The Quantitative Biology (q-Bio) group is a multidisciplinary team of computer scientists, biologists, mathematicians, and physicists working at the interface of computational modeling and life sciences. Our mission is to develop innovative algorithms and models that integrate biological and medical data to uncover biological mechanisms, support precision medicine, and simulate disease evolution.

We are driven by the need to interpret big data in biology and medicine through a systems approach — modeling cancer, genomics, and epidemiology with mathematical rigor and computational innovation, while exploiting high-performance computing (HPC) infrastructures to ensure scalability and efficiency.

Deep Sequencing & Omics Analysis

• Genome assembly, isoform detection, and chimeric RNA discovery and metagenomic analysis.
• CHiP-seq, histone data integration
• Multi-omics integration using AI
• Collaborations with MIRRI & Clinical Sciences for data analysis workflows on HPC infrastructure
• Multi-omics data analysis and integration
• AI for target trial emulation
• AI-based patients stratification and personalized medicine

Longitudinal Data & Functional Analysis

• Functional Data Analysis (FDA) for longitudinal data
• Advanced clustering and trajectory modeling

Cancer Modeling & Mechanistic Modeling

• Mathematical modeling of biochemical reactions in cancer
• Differential equation systems for simulating cancer progression
• Patient-specific treatment simulations
• Focus on colorectal, bladder, and cervical cancer
• AI approaches for patients phenomapping.
• Synthetic patients applied to RCT and observational studies

FAIR Data & Reproducible Research

• Tools like ORCA for reproducible omics data analysis
• Aligned with EU FAIR guidelines