SBI Research Areas

Systems Biology Ireland investigators use a combination of multi-omics, wet-lab experimental analyses (e.g. cell, animal and human studies) and computational modelling and simulation. This combined approach allows us to identify important nodes in signalling networks and to reconstruct the architecture and biological function of these networks. The approach also allows us to obtain an integrated and holistic view of how genes, proteins, and metabolites interact, and what goes wrong in a disease setting; unlocking potential for personalized approaches in medicine.

Computational Modelling

In biology, things never work in isolation. Individual molecular structures - genes, proteins and metabolites - work together in networks to get things done. In turn, cells function as part of a wider tissue, organ or system in the body. And organisms generally interact with each other in ecosystems and societies. Cells are communities of molecules, and organisms are communities of cells. Like in social networks, the analysis of these biological networks allows us to understand how they work, why they sometimes fail, and how we can repair them. SBI uses mathematical and computer modelling to analyse and understand this complexity.

In collaboration with SBI wet-lab scientists, our dry-lab researchers take observations or measurements from biological samples and build that data into a computer model, which shows how biological molecules are interacting and how the resulting networks function. We use different computational modelling methods ranging from biophysical, mechanistic models to machine learning and artificial intelligence (AI) driven models to to solve pertinent biological and biomedical questions, such as what drives cancer and drug resistance, why different people respond differently to therapies, and how we can custom tailor therapies to each patient. At the heart of this strategy is the analysis of the function of biological networks to understand and predict how molecular interactions change in disease settings. This requires seamless cooperation between biologists, computer scientists and clinicians. Our vision is to build Digital Twins, computer models of patients that allow us to understand disease mechanisms and their treatment through computational simulations in order to find the optimal and personalised treatment for each individual patient. 

Cancer

Cancer arises when cells in the body go rogue and stop cooperating with other cells in biological networks as healthy cells do. Instead of performing their physiological functions, they show unrestricted growth and even invade neighbouring and distant tissues. This is due to a rewiring of the cell's internal signalling networks that changes how they communicate, function and grow. SBI looks to understand these changes so that we can identify more effective combinations of existing anti-cancer drugs, new drug targets, and smart ways to overcome drug resistance.  

SBI is working on various types of cancer, including melanoma, ovarian, pancreatic, colorectal, breast and childhood cancers because they all share the common theme of molecular and cellular miscommunication. We believe that future cancer therapies will not targeting organs but network faults that drive cancer in different organs.

SBI strives to both improve current drug treatments (i.e., developing combinations of existing drugs that are more efficacious with less toxic side effects), and also to develop new anti-cancer drugs by identifying new targets that are based on their biological network context. To tailor such treatments to individual patients we are also constructing Digital Twins of cancer patients. 

VIDEO: We asked the SBI Community - Why are digital twins important for the future of Paediatric Oncology?  

Clinical Trials

We are taking our digital twin models from the computer/bench to the clinic. One model is in a (opens in a new window)clinical trial in the United States, where we have used a mathematical modelling system developed by SBI researchers as a basis for treating pancreatic cancer patients with a new drug combination. This is the first ever clinical trial based on a mathematical model of dynamic signal processing. This trial could  serve as paradigm leading to extensive applications in other difficult to treat cancers, such as advanced colorectal and lung cancer.

In collaboration with Prof Austin Duffy at the UCD Mater Misericordiae University Hospital in Dublin and Dr Stephen Thorpe at UCD, we are exploring the resistance mechanisms to a new treatment for pancreatic cancer that targets the MEK-ERK signalling network.

More clinical trials are in preparation, excitingly using Digital Twin models. Watch this space.