First person – Asadullah and Sandeep Kumar Free

Asadullah

Sandeep Kumar

How would you explain the main findings of your paper in lay terms?

A.: Our research shows that cell migration can be very similar to a situation wherein families migrate (combined migration of a heterogeneous population) to a new city (that might have cultural and language differences) because there are better job prospects, basic amenities, etc. During the process of settlement at this new place, one needs to be flexible with salaries, housing and other requirements (akin to our finding, wherein cells of varying flexibility tend to be more invasive). Meanwhile, individuals who try to learn the language of the new region, understand the culture and be less egoistic (very akin to the cells, which are small and more deformable) find it easier to dwell at the new place and can guide others in the family, while still receiving moral support from other family members (combined heterogeneity helps).

S.K.: Tumor population is heterogeneous, like a village that has residents with different abilities, potentials and skills. When migration of these people takes place, they follow different trajectories, strategies and eventually end up reaching different destinations. This study somewhat mimics similar ‘heterogeneous migration’ and tries to predict where each cell with a particular set of abilities will migrate to.

Were there any specific challenges associated with this project? If so, how did you overcome them?

A.: Being a basic biologist by educational training, it was an altogether different challenge to learn to code biological parameters and perform quantitative analysis of the huge data generated from simulations.

The senior researcher from our lab, Dr Sandeep Kumar, with whom I am sharing authorship on this research paper, had been of immense help to me in understanding the intricacies of the software package (CompuCell3D) and the biology associated with it. Also, I have learnt other coding skills from online courses.

S.K.: Since the obtained simulation data was highly dimensional, it was not easy to visualize that data using conventional plots. To address that, we used tools from machine learning to reduce the data dimensionality. Visualizations obtained from projected data were very intuitive and correctly capture the essence of observations.

When doing the research, did you have a particular result or ‘eureka’ moment that has stuck with you?

A.: Yes, we did. We were trying to study cell invasion and the effect of heterogeneous population has in an in vivo situation that is under the effect of chemical gradients. We then started to see the gradual enrichment of a particular type of cell at the invasion front and, more importantly, we could co-relate it with cancer stem cell behavior.

S.K.: We were trying to hypothesize which tumor subpopulation might be represented by the sub-population enriched at the invasive front, and it was very exciting for all of us to see that this ‘computational population’ closely resembles CD44-high breast cancer stem cells.

Why did you choose Journal of Cell Science for your paper?

A.: We chose JCS for our paper because of several good reasons, such as it is peer-reviewed by eminent cell biologists all over the world, free to publish and also it becomes free to access after a short interval of time, thus giving easy access to the researchers from developing and under-developed nations.

S.K.: This study is a cellular-level study revealing several new insights into breast cancer heterogeneity. Owing to the nature of study and its high relevance in the breast cancer biology, we found JCS to be the one of the best journals to publish this work.

Have you had any significant mentors who have helped you beyond supervision in the lab? How was their guidance special?

S.K.: During the early stage of this project, when the Cellular Potts Model was a newer formalism to me, I was having difficulty in interpreting multiple aspects of this formalism and its implementation. Dr Maciej Swat from Indiana University (lab of Dr James Glazier) helped me understand these aspects. That really helped me develop a more effective model.

What motivated you to pursue a career in science, and what have been the most interesting moments on the path that led you to where you are now?

A.: The intricate beauty of nature and its hidden secrets had a hold on me. In early childhood, the idea that I should try to decode them was ignited by my family and friends, and I like that this work might make this place a better world to live in and appreciate.

During my initial days of undergrad study, I had an opportunity to intern in some reputable labs in India, which further increased my thirst for knowledge. Also, I realized that I need to have good technical skills along with the scientific knowledge to achieve my dream of understanding nature, which further drove me to pursue higher degrees in science as well as in engineering fields.

S.K.: Coming from a computer science background and being inspired by the computing abilities of biological systems, I decided to pursue a career in biological science to understand/discover the ‘algorithms’ driving spatiotemporal evolution of highly distributed biological systems.

Who are your role models in science? Why?

A.: My role model in science is my PhD guide, Prof. Shamik Sen. It is difficult for me to put into words the entire reason behind it. He taught me how to do science in its actual sense, how to design, logically interpret and be precise in scientific research. It is he who introduced into me to scientific rigor. I am enlightened by his approach of having maximum scientific outputs with limited funding, and in a limited time frame, too. Along with all this, I got to learn how to skillfully deal with students and other persona across academia.

What's next for you?

A.: My immediate goal is to complete my PhD and find a professorship position either in a university or college or, if the situation permits, I would wish to go for a post-doc to hone my research skills.

S.K.: I am currently exploring the options to make use of my diverse skill set in developing commercializable tools and techniques, which can help accelerate scientific data analytics and investigation lead generation. Since most of the modern experiments generate a large amount of data, we need more reusable automated platforms for analysis of this data in a timely fashion. Machine learning-based tools can be used for rapid analysis of large and complex data and such automated analysis will yield fully reproducible results that will also be easy to share with colleagues, publishers or reviewers.

Tell us something interesting about yourself that wouldn't be on your CV

A.: I am an avid traveler, a nature enthusiast and a footballer. Also, in my leisure time, I am interested in watching documentaries related to forestry, wildlife, and their protection and preservation.

S.K.: I have an immense interest in astrobiology. Understanding the origin of life and the effects of gravity on development are two questions I have never worked on, but am very interested in.