Published on March 4th, 2013 | by Nathan0
Google Maps Of Human Metabolism, Most Comprehensive Map Of Human Metabolism Made To Date
The most complete and comprehensive systems reconstruction of human metabolism to date has now been created via the collaboration of a large number of international researchers. Some of the researchers have likened it to the the “Google Maps” of human metabolism, merging extremely complex details into a easy to navigate and understand interactive map. The researchers think that their new model, dubbed “Recon 2″, will be very helpful in the identification of the myriad causes of many common diseases, and of the available treatments. Cancer, neurodegenerative disorders, and diabetes, have been singled out as some of the diseases that the new map will help us to understand better. Because of all the factors that go into a human’s health and specifically their metabolism, it can be difficult to determine the exact factors that go not many complex diseases. Human metabolism is determined us determined primarily by lifestyle/environment, nutrition, and genetics. It’s these factors that determine how that person converts the food that they eat into energy and the variety of molecules that the human body uses.
It has been clear for a long time that metabolic imbalances have a profound role in the development of many diseases. So in recent years, many researchers have been focusing the connections between these imbalances and disease. Partly this has been enabled as a result of the data provided by the Human Genome Project, and improvements in the understanding of systems biology.
“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.
“It’s like having the coordinates of all the cars in town, but no street map. Without this tool, we don’t know why people are moving the way they are,” said Palsson.
As an example, when work is being done to determine how exactly metabolism sets up the conditions for cancerous tumor growth, the researchers can now simply “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. ” More or less the same thing that you do when you are looking up directions on a system such as Google maps, and can zoom in or out to see the context at any scale desired. In the same way that mapping services such as those incorporate large amount of data; street images, addresses, streets, traffic, etc; Recon 2 does the same but with published literature and existing models of various processes.
The model also makes the various, and important, contexts of any new research clear from the get go. Researchers 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.”
The original virtual reconstruction of human metabolism, Recon 1, was created back in 2007 by Palsson’s Systems Biology Research Group at UC San Diego. That reconstruction incorporated over “3,300 known biochemical reactions documented in over 50 years of metabolic research.” the new reconstruction, Recon 2, features more than 7,400 reactions. It was “built by bringing together researchers from dozens of institutions around the globe in a series of ‘jamboree’ meetings to refine and consolidate the data used in the reconstruction. Palsson said this jamboree approach helped the group establish common standards to build a consensus reconstruction, simplify its usability for biomedical researchers, and increase its transparency.”
And Recon 2 has already started showing its utility, according to Ines Thiele, a professor at the University of Iceland and UC San Diego alumna, who led the Recon 2 effort. Saying that Recon 2 has “successfully predicted alterations in metabolism that are currently used to diagnose certain inherited metabolic diseases.”
“The use of this foundational resource will undoubtedly lead to a myriad of exciting predictions that will accelerate the translation of basic experimental results into clinical applications,” said Thiele. “Ultimately, I envision it being used to personalize diagnosis and treatment to meet the needs of individual patients. In the future, this capability could enable doctors to develop virtual models of their patients’ individual metabolic networks and identify the most efficacious treatment for various diseases including diabetes, cancer and neurodegenerative diseases.”
“As much as Recon 2 marks a significant improvement over Recon 1, there is still much work to be done, according to the research team. Thiele said Recon 2 accounts for almost 1,800 genes of an estimated 20,000 protein-coding genes in the human genome.”
“Clearly, further community effort will be required to capture chemical interactions with and between the rest of the genome,” she said.
The new map, Recon 2, has been presented in detail in a new paper published March 3re in the journal Nature Biotechnology.
Image Credits: Anna Dröfn Daníelsdóttir, Freyr Jóhannsson, Soffía Jónsdóttir, Sindri Jarlsson, Jón Pétur Gunnarsson & Ronan M. T. Fleming from the University of Iceland.