Building on earlier pioneering work by researchers at the
University of California, San Diego, 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 researchers presented Recon 2 in a paper published
online in the journal Nature
How Recon 2 works
Doctors have long recognised the importance of metabolic
imbalances as an underlying cause of disease, but scientists have been ramping
up their research on the connection as a result of compelling evidence enabled
by the Human Genome Project and advances in systems biology, which leverages
the power of high-powered computing to build vast interactive databases of
"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.
He 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 tumour 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.
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 neighbourhood, 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.
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.
Palsson's Systems Biology Research Group at UC San Diego
built the first virtual reconstruction of the human metabolism network, known
as Recon 1, in 2007 with a six-person team. It featured more than 3,300 known
biochemical reactions documented in over 50 years of metabolic research.
Recon 2, which contains more than 7 400 reactions, 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. Recon 2
will facilitate many future biomedical studies and is freely available here.
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 earned her Ph.D. in bioinformatics as a student
of Palsson's and was part of the original Recon 1 team. Several other UC San
Diego alumni, and former Palsson students, participated in the consortium from
their new institutions, including Neema Jamshidi (Ph.D., 2008, M.D., 2009), who
is now interning at UCLA; Jason Papin (Ph.D., 2004), a professor at the
University of Virginia; and Nathan Price (Ph.D., 2005), who is a professor at
the Institute for Systems Biology in Seattle, Wash.
Thiele said Recon 2 has successfully predicted alterations
in metabolism that are currently used to diagnose certain inherited metabolic
"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
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.