Drug development, although extremely impactful, is slow and expensive. Mathematical models help pharmaceutical companies make smarter decisions earlier — reducing the time and cost it takes to bring a new therapy to patients.
Maithreye R (PhD), our next pathbreaker, QSP Consultant and Team Leader at Vantage Research, works with pharmaceutical and biotech companies in order to help them in developing new drugs or exploring existing drugs in new combinations or patient groups.
Maithreye talks to Shyam Krishnamurthy from The Interview Portal about a defining moment in her career while reading a research paper that described a mathematical model of a virus’s lifecycle, and understanding how powerful a well-built model could be.
For students, don’t give up on your ambitions because the path gets complicated. Career breaks, family responsibilities, gaps in your CV — none of these are the end of the story. Keep going !
Maithreye, Your background?
I grew up all over South India — my father was transferred every three years, so I was constantly exposed to new languages, cultures, and places. My parents were both postgraduates who loved science, history, and literature. Our home was full of books and conversations about the world.
My father had a gift for finding books on whatever we were curious about, so I read biographies of famous scientists, books on astronomy, medicine, geology, and history. Science fascinated me — especially the idea that the entire universe runs on principles we can discover and understand.
When we moved to Mumbai, our house was on a campus surrounded by trees and wildlife. I spent hours exploring, and developed a deep curiosity about plants, animals, and birds that has never really left me.
What did you study?
I did my BSc and MSc in Microbiology in Mumbai. My original dream was to become a doctor, and I worked hard at the entrance exams — but it didn’t happen. Microbiology felt like a natural alternative. I’d read the biographies of Pasteur and Elie Metchnikoff and was inspired by the idea of discovering cures to diseases. Those years in college were genuinely exciting — I fell in love with biology and experimental work and realised I wanted a career in research and teaching.
I also did a PhD in Computational Biology from the Center for Cellular and Molecular Biology (CCMB).
What led you to this career?
After my MSc, I quickly realised that research jobs were mostly open only to PhD holders. So I cleared the NET exam and was selected for a PhD at CCMB (Centre for Cellular and Molecular Biology) in Hyderabad.
That’s where everything changed. My mentor, Dr. Somdatta Sinha, was working on mathematical biology — using equations to describe biological phenomena like insulin secretion in diabetes, gene regulation in bacteria, and even ecological processes. The idea that mathematics could describe living systems was completely new to me and utterly fascinating.
My PhD project combined my microbiology background with mathematical modelling of gene circuits in bacteria. I was intimidated at first — I hadn’t touched maths since Class 12. But I worked through textbooks on differential equations, learned simulation software, and slowly the pieces came together.
One moment I remember clearly: reading a research paper that described a mathematical model of a virus’s lifecycle. For the first time, I understood how powerful a well-built model could be. That moment motivated me to keep going.
How did your career path unfold? Tell us about your career path
My path was anything but straight — and I think that’s worth being honest about.
The problem I worked on in my PhD was pretty exciting and cutting edge for the time – this was in a area called Gene circuits which opened up in 2000 when a lot of information became available on genes and their regulators. The main idea in building gene circuits is that genes and their regulators could be cut and pasted together in different sequences to ensure that protein (the products of genes) production follows a particular pattern e.g., you could make a gene circuit where proteins could be turned off and on like a switch. If protein production can be controlled using such artificially constructed circuits, you could potentially control when and where a protein (maybe a therapeutic one) is produced. So, for making these circuits function the way we want, people use mathematical models describing the strength of regulation and predicting how much protein will be produced.
In my PhD, I worked on understanding the effect of time delays in regulation on protein expression – for this I had equations to explain how protein expression should be affected by delays in regulation and I also made gene circuits where I tested whether whatever I was seeing in my models were observed in real life (in my case, I tested In bacteria which are much easier to grow and manipulate than higher organisms).
As you can imagine, the work was difficult and I had to figure out how to do many things to test out my models. I had the help of several people in my lab and among my friends who helped me to make models, refine my ideas and understand my results. It was hard work, but I also enjoyed pushing myself to the limits and I learnt a lot.
