Systems Biology Ireland investigators use a combination of traditional wet-lab experimental analyses (e.g. cell, animal and human studies) with computational modeling and simulation. This combined approach allows important nodes in signalling networks to be identified quickly and at a lower cost than might otherwise be possible. The approach also allows SBI researchers to better determine how genes, proteins, and metabolites interact, and what goes wrong in a disease setting; unlocking potential for personalized approaches in medicine.
In biology, things seldom work in isolation. Individual molecular structures - genes, proteins and carbohydrates - work together 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. SBI uses mathematical and computer modelling to help make sense of this complexity.
In collaboration with SBI wet-lab scientists, our dry-lab researchers take a series of observations or measurements from biological samples, and build that data into a computer model which shows how biological molecules are interacting. They also use machine learning or AI to predict how molecular interactions might change in disease settings, and can also integrate data from smaller systems (e.g., cells) into larger systems such as tissues, organs or the body as a whole. In turn this informs what wet-lab experiments should be done next, and the iterative cycle between wet and dry lab continues.
In cancer, tumour cells in the body go rogue (i.e., they stop listening to the body’s signals in the way that healthy cells do). This is due to rewiring of the cells internal signaling networks; changing how they communicate and grow. SBI looks to understand these changes so that we can identify more effective combinations of existing anti-cancer drugs for patients and help to design future drugs to silence tumours.
SBI is working on various types of cancer, including colorectal and childhood cancers to identify the communications mechanisms that make them cancers and also the signalling qualities that could make them unique.
SBI strives to both improve current drug treatments (i.e., devloping combinations of existing drugs that are more efficacious with less toxic side effects), and also to develop new anti-cancer drugs by identifying nodes in cancer signalling (and predicting what could happen if they become disrupted).