'Google Map' of human metabolism could pave way for new medical treatments

LONDON: An international consortium of university researchers has produced the most comprehensive virtual reconstruction of human metabolism to date.
Scientists could use the model, known as Recon 2, to identify causes of and new treatments for diseases like cancer, diabetes and even psychiatric and neurodegenerative disorders.
Each person's metabolism, which represents the conversion of food sources into energy and the assembly of molecules, is determined by genetics, environment and nutrition.
The new model is based on earlier pioneering work by researchers at the University of California, San Diego.
"Recon 2 allows biomedical researchers to study the human metabolic network with more precision than was ever previously possible. This is essential to understanding where and how specific metabolic pathways go off track to create disease," said Bernhard Palsson, Galletti Professor of Bioengineering at UC San Diego Jacobs School of Engineering.
Palsson likened Recon 2 to Google mapping for its ability to merge complex details into a single, interactive map. For example, researchers looking at how metabolism sets the stage for cancerous tumor growth could zoom in on the "map" for finely detailed images of individual metabolic reactions or zoom out to look at patterns and relationships among pathways or different sectors of metabolism.
And just as Google maps brings together a broad set of data - such as images, addresses, streets and traffic flow - into an easily navigated tool, Recon 2 pulls together a vast compendium of data from published literature and existing models of metabolic processes.
As a multi-scale representation of the human metabolic network, Recon 2 provides essential context for data being reviewed by researchers.
Palsson and other scientists in the field have already successfully demonstrated the utility of such models in simple organisms such as yeast and E.coli. As a result, they have been able to engineer these organisms in the lab to improve the efficiency of ethanol production and predict drug resistance in bacteria.
One of the most promising applications for the network reconstruction is the ability to identify specific gene expressions and their metabolic pathways for targeted drug delivery.
Large gene expression databases are available for human cells that have been treated with molecules extracted from existing drugs as well as drugs that are in development. Recon 2 allows researchers to use this existing gene expression data and knowledge of the entire metabolic network to figure how certain drugs would affect specific metabolic pathways found to create the conditions for cancerous cell growth, for example. They could then conduct virtual experiments to see whether the drug can fix the metabolic imbalance causing the disease.
Recon 2 is already proving its utility, according to Ines Thiele, a professor at the University of Iceland and UC San Diego alumna, who led the Recon 2 effort.
Thiele said Recon 2 has successfully predicted alterations in metabolism that are currently used to diagnose certain inherited metabolic diseases.
The researchers presented Recon 2 in a paper just published online in the journal Nature Biotechnology.