redOrbit Staff & Wire Reports – Your Universe Online
An international team of researchers have produced what they are dubbing the “Google Maps” of human metabolism — the most comprehensive virtual recreation of the cellular chemical transformation ever crafted.
The model is known as Recon 2, and according to the University of California, San Diego (UCSD) — one of the institutes behind the research — it could potentially be used to locate the causes of cancer, diabetes, and neurodegenerative diseases. Furthermore, the metabolism map might even help medical professionals develop new treatments for those conditions.
“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,” Bernhard Palsson, a bioengineering professor the UCSD Jacobs School of Engineering, explained.
He likened it to the Mountain View, California-based tech giant´s popular web mapping application because, like Google Maps, Recon 2 is capable of combining complicated details into one 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,” the university explained. “This is not unlike how you can get a street view of a single house or zoom out to see how the house fits into the whole neighborhood, city, state, country and globe. 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.”
“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,” they added. “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.”
The international team — which includes researchers from the US, UK, Austria, Germany, Iceland, Japan, the Netherlands, Russia and Sweden — builds upon the first virtual reconstruction of the human metabolism. That model was known as Recon 1 and it was developed by a six-person team in 2007.
Recon 1 included over 3,300 known biochemical reactions tracked over the course of five decades worth of research. Recon 2 contains more than double that amount — it includes over 7,400 different reactions. Their findings have been published in the latest edition of the journal Nature Biotechnology.
“This research is the second important stage of our understanding of the human genome. If the sequencing of the human genome provided us with a list of the biological parts then our study explains how these parts operate within different individuals,” study co-author Pedro Mendes of the University of Manchester’s School of Computer Science said in a statement.
“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,” he added. “The model takes us an important step closer to what is termed ‘personalized medicine’, where treatments are tailored according to the patient’s genetic information.”
Recon 2 will facilitate many future biomedical studies and is freely available here.
Comments