Scientific data needs to be explored, studied and understood via the convergence of knowledge and data-driven insights in order to solve complex biological problems.

Vivek Das, our next pathbreaker, Post-Doctoral Scientist in a Pharmaceutical Company, studies Chronic Kidney Disease, in the quest of finding new therapeutics using Bioinformatics approaches.

Vivek talks to Shyam Krishnamurthy from The Interview Portal about being drawn to the field of Bioinformatics for its increasing role in Drug Development through cross-disciplinary approaches involving Biology, Computer Science, Maths and Statistics.

For students, while mathematics and biology might appear to be unrelated, the world of medicine and drug discovery entails crunching a lot of data and numbers to understand disease mechanisms.If you are passionate about such kind of research, take up a career in Bioinformatics !

Vivek, tell us about Your background? 

I was born and brought up in Eastern India, City of Joy, Kolkata, in a lower middle-class Bengali family. I was fortunate enough to pursue my schooling in a renowned private English-medium school. I pursued Science with Biology as a specialization during my higher secondary education. I thank my parents whole-heartedly for providing me that opportunity despite all the challenges. My late father was into business. My super multi-tasking mother is a homemaker who often donned the hat of a local Hindi language tutor to the kids in the neighborhood. As a kid, I was pretty active, took part in annual quiz, sports and painting competitions. I pursued painting as a part of my Bengali heritage for 9 years till the age of 15. I was a curious kid growing up, but my interest in Science majorly took off when I learnt about the PSLV and various exciting things about rocket science from my maternal uncle who happened to be a scientist at the Indian Space Research Organization (ISRO) back in the 90’s. I formally expressed my interest in being a scientist when I was around 8 years old, but back then I was more fascinated by space, rockets, planets and superhuman comic series. I also thoroughly enjoyed learning Chemistry, Mathematics along with Computer Science. However, I never really had a plan to be a scientist until I pursued my master’s degree in Bioinformatics. Coming from a lower middle class financial background, my primary goal was to pursue a career leading to a job that would pay enough to repay my student education loan at the earliest. However, after my masters and two years of IT job in India (2010-2012), I was craving for more. I was hungry to challenge myself and take risks. I felt my passion could be satisfied if I chased my dream in Bioinformatics research, but little did I know that pursuing research would send me on a never-ending curiosity driven voyage of fun-filled exploration.  

What did you do for graduation/post-graduation? 

I obtained a bachelor’s degree in Biotechnology from Bangalore University (2008) and a master’s degree in Bioinformatics from Birla Institute of Technology in Mesra (2010). It was during my master’s degree when I realized how much computing and mathematics can help us solve biological queries. This started my journey into the fascinating field of Bioinformatics and that eventually motivated me to also pursue a PhD degree in the same field from the European School of Molecular Medicine (SEMM), Italy after a 2-year break as a Programmer Analyst in a leading IT company in India. 

What made you choose such an offbeat, unconventional and unusual career? 

I have always been fascinated by numbers and problems in Biology, Medicine, Healthcare, and in Nature. I enjoy breaking down problems into logical and systemic frameworks in order to formulate them into simpler distinct pieces of the puzzle that can potentially trace a path towards a solution. I feel most of the things that I do research on can be broken down into numbers, and I can derive patterns from these to eventually come up with a solution. Often such inspiration comes from reading scientific journals, listening to lectures of other scientists, drawing inspiration from their works alongside video games, novels/books that help me develop strategies. I am also fascinated about DNA and how four letters (A, T, G, C) hold the key to our human health and disease. One of the key projects that also made me choose the field of Bioinformatics is the Human Genome Project, completed in 2003. I enjoy gathering knowledge of things I do not know about, and once I learn something new; I try to figure out how I can draw inspiration from those within the scope of my current problems.

During the end of my undergrad, I realized that I was still fascinated by mathematics, statistics and computer science. I wanted to understand how I can use them in the field of Biology. It was then, I found out a few universities in India offering graduate programs in Bioinformatics. Given my interest, I wanted to pursue this route that would not only open up a new field for me but also enable me to compete for programming related jobs. 

My primary interest to do a PhD developed since I started my masters, and the more I got informed about the field, the more I got drawn towards a PhD by the limitless possibilities of it. During my master thesis, I worked briefly in the field of comparative genomics and developed a tool to perform phylogenetic classification of proteins using clusters of orthologous groups (COG) at scale. In the real world, apart from functional annotation of genomic sequences, it enables understanding of similarities/dissimilarities between related organisms, structural and functional characterization of the same, etc. E.g., understanding the microbial diversity using completed genomes, their functional characteristics and contribution to metabolism [1]. 

How did you plan the steps to get into the career you wanted? Or how did you make a transition to a new career? Tell us about your career path 

After my master’s degree in 2010, I was unable to secure a fully funded PhD position in the USA as I did not have a high GRE score. Owing to our financial background, the best logical decision back then was to find a job, pay off the debts and then plan for the next step. My GRE preparation helped me crack the off-campus drive and landed me an IT job at Cognizant India. 

A year into my IT job, I realized I still wanted to pursue a career in research, and this is when I decided to try again. Thus, I set up an ambitious goal to find a fully funded PhD position in Europe. I learned from a few friends that one can pursue such a goal through institutional entrance exams and that most of the applications were free of charge. It was bizarre and seemed very challenging given my work schedule, but it was not impossible. It was also pretty challenging to convince my parents, but they never stopped me from chasing my dreams even though I occasionally got MBA requests and brochures, given that some of my relatives and friends pursued that route and landed up with great career options. So, I would work during the day and search for positions at night, prepare my research statements, email professors to figure out if there were positions available and seek their response. I knew it was not easy given I did not have a lot of research experience apart from a 6-month research internship at Indian Institute of Chemical Biology during my Master thesis project. I did get numerous rejections, but I never gave up and eventually secured a position at SEMM in the beautiful and picturesque city of Milan, Italy. This is where I spent my next 5 years to gain insights into cancer research and advance my knowledge of genomics, high throughput technologies like molecular sequencing coupled with computer science, mathematics and statistics, contributing to drug discovery. 

