To understand biology in motion, we must look to the proteome—the full set of proteins expressed in a biological system at a given moment. DNA may be a master blueprint for life, but it’s proteins that act as tireless builders, executing that blueprint through cellular machinery. Today, proteomics provides a powerful lens into real-time biological activity, revealing not just which proteins are produced under various conditions, but also how they behave, interact and transform.
At the A*STAR Institute of Molecular and Cell Biology (A*STAR IMCB), Senuri De Silva is on a mission to decode these protein behaviours across diverse disease contexts. A recipient of the Singapore International Graduate Award (SINGA), her research aims to identify early diagnostic biomarkers, monitor disease progression and uncover new therapeutic targets to ultimately guide the development of more effective treatments.
In this interview with A*STAR Research, we speak with De Silva on the rare intersection of computational biology and translational science which drew her to proteomics. She also reflects on a scientific career spanning one island nation to another, the impact of her work along the way, and her advice for early-career scientists.
Q. Share with us how you went from Sri Lanka to Singapore.
I grew up in Sri Lanka, a country known for its breathtaking landscapes and rich biodiversity. Frequent childhood trips to the countryside—from lush forests to vibrant coral reefs—sparked my early curiosity about nature and biology. At the same time, Sri Lanka was rapidly embracing technology; as computers became more common in schools and homes, I was excited to experiment with them. It felt like discovering a whole new world.
With a math-loving mother and an engineer father, pursuing computer science and engineering at the University of Moratuwa, Sri Lanka, felt like a natural path. A course in computational biology helped clarify my aspirations: to use computing to solve complex biological problems. Later, a final-year project combining computing and biology strengthened both my technical skills and my interest in translating science to medicine.
I wanted to go beyond computational predictions to understand how they manifest at the molecular level. As I explored relevant opportunities worldwide, Singapore stood out for its thriving scientific ecosystem, the presence of world-class research institutes such as A*STAR and the National University of Singapore, and a scholarship opportunity that would fully support my studies. The research project I joined also provides a meaningful chance to contribute to impactful biomedical research while dissecting the molecular mechanisms behind disease.
Q. Tell us about the ADHD project you previously worked on.
For my final-year undergraduate project, I teamed up with two close friends to develop a support system for the early detection of attention-deficit hyperactivity disorder (ADHD), a condition we felt was underdiagnosed in our community.
Guided by our supervisor Dulani Meedeniya at the University of Moratuwa, we used a multimodal approach that combined brain scan data from functional magnetic resonance imaging (fMRI)—which tracks brain activity during task performance—and subtle eye movement patterns detected by a machine learning (ML)-based model we built. This provided objective, data-driven support for clinical diagnosis of ADHD, which is often based on subjective assessments.
In our deep learning analysis of imaging data, we found people with ADHD had reduced functional connectivity in the default mode network: a brain region linked to rest and mind-wandering states, which was consistent with findings from other emerging studies. A key novelty of our work was its ability to generate an objective score using either or both data types, allowing minimally invasive and child-friendly ADHD screening.
We consolidated these features into a web-based tool called ADHD-Care, which we piloted in local clinical settings. A senior psychologist praised its usability and translational potential; it was later presented at international conferences and published in peer-reviewed journals, and went on to win the university’s Best Final Year Project in Computer Science and Engineering award in 2020.
Q. What are you working on at A*STAR IMCB?
At Jayantha Gunaratne’s A*STAR IMCB lab, my work primarily focuses on integrating in-house experimental data with large-scale public proteomics datasets to uncover meaningful patterns in protein expression. Specifically, I investigate the proteomic landscape of breast cancer, with a focus on aggressive and hard-to-treat subtypes.
Over the past three years, our research has led to several exciting findings. We identified a previously uncharacterised breast cancer subtype linked to poor prognosis, along with distinct molecular signatures that improve patient stratification. We’ve since found this subtype in other cancer types as well, which prompted us to develop novel computational methods capable of extracting robust features with high sensitivity and specificity.
This effort culminated in the creation of several open-source, user-friendly friendly statistical and ML tools— now widely adopted both in our lab and the wider community—to support biomarker discovery from proteomics data. Crucially, these tools have also enabled the discovery of highly specific, previously unrecognised biomarkers in diseases including high-grade serous ovarian cancer, chronic kidney disease, triple-negative breast cancer and eye disease. Our discoveries have led to two patent filings; I’ve been privileged to present our work at multiple international conferences.
Q. What big questions in science do you hope to answer?
A central question in my research, especially for complex diseases like cancer and neurological disorders, is how to identify novel, non-invasive biomarkers and therapeutic targets that have remained overlooked. I see immense potential for computational methods, given their rapid advancement, to improve disease diagnosis, inform treatment strategies and advance precision medicine in scalable and accessible ways.
I am especially intrigued by the ‘dark proteome’: regions of the protein universe often uncharacterised or mislabelled due to algorithmic limitations. Mapping this underexplored space with improved computational techniques could be a transformative step that helps connect previously unseen biological relationships and address longstanding questions across multiple disease contexts.
Q. What advice do you have for your peers and juniors in science?
I often reflect on a quote by physicist Richard Feynman in his Nobel Prize interview: “I have already got the prize. The prize is the pleasure of finding the thing out, the kick in the discovery, the observation that other people use it.” This sums up perfectly what drives me in science: the joy of discovery and the fulfilment of contributing something meaningful, even in small ways.
My advice is to actively seek mentorship and stay open to learning at every stage. We are all lifelong learners, and the right mentors can guide, inspire and shape your scientific journey. Seminars, scientific events and informal discussions are also opportunities to engage with brilliant minds, broaden your thinking and remember the human side of science.
Above all, find joy in what you do, and pursue it with curiosity and purpose. Science can be challenging, but it is also deeply rewarding when approached with passion and openness. Stay inspired, remain humble and never stop learning.
