologists that had masters or bachelors degree. But, I have to say having a PhD gives you a better eye looking at things and also gives you a head start. Obviously, you spent six years and now you have a head start, but I think I strongly suggest someone who wants to be a biological scientist considering PhD and sometimes if they are not sure because that's a big investment based on time and other sacrifices, you can start a bioinformatics position after your master's or bachelor's, and I have seen several examples of that, they start after bachelors or masters, and they decide to pursue PhD afterwards, and that makes you evaluate do I need a PhD or, can I just start from here?

Pratibha Pandit:

It's great. Now let's go back a little bit further to younger students, like high school students or maybe even sometimes these days, middle school students who are pretty aware and Yeah. start looking at what they want to do. College admissions are becoming ever competitive increasingly at every stage. For that age group who are probably still green on a lot of things what exists. What are some of the things for them to familiarize or if they Yeah. have inclination towards biology or maybe a little bit of computing. What are some of the things that they can do at that age group?

Tolga Turan:

Yeah. So one thing is consider internships because there are companies that also takes in high school students as internships. If they're interested in an area, but they're not sure there are different ways of getting more information about that field. I think being proactive is good. Sometimes a shy high school student might not be inclined to get information. But I think being proactive, let's say, you have a neighbor. They are a computational biology scientist in a company. Just reaching out to them. Maybe you have a relative. Just learning as much as possible. Not only computational biology, but also other fields that they are considering. Getting as much information as possible, and not everything can be learned by reading. I think reading is very important, and getting books, getting learning things from web are good. But you can do so much by reading. You cannot you cannot learn everything by reading. So one additional advice that I would give is, there are podcasts like this one and there are also resources that they can listen while they're driving, while they're biking. I think being proactive, getting access to information what it's like to be a computational biologist, for example after I decided to be a bioinformatician before my first job I knew data analysis, I knew coding but I, never been a really full fledged bioinformatician. And I looked at podcasts there are several podcasts related to bioinformatics. I looked at books, I got a really nice book that gives a general overview. So getting as much information as possible would help them. I think starting from web and doing guided searches could help, and reaching out to people is helpful. Sometimes you never know, someone total stranger could help. Let's say there's a professor in academia, you reach out to them. You want to learn and a relation starts and you do an internship in his lab. So being proactive again, I want to emphasize.

Pratibha Pandit:

Thank you

Tolga Turan:

Tolga,

Pratibha Pandit:

This has really been very nice speaking with you, and,

Tolga Turan:

Thank you Pratibha well.

Pratibha Pandit:

very informative. I'm really glad to have you on my podcast.

Tolga Turan:

Me too. Me too. Thank you for having me.

Pratibha Pandit:

You can learn more about the field by subscribing to bioinformatics focused podcast, such as the bioinformatics chat. The bioinformatic, CRO podcast and many more. To learn data skills relevant for a biologist refer to bioinformatics data skills by vince buffalo And from cell line to command-line by meantime You can also hear more from toga on science, spirituality and music on his blog Where science flourishes

This is career calling. And I'm your host Pratibha Pandit. Thank you for tuning in.