My PhD work involved identification of transcription factors that orchestrate cancer progression both at onset and relapse. I developed computational workflows to integrate patient’s genomics, transcriptomics and epigenomics data to identify regulatory units that act as master regulators and drive tumorigenesis in high grade serous ovarian cancer and glioblastoma (a type of brain cancer). My thesis titled Leveraging transcriptomic analysis to identify transcription factors orchestrating cancer progression [2] is available here for a read.

I cannot single out any one source of inspiration as I have many who made me the person I am today, like my parents, my family, friends, school and university teachers, my PhD supervisors, former colleagues, rock star scientific community folks, my current colleagues, manager and my Post-Doctoral supervisor. I also want to thank everyone who did not think I would make it this far. I am also very fascinated by philosophical teachings and wisdom shared by Swami Vivekananda. All these inspired, motivated, and guided me — and they still do. I am always learning from each and every one who keeps my scientific wheel spinning.

How did you get your first break? 

My first research break was during my master’s dissertation project and our Bioinformatics professor (at that time) helped me make a case to get that position at IICB in a great research lab. However, my first job was in the IT industry that was through an off-campus drive that I happened to stumble upon by chance in a newspaper column after a disastrous GRE. Although the GRE prep did not land me a great score, it did help me with the Programmer Analyst job at Cognizant, India. 

What were the challenges you faced? How did you address them? 

Challenges never deterred me from pursuing my goals and ambitions. I did face my fair share of rejections while chasing my PhD dreams and while working full time, but I did not give up. Rejections were primarily due to lack of experience, however, with every rejection, I got better with my applications, picked up soft skills and built a stronger case as a PhD candidate. Ultimately, after 2 years of IT job and quite a few rejections, I was able to crack the PhD entrance examination in SEMM, Italy in late 2012. My suggestions for anyone who wants to pursue research is to acquire some experience in some research labs, and also publish papers if possible. This not only provides experience but gives ample networking opportunities.

Where do you work now? Tell us about your research

I currently work as a Post-Doctoral Researcher in a Pharmaceutical Company in the USA, studying Chronic Kidney Disease, in the quest of finding new therapeutics using Bioinformatics approaches. I work with stakeholders from Europe and USA on several projects as a part of public-private consortia collaboration, both from academia and other pharmaceutical industries to develop a cure for Chronic Kidney Disease (CKD). 

I amass large amounts of observational patient data and animal experimental data, analyze them using advanced computational/statistical tools and interpret them for various projects that I am involved in, to find patterns that can help us understand underlying disease mechanisms and their features. Such understanding can eventually help us narrow down our analysis to potential drivers of a disease, leading to new therapies. I am learning a great deal about the kidney, it’s underlying structure, physiology and factors contributing to its malfunction/injury/disease in my current position. Given my previous background was in cancer, I am also gaining a lot of knowledge about the drug discovery process as well. Developing a drug has a lot of steps and involves many tracks. It has been an absolute thrill to learn so many processes in the drug discovery journey. It is so much beyond publishing papers in leading academic journals. I enjoy the overall end-to-end learning and find something new everyday that can elevate my learning wheel. 

How does your work benefit society? 

In today’s world, medicine and drug discovery entails crunching a lot of data (often termed as big data), to derive patterns from them, help build hypothesis, build better future experimental designs, solve complex biological problems, to understand disease mechanisms using more generalizable and interpretable methods, thus providing insights into early drug discovery via convergence of knowledge and data-driven insights. All these fascinated me because I am passionate about Biology, Computer Science, Maths and Statistics. We have a lot of scientific data that needs to be explored, studied and understood. These can help us learn about medicine and bring new insights in the field of medicine. I believe, if one is passionate about such kind of research, Bioinformatics/Computational Biology is a fascinating field of science to pursue that can benefit one and all. 

Tell us an example of a specific memorable work you did that is very close to you! 

A memorable experience was inspiring my longtime childhood friend (who happens to be an excellent Computer Scientist and a teacher) to pursue a PhD track in Computational Biology, co-authoring a paper with him and publishing it in a top-tier Bioinformatics journal with limited funding. This project started out as a curiosity driven venture between the two of us with a few sets of questions in mind that needed convergence of Biology, Computing, Mathematics, Machine Learning and Statistics. Our passion and dedication led us to complete this project, publish it [3] and this turned out to be a great learning experience at my end.

Your advice to students based on your experience? 

My advice to students is, be passionate, never give up, keep trying, keep networking, do not be afraid of rejections, make short term goals and keep the learning wheel turning. 

Future Plans? 

To be an informed researcher who keeps up with the latest developments of medicine and data science, inspire dreamers to achieve their goals and contribute to society.

Reference:

  1. Galperin MY, Kristensen DM, Makarova KS, Wolf YI, Koonin EV. Microbial genome analysis: the COG approach. Brief Bioinform. 2019;20(4):1063-1070. doi:10.1093/bib/bbx117
  2. Das V, LEVERAGING TRANSCRIPTOMIC ANALYSIS TO IDENTIFY TRANSCRIPTION FACTORS ORCHESTRATING CANCER PROGRESSION. http://dx.doi.org/10.13130%2Fdas-vivek_phd2018-03-26
  3. Seal DB, Das V, Goswami S, De RK. Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration. Genomics. 2020;112(4):2833-2841. doi:10.1016/j.ygeno.2020.03.021