I was always fascinated by wildlife and biology had a special place in my heart during my school days. However, the quantitative perspective gained from physics out-competed the qualitative perspective of biology and led me to a masters in physics. 

I came to know of the quantitative side of biology from my biophysics course during my master’s studies. It was deciphering and finding solutions for complex systems that drove my passion for science. For some time, I was torn between cosmology and biology. My inadequate knowledge of biology held me back and led me to pursue cosmology instead. After a short stint of graduate-level research in cosmology and a lot of reading in biology I realized that my true calling was in biology, also a complex system with applications that can be perceived more easily compared to theoretical physics.

I joined MRN Murthy’s lab at the Molecular Biophysics Unit at the Indian Institute of Science as a research fellow and was introduced to the world of proteins, their structure, and how their function depends on their conformation. MRN Murthy encouraged me to harness my mathematical skills and develop computational methods for protein sequence and structure analysis. 

Soon I started working independently on evolutionary analysis of proteins and comparative genomics to understand why or how organismal complexity arises. I realized that although proteins are the workhorses of the cell, the complexity of an organism does not linearly increase with the number of protein-coding genes in the cell. In fact, the way protein-coding genes are regulated is significantly correlated with a higher fraction of the noncoding regions of a genome in the organism (proposed by Michael Lynch). 

I wrote to Wen-Hsiung Li in Chicago with the results from the above analysis and got a PhD offer. Li had two labs — one at Chicago and another at Academia Sinica, Taiwan. He convinced me to come to Taiwan, as he had better infrastructure and facilities there. Coincidently, my physicist wife had also found a postdoctoral position in Taiwan. I did my PhD in computational biology from the Institute of Information Science, Academia Sinica, Taiwan. 

I was lucky to work with Huai-Kuang Tsai, a young and dynamic PI and a former postdoc of Wen-Hsiung Li, who had set up his own computational biology lab. During my PhD I developed methods to analyse and predict the structural, functional and evolutionary aspects of noncoding regions of the genome important for mediating transcription in yeast, Drosophila, Arabidopsis and primates. 

During my PhD, I realized that experiments play a key role in understanding biology. I transitioned from a theorist and computational biologist into an experimental biologist as a Distinguished Postdoctoral Fellow in Jun-Yi Leu’s lab at the Institute of Molecular Biology, Academia Sinica, Taiwan. Here, I developed computational methods and designed experiments to decipher the molecular mechanisms of speciation, the evolution of complex traits such as fermentation in yeast, and the evolution of co-operation. 

I have been lucky for having the support of my postdoctoral advisor, Jun-Yi Leu, especially since I was doing experiments for the first time in my life. He gave me leeway and taught me well enough that I could set up an experimental molecular biology lab of my own. After seven years of postdoc, I joined the school of arts and sciences at Ahmedabad University as an Assistant Professor in March 2019.

Here is some advice for conventionally trained biologists aspiring to do interdisciplinary research:

Be open-minded: You might be required to change the way you think when using or developing a mathematical and computational model. Systems biology, for example, usually involves a fair amount of computation and experiments. Although both molecular biology and systems biology can answer the same biological questions, they do it in different ways. While systems biologists try to arrive at the underlying principles between genetic interactions responsible for a phenomenon, they might not arrive at the molecular mechanisms of a specific genetic interaction, which traditional molecular biologists strive for. Findings in systems biology (like computational biology) could be derived from statistical and mathematical models, and sometimes, direct experimental validation might not be feasible. A lot of relearning might be needed if one is new to mathematical and computational modelling and is handling whole genomic or transcriptomic datasets.

Index reading: Time spent on learning certain concepts and basics will go a long way. Although it might seem that there is an ocean of knowledge out there, index reading (reading the required sections by looking it up in the back-of-the-book indexes) serves for most practical purposes.

Do not hesitate to ask for help: While you can pursue a new field independently, it is good to partner with a person with considerable expertise in the new field for the first few projects, till you learn the ropes of the trade. You could also reach out to other people working in the field. Although they might not work on your problem, they can help in overcoming hurdles by discussing them with you. 

Attend meetings and conferences: This is probably the most important factor. Speakers in meetings and conferences usually provide distilled information from several years of research. It also helps to know the recent advances even before they are published. Conferences are also probably the best venue to network and find future collaborators.

While traditional molecular and cell biology are evergreen fields and required for determining molecular mechanisms, having expertise in multiple disciplines has its benefits. It can help in addressing problems at a systems level and in deriving general solutions applicable across species which might not be feasible by traditional biological techniques. 

However, it can come with a trade-off in the depth vs breadth of one’s knowledge. Such a trade-off is also true for the scientists’ grasp of knowledge in different fields. Hence, interdisciplinary research is best conducted in a collaborative set up. YIM is one such venue for young PI’s (like me) to network and build long-term collaborations.