Global team of scientists has developed a representation of human metabolism applicable to computational modeling.
An international group of researchers have recently developed the most comprehensive representation of human metabolism through the integration of information from five different sources. The community-driven computational model, known as Recon 2, reported in the journal Nature Biotechnology, builds on previous generations of metabolic reconstructions and may find its use in identifying biomarkers of metabolic diseases, for example, glycogen storage disorder. In addition, the metabolic reconstruction may play a role in discovering cancer drug targets and predicting unwelcome drug side effects.
The scientists who carried out the investigation hope that the model will provide a deeper understanding of how human metabolism impacts health and disease. A variety of models of human metabolism have co-existed but until now represented only a subset of the knowledge available, this model has combined content from a variety of databases to provide a more complete overview.
Pedro Mendes, a computational systems biologist, leader of the Biochemical Networks Modeling Group at the Virginia Bioinformatics Institute and one of the authors of the article, explained the significance of the recent development, “This is important because we are finally mapping the links between the human genome and metabolism. The results provide a framework that will lead to a better understanding of how an individual’s lifestyle, such as diet, or a particular drug they may require, is likely to affect them according to their specific genetic characteristics.”
Compared to earlier-generation reconstructions, Recon 2 includes approximately 1.7-times more unique metabolites and approximately two-times more reactions. The recently published article demonstrated predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with an accuracy of 77%, compared to experimental data. The international research team based in Manchester, Cambridge, Edinburgh, Reykjavik, San Diego, Berlin and others, mapped 65 cell-type specific models, which could lead to further investigation into cell-specific metabolic properties.
Mendes commented on how the model, which is freely available online, could affect the future of disease treatment, “The model takes us an important step closer to personalized medicine, where treatments will be tailored according to the patient’s genetic and metabolic information.”