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Mathematical Model Identifies Genes which Battle Hepatitis C

Last updated:13August2007

Infection Control Today

Joint research by Dr. Leonid Brodsky, of the Institute of Evolution of the University of Haifa (see article below), and Dr. Milton Taylor, of Indiana University, led to the discovery of a mathematical method which can identify which genes in our bodies conduct the battle against the various viruses that attack us. In their research, they identified 37 genes out of 22,000 possible genes which fight the hepatitis C virus.

"When we know which genes are responsible for fighting the viruses which attack our liver, we will be able to look for the medications which will activate these genes most favorably," said Brodsky. The team conducted clinical trials, supported by the Health National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the U.S. National Institutes of Health (NIH), which included 400 patients at eight different centers in the United States. The results will be published in the journal PLOS ONE.

The hepatitis C virus, found mostly among many patients who have had a blood transfusion or who share needles, attacks the liver and in extreme cases can cause cancer of the liver. At present, there is one well know medication, interferon, used to treat the virus; however, while some patients respond to the treatment with interferon, others do not. In this research, the clinical study was combined with the mathematical model developed by Brodsky. The study identified 37 genes which are key for patient response to treatment.

"In the specific case of hepatitis C, we have now isolated the genes that show which patients will respond to treatment. Until now, all patients received treatment for an extended period of time without knowing whether or not they would respond. In the future, we hope to find other medications that will be more effective in activating all of the 37 genes." summarized Brodsky.

He further explained that this mathematical model is not limited to identifying the genes which fight viruses that attack the liver. It can also be applied further in the fields of medicine, biology and agriculture.
Source: University of Haifa


Scientists model hepatitis C virus By Todd Hanson

Model may lead to new treatments for a deadly virus

One of the most common life-threatening viral infections in the United States today is hepatitis C virus (HCV). The standard treatment is successful in only about 50 percent of treated HCV chronic patients, with no effective alternative treatment for those who fail to clear the virus.

Laboratory scientists, in collaboration with researchers from the Center for HCV Research at Rockefeller University, recently developed the first mathematical model of intracellular HCV replication. The model is designed to help scientists and medical researchers develop a better understanding of the dynamics of replication, as well as the mechanisms of drugs currently being used to treat HCV. This new understanding may eventually lead researchers to a more successful treatment for the virus.

In research published in the Journal of Virology, Los Alamos theoretical biophysicist Harel Dahari and his colleagues describe how they leveraged recent advances in HCV cell culture replication to provide the quantitative data necessary for creating a computer model of the dynamic interplay between host and virus during replication of the virus in Huh-7 (human liver) cells.

Wiith more than 200 million people in the world infected with HCV and half of those not responding to treatments,” said Dahari, “our model can be an important tool for understanding the HCV replication mechanisms. Perhaps more importantly, it may prove useful in designing and evaluating new antivirals for use in combating the virus.”

According to the researchers, the next step will be to incorporate virus production and infection into one comprehensive model of the complete HCV life cycle.

Dahari, Ruy Ribeiro and Alan Perelson of Theoretical Biology and Biophysics (T-10) and Charles Rice from Rockefeller University collaborated in this research, which was funded by the National Institutes of Health.