Scientists image tuberculosis regulatory-metabolic network

Scientists at the University of Illinois have recently developed a way to utilize the large quantities of genomic and metabolic data that are generated through high-throughput genomics as well as other techniques, creating the first genome-scale regulatory-metabolic network of Mycobacterium tuberculosis

The Institute of Genomic Biology at the university has created an algorithm that integrates both data sets automatically. The model, called the probabilistic regulation of metabolism, allows researchers to stimulate a given gene in a regulatory metabolic process and see how that will affect an entire network.

"PROM provides a platform for studying the behavior of networks in a wide range of different conditions," principal investigator Nathan Price said. Rice is an associate professor of chemical and biomolecular engineering at the University of Illinois.

Using this model, the researchers were able to create the first genome-scale regulatory-metabolic network of Mycobacterium tuberculosis, the bacteria that causes the disease tuberculosis. The team’s results were published in the Proceedings of the National Academy of Science.

The scientists decided to use tuberculosis after using E. Coli as a proof of principle. They targeted tuberculosis since it was not as extensively studied as E. Coli.

PROM may be particularly helpful to tuberculosis researchers because it may identify and target the pathways that keep the bacterium alive during its dormant stage. PROM is also a significant advance because it successfully integrates the statistically derived transcriptional regulatory network with a biochemically derived metabolic network.

"That is the new part," Price said. "People have created regulatory models and metabolic models. But there has been nothing before that could combine them in this automated fashion. It is difficult to get these two to talk to each other in the right way."