Our research and activities are organised around various research and technology themes:

Biosensors and nucleotide editing

Gene editing and sensing technologies have revolutionised our understanding of biology and has opened novel avenues for molecular diagnostic and gene therapies. We develop new computational and encxperimental tools with the goal of designing new biosensors, better molecular diagnostic methods, and optimal nucleotide editing technologies. The ultimate goal is to find and predict the efficiency and specificity of the next generation of gene editing and sensing tools for precision medicine. CCBS groups working in this space include:

Single cell and spatial biology

Living cells are an integrated network of genes, proteins and dynamic metabolic reactions that give rise to functions that are essential for life. The key to gain a deep understanding of the mechanistic process that promotes healthy and abnormal development of the mammalian biological system is to map out the physical and chemical responses of single cells over time and space. The timing of numerous molecular processes and the relative spatial cellular configurations in which they occur are critical in cell signaling, development, homeostasis, and immune responses. The goal of single cell and spatial analysis is to develop novel computational informatics and imaging tools that can study and correlate behavior of molecules in cells within the context of intact tissue and/or living organisms across space and time at high resolution. CCBS Groups working in these space include: 

Genome data, health and high-throughput phenotyping

A great many human diseases, including cancer, have a genetic basis. But our understanding of how genetic variation and mutation causes disease is still in its infancy.  This is a very real problem, as we increasingly move to a dependence on patient genomes to understand the pathogenesis of their diseases. To our advantage, we can tackle this problem with a rapidly-growing corpus of complex genomic, clinical and phenotype data. These data science tasks involve the analysis of massive datasets, the development of new machine learning and deep learning tools to predict patient outcomes, and the intensive usage of high-performance computing systems. An active project is now harnessing high-throughput data from automated instruments (sequencers, cytometers, microscopes) to generate the massive information that can be used to train diagnostic data tools of the future. These tasks are often collaborative and bring together researchers working in medicine and genomics, data scientists, computational biologists, clinicians and patients.  CCBS Groups working in this space include:

RNA regulation and programming

The functional capacity of cells is defined by how their DNA is expressed into RNA molecules to define a cell’s transcriptome. Precise knowledge of a transcriptome, in terms of the RNA sequences, their abundances and structures, and their chemical modifications (i.e. the epitranscriptome), is fundamental for our understanding of relevant physiological mechanisms and to determine individual phenotypes that are critically important for health.  However, our understanding of how RNA features regulate and control complex biological processes is still limited, and our ability to modify RNA for specific purposes, such as vaccines, is only emerging. We develop computational tools to discover and characterise the RNA molecules operating in cells and their functions, and build predictive algorithms that capture the properties of RNA molecules that can be used to design RNAs with optimal properties. Our goal is to characterise the function of RNA molecules in disease and to develop RNA programming tools for biomedical intervention.  CCBS Groups working in this space include: