Biomedical engineers have developed a computational model that will help biological researchers clearly identify the significance of variations between different genomes -- the complex sequences of DNA and RNA at the foundation of all living organisms. The findings will be published March 31 in the open-access journal PLoS Computational Biology.
The international team of researchers, from the University of Virginia, USA, Wageningen University, The Netherlands, and Helmholtz Center for Infection Research, Germany, demonstrated their approach by focusing on the pathogen Pseudomonas aeruginosa--a bacterium that causes about 10 percent of hospital-acquired infections. The bacterium is especially problematic for burn victims and those with cystic fibrosis or those whose immune systems are compromised, and this new approach is an important first step toward improving their treatment.
In recent years, researchers have been mapping the genomes of multiple organisms and they can now measure the activity of specific genes across the entire genome at the same time and under multiple environments. The emerging field of systems biology integrates this information into computational models. While these models can be used to predict which genes are critical for various cell functions--such as how the cells will respond to medicine or how fast they will grow in different environments--there are still limitations to the science.
"Unfortunately, as these models get built, some of the differences between the models of two cells or two bacteria can be an artifact of the model-building process itself," said Jason Papin, one of the authors and an assistant professor of biomedical engineering at University of Virginia. "Our paper presents an approach for reconciling two models so that you can have confidence that the differences are actually present in the living systems. With the reconciled models, you can then start to ask very specific questions like which genes that are unique to each bacterium are essential for some basic processes."
To illustrate the efficacy of their approach, the researchers compared the pathogen Pseudomonas aeruginosa--one of the principal antibiotic resistant bacteria--with the non-pathogen Pseudomonas putida. The reconciled models clarified how the explicit differences between the genomes of the bacteria mapped to differences in functions of the cells.
Funding: MAO and JAP acknowledge funding from the National Science Foundation (CAREER grant 0643548 to JAP), the National Institutes of Health (R01-GM088244 to JAP and NIH Biotechnology Training Grant support to MAO), and the Cystic Fibrosis Research Foundation (grant 1060 to JAP). VAPMdS and JP acknowledge funding by the European Union through the Microme project (www.microme.eu, GA # 222886-2) and the Bundesministerium fu¨ r Bildung und Forschung (BMBF), Germany through the ERA-NET project PSYSMO (www.psysmo.org, AZ #0313980A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist. Citation: Oberhardt MA, Puchałka J, Martins dos Santos VAP, Papin JA (2011) Reconciliation of Genome-Scale Metabolic Reconstructions for Comparative Systems Analysis. PLoS Comput Biol 7(3): e1001116. doi:10.1371/journal.pcbi.1001116
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