About the Systems Medicine and Bioinformatics laboratory
Heart disease and diabetes don't happen in isolation — they emerge from complex networks of molecular interactions happening inside every cell. While traditional research examines single risk factors, we're taking a different approach: viewing human health as an interconnected system.
By integrating biobank-scale data with advanced analytics and multi-omics technology, we're revealing the hidden molecular architecture of disease. This holistic lens helps us move beyond spotting patterns to understanding what actually causes cardiovascular and metabolic conditions — and more importantly, how to stop them.
Our goal isn't just to publish findings. We're translating these discoveries into clinical tools that doctors can use today: from precision risk scores to AI-powered digital twins that can predict how individual patients will respond to treatment.
Our research approach
Big data meets personalised medicine
We combine national and international biobanks with diverse multi-omics biological datasets — genomics, metabolomics, lipidomics and proteomics — to examine disease at unprecedented resolution. This gives us a complete picture of what's happening at the molecular level.
AI-powered patient simulation
We're developing multi-modal AI systems that decode the fundamental rules of human metabolism from vast datasets. These models create patient-specific "digital twins" — virtual representations that simulate health trajectories and test different treatment scenarios before they're prescribed.
From prediction to proof
We don't stop at statistical models. We bridge the gap between computational prediction and biological reality by testing genetic variants in population-scale cellular models, ensuring our discoveries translate into treatments that work.
What is systems medicine?
Systems medicine is healthcare's future — a holistic approach that treats your body as an integrated network rather than isolated parts.
Instead of studying a single gene or protein, we map the complex connections between your biology and environment. By combining computational modelling with comprehensive datasets, we can predict individual disease risk with precision and design treatments tailored specifically to you.
It's medicine that sees the whole picture — because your health depends on how everything works together.