After my PhD, I worked briefly (for around 6 months) at the Advanced Technology Centre of TCS at Hyderabad, where there were groups working on different aspects of bioinformatics and the newly developing field of systems biology. I started a new project there – to model the regulation of an important regulatory region in Salmonella typhi. However, my time at TCS was limited since I got married and moved to Chicago where we stayed for a year.
I took a break for a year following my marriage and the birth of my first child. I then did three years of postdoctoral research at IIT Madras in Computational Neuroscience. This was in a related field in that it is also mathematical modelling but of neurons and sections of the brain. The goal was to create a model of the regions of the brain involved in Parkinson’s disease to design better therapies for this disease.
I left this postdoctoral work when my second child was born and took another two-year break before actively looking for opportunities again.
Re-entering the workforce after career gaps is genuinely hard. My field was specialised, my network was thin, and many jobs aren’t advertised publicly — they’re filled through connections. I tackled this by reaching out to friends, family, and fellow researchers, sharing my profile widely, and asking directly for advice and leads. I also applied for research grants and explored collaborations with scientists I met along the way. I kept at it, even applying for roles outside Chennai, since my computational work could be done remotely.
Persistence was the skill that mattered most during this period.
How did you get your first break?
A friend referred me to Vantage Research, a consulting startup working in a field called Quantitative Systems Pharmacology — QSP for short.
QSP is the use of mathematical models to solve real problems in drug development: predicting how a drug will behave in humans, designing clinical trials, figuring out the right dose, or understanding why something that works in mice might not work in people. My biology background combined with my modelling experience made me a strong fit, and I’ve been here for over ten years now.
What were the biggest challenges?
During my PhD: Committing to 5+ years on a low stipend in a niche field wasn’t easy. Some family members wanted me to settle down rather than pursue research. Professionally, very few people worked in mathematical biology, so I often had no one nearby to bounce ideas off.
Learning maths from scratch: Coming from a biology background, mathematical modelling felt like a foreign language. I worked through old calculus textbooks — this was before YouTube — and leaned on senior lab members for help. Admitting what I didn’t know, and being willing to learn from scratch, was the key.
Building a network: When I started looking for industry jobs after my PhD, I realised how much careers depend on who you know. Most opportunities are never posted online. I had to be proactive — reaching out to people, sharing my profile, asking for introductions.
Balancing career and family: This has been the ongoing challenge. I’ve been lucky to have a very supportive family, including parents who live nearby and help with my children. As I’ve grown into a leadership role, the challenge has shifted — now I also have to be a good mentor to the people on my team.
What do you do now? What problems do you solve?
I’m a QSP consultant and team leader at Vantage Research. Our clients are pharmaceutical and biotech companies developing new drugs or exploring existing drugs in new combinations or patient groups. They come to us with questions like: What dose will work? Which patients will benefit? Will a result from animal studies translate to humans?
We build mathematical models to answer these questions, running simulations that would be impossible or unethical to test directly in people.
What’s a typical day like?
A typical day involves team meetings to discuss approaches and results, working with junior scientists on their development, presenting findings to existing clients, or meeting potential new clients.
What I love most is that the problems are always new and always matter — we’re contributing, in a small but real way, to therapies that reach actual patients.
How does your work benefit society?
Drug development is slow and expensive. Mathematical models help pharmaceutical companies make smarter decisions earlier — reducing the time and cost it takes to bring a new therapy to patients. In that sense, the work has a direct impact on how quickly new treatments become available.
A memorable project?
One project stands out. We were working on a completely novel therapy for a rare disease — which meant very little existing data to guide decisions on dosing. My team built a mathematical model that pulled together all the available evidence and used well-reasoned assumptions to predict a dose likely to be effective in humans. Seeing a mathematical model fill in the gap where clinical data simply didn’t exist yet — and knowing that prediction could guide a real trial — was deeply satisfying.
Advice to students?
Work hard at whatever genuinely interests you. There’s a particular happiness that comes from doing something you care about, and doing it well.
To girls and young women especially: don’t give up on your ambitions because the path gets complicated. Career breaks, family responsibilities, gaps in your CV — none of these are the end of the story. Keep going.
Future plans?
Professionally, I want to keep contributing to drug development through QSP modelling, and to train the next generation of people in this field. Personally, I want to support my family’s own growth and goals — that matters just as much to me.