Photo by Melissa Maki
Jason Papin, an assistant professor of biomedical engineering, is helping to further understanding of the human genome.
Over the last decade, scientists have sequenced the genome of scores of organisms, from bacteria such as Escherichia coli and Salmonella enterica to primates such as the chimpanzee and, of course, human beings. This has been an enormous undertaking. Mapping the human genome, for instance, involved specifying the composition of each of the 3 billion chemical building blocks that make up our DNA. Impressive as this accomplishment is, much more needs to be done before we can measure the impact of these advances in lives saved or extended.
The next challenge for scientists is to annotate these genomes, placing each of an organism’s genes at a specific place on its DNA sequence and determining how a gene and the protein it encodes contribute to the constant stream of events required for the organism to survive and reproduce. Computational modeling being conducted by Jason Papin, an assistant professor of biomedical engineering at U.Va.’s School of Engineering and Applied Science, is helping to accelerate this process.
There is too much information for scientists to attempt to systematically catalog the function of each of an organism’s genes one by one — the single-celled Escherichia coli has close to 4,400 genes, while human beings may have as many as 25,000. The approach that Papin and his graduate students use is to develop a set of algorithms that describe the interactions that occur among the proteins the genes produce. They base these algorithms on an exhaustive survey of the scientific literature and the vast databases of information generated by high-throughput studies of gene expression. “We use tools from linear algebra, optimization and systems engineering to take this data and construct models of what are essentially biological networks,” Papin says.
In the process of constructing and running the models, Papin and his colleagues can pinpoint missing information that would contribute to a more accurate understanding of a biological process, suggesting fruitful directions for laboratory research that could yield a more complete annotation of a genome. In addition, as their models gain in accuracy, they can manipulate the variables, shedding light on basic biology and suggesting possible targets for drug discovery. Their recent work on a human pathogen, Leishmania major, highlights the value of this work.
Leishmaniasis is a debilitating tropical disease found in 88 countries — in the Western Hemisphere it’s geographic range stretches from northern Argentina to southern Texas — that infects 2 million people a year and causes close to 60,000 deaths worldwide. Spread by the sand fly, it causes eruptions of skin sores and, in serious cases, can attack the liver and the spleen. Papin and graduate student Arvind Chavali (BME ’06, ’07, ’11) chose to create an L. major model because its genome had just been published, and the organism had begun to develop resistance to existing therapies.
Together, they focused on L. major’s metabolic system, a reconstruction that accounted for 560 genes, 1,112 reactions and 1,101 metabolites in eight compartments within the cell. Once the model was validated, they experimented by shutting down genes one and two at a time, developing a list of deletions that were lethal. At the same time, they generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome.
Papin is beginning to partner with chemists, who will use the information he developed on lethal gene deletions in L. major as a starting point for screening drug candidates for leishmaniasis. “The wonderful thing about using a model,” Papin says, “is that you can generate results in a matter of hours that would have taken years to achieve in the laboratory.”
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