Personalised computational models of how our bodies work have the potential to become a powerful tool for understanding, and eventually treating, complex illnesses like cancer. Each person is biologically unique and models like these could help us to understand why some people respond to medical treatments in unexpected ways.

A study published in the journal Science employed computational analysis to investigate differences between tumour cells and amongst cancer patients. A total of 6,753 custom models were created using anonymised data from real cancer patients. These models are freely available through a custom section of BioModels an online database of computational models representing biological processes.

BioModels is a collaboration between scientists at the Babraham Institute and EMBL–EBI and is supported by the Biotechnology & Biological Sciences Research Council (BBSRC). This is the most extensive single collection of models that has been added to the platform since its creation in 2006.

These models are a valuable resource that could eventually help scientists to predict the effects of new drugs on a wider range of patients. Scientists hope that approaches like this will lead to better treatments with fewer side effects. It could even result in personalised medicines tailored to the unique needs of individual patients. In the future, using advanced models like these in the clinic could even help to predict the best therapies for treating patients.

Varun Kothamachu, Postdoctoral Computational Biologist at the Babraham Institute and a Visiting Researcher at EMBL–EBI, explained: “Each model is associated with a single patient and represents genetic information found in their tumour samples. In their current form, these models can help in understanding the difference between patients and amongst different cancer types. In the future, models like these, derived directly from patient data, provide a way of studying drug response in individual patients. This allows us to analyse the possible effects of a drug on a person before prescribing treatment.”

The BioModels team have worked closely with the authors of the paper to ensure that every model is annotated in detail so that relevant models can be easily found and used by as many researchers as possible. Every model includes information about the age and gender of the patient, along with the type of cancer.

“We think that these models will be a great resource for many cancer researchers,” says Adil Mardinoglu, Assistant Professor at Chalmers University of Technology and one of the senior authors of the paper. “We are confident that BioModels is a great platform to share and improve our models.”

Anybody can submit a model to the BioModels. If you have computational models representing biological systems that you would like to share with the wider community, please send them to BioModels here. For researchers with large datasets or any specific enquiries, please contact the BioModels team directly.