The program aims to increase our understanding of genome function in humans and all living organisms. The programme has broad interests in topics ranging from human disease, genetic variation, regulation of genome function, biodiversity, and the evolution of genomes. Members of the programme develop methods in computational biology, statistical genetics, and AI, and use innovative technologies to model genome function in health and disease. The programme hosts the EGA team at the CRG, which together with EMBL-EBI, manages the European Genome-Phenome Archive (EGA).
Scientist in the program use quantitative methodologies, engineering approaches and mathematical modelling to discover new principles underlying cell functioning. Their research bridges scales - from molecules to organelles to cells, tissues and organisms - to address biological questions of fundamental importance, relevant for health and disease.
The program focuses on investigating the mechanisms that lead to expression of our genome during homeostasis, cell reprogramming, and disease. We use quantitative ‘omics’ technologies, mathematical modeling, cellular biology and mouse genetics to understand chromatin organization, transcription, splicing, mRNA translation, signaling and RNA modification.
In the Systems and Synthetic Biology program we are transforming molecular biology into a predictive engineering science. In the SSB program we combine large-scale quantitative data generation with modelling and machine learning to solve the foundational science to make molecular biology predictive and programmable. Part of our work is performed through a joint-initiative with EMBL Barcelona, the Barcelona Collaboratorium for Modelling and Predictive Biology.
The human body consists of trillions of cells, but a mutation in one of them can cause cancer. This example demonstrates that biology is not a process driven by averages and highlights the importance of single cell-level studies. The massively parallel profiling of tissues and even entire organisms at the single-cell level have enabled paradigm shifts in many fields of biological sciences, ranging from evolutionary biology over stem cell biology to cancer research.