Key Research Areas

Research Interests

• Applied Mathematics & Mathematical Modelling
• Bioinformatics & Computational Biology
• Data Science & Machine Learning
• Graph and Network Analysis
• Systems Biology, Flux Balance Analysis, Genome-scale metabolic models

Research Overview

Our research focuses on developing computational approaches that integrate bioinformatics, systems biology, mathematical modelling, and artificial intelligence to understand complex biological systems. By combining genomics, transcriptomics, single-cell omics, biological networks, and genome-scale metabolic models, we investigate molecular mechanisms underlying cellular functions, disease progression, and biological adaptation. A major research area is the development of computational frameworks for systems biology and constraint-based metabolic modelling. Our work includes genome-scale metabolic models (GEMs), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), and multi-omics integration, with recent developments such as ICON-GEMs and IGM for integrating transcriptomic data into metabolic network analysis. These approaches have been applied to microorganisms, plants, and other biological systems. We also develop computational methods for precision medicine and network medicine by integrating transcriptomics, single-cell sequencing, GWAS, protein interaction networks, and machine learning to identify disease mechanisms, biomarkers, therapeutic targets, and drug repurposing opportunities. Current applications include breast cancer, chronic lymphocytic leukaemia (CLL), Richter transformation, neutrophil, and plant systems biology, particularly rice stress biology and metabolic regulation. Through interdisciplinary computational research, we aim to translate large-scale biological data into mechanistic understanding and practical applications in medicine, biotechnology, and sustainable agriculture.