BioModels: New patient derived metabolic models

BioModels: New patient derived metabolic models

22 Aug 2017 - 14:51

Summary

  • 6753 patient derived genome scale metabolic models of 21 different types of cancer are now freely available through BioModels
  • The BioModels team worked closely with the authors to curate and annotate the models, making them easy to find and build on by researchers worldwide
  • Researchers studying cancer can use these models as a starting point towards exploring what happens in tumours at a molecular level and understand variability amongst patients and different types of tumours

August 22, Hinxton – A study published in the journal Science employed systems-level analysis to investigate heterogeneity in cancer. The 6753 metabolic models used in the analysis were derived by using gene expression data from tissue samples collected from cancer patients. These models, which are now freely available through BioModels, allowed the authors to explore the metabolic variability between different types of cancer, and among individual patients with the same type of cancer.

Models discussed in the paper are freely available on the Patient-derived genome-scale metabolic models page in BioModels.

Why does it matter?

These genome-scale metabolic models give us an insight into the impact of genomic variability amongst individual patients and tumour types. This information could be used to understand the molecular mechanisms underlying different tumours.

“These models can be interesting tools for researchers developing drugs to treat cancer because each model contains information about the metabolic pathways occurring in individual patients, the age and gender of the patient, as well as the type of cancer,” explains Varun Kothamachu, Postdoctoral Computational Biologist at the Babraham Institute and a Visiting Researcher at EMBL-EBI. “Researchers add information from patient derived tissue samples to a base model to build these patient-specific models,” continues Kothamachu. “That’s where the value of these models lies. In their current form, these patient derived models can be an excellent starting point for building in-silico models to understand drug response. These models would allow researchers to analyse how different drugs work on individual patients, in addition to supporting the identification of more effective drug targets for treating cancer.” 

A close collaboration

The BioModels team was approached by researchers from the KTH-Royal Institute of Technology in Stockholm and collaborators to curate and disseminate these models.

“The authors of the paper had previously submitted models to BioModels, but never on this scale,” points out Rahuman Sheriff, BioModels Project Lead at EMBL-EBI. The Hermjakob team at EMBL-EBI and Le Novère’s team at the Babraham Institute worked with the researchers to make sure that the models fulfilled the relevant standards and that the annotations were correct. “We tried to make as much of the metadata available in standard formats,” continued Sheriff. “This makes the models much more valuable because it allows other researchers to easily find models that they are interested in, and build on them.”

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

Anybody can submit a model to 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 models with large datasets or any specific enquiries, please contact the BioModels team.

BioModels

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 or check out the latest Model of the Month.

Discover more

Source article

UHLEN, Mathias, et al. (2017). A pathology of the human cancer transcriptome. Science. Published online 17 August; DOI: 10.1126/science.aan2507'

Contact the news team

Mary Todd Bergman
Senior Communications Officer
mary@ebi.ac.uk
+44 (0)1223 494 665

Oana Stroe
Communications Officer
stroe@ebi.ac.uk
+44 (0)1223 494 369

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