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 last week 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, explains: “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. For researchers with large datasets or any specific enquiries, please contact the BioModels team directly.
The research that produced these models was led by researchers from KTH-Royal Institute of Technology, Stockholm, Uppasla University and Chalmers University of Technology Gothenberg.
UHLEN, Mathias, et al. (2017). A pathology of the human cancer transcriptome. Science. Published online 18 August; DOI: 10.1126/science.aan2507'
Computer models of cancer. Spencer Phillips, EMBL-EBI
BioModels is a repository of computational models of biological processes. It hosts manually-curated models described in peer-reviewed literature and models generated automatically from pathway resources (Path2Models). All models are provided in the public domain and enriched with cross-references from external data sources.
To find out more, go to the BioModels website and check out the latest Model of the Month to discover more about the models currently available on BioModels.
Dr Jonathan Lawson, Babraham Institute Communications Manager email@example.com
About the Babraham Institute
The Babraham Institute receives strategic funding from the Biotechnology and Biological Sciences Research Council (BBSRC) to undertake world-class life sciences research. Its goal is to generate new knowledge of biological mechanisms underpinning ageing, development and the maintenance of health. Research focuses on signalling, gene regulation and the impact of epigenetic regulation at different stages of life. By determining how the body reacts to dietary and environmental stimuli and manages microbial and viral interactions, we aim to improve wellbeing and support healthier ageing.
22 August 2017