Systems Biology for EnteroPathogens
New publication utilizing multi-omic datasets and computational network modeling to elucidate Yersinia virulence mechanism in Molecular BioSystems (Ansong et al. 2013) selected as Journal Cover for January 2013 edition (Volume 9, Number 1).
Exciting new publications utilizing genome scale modeling approaches combined with high throughput omics data Joshua A. Lerman in Nature Communications and A. Bordbar in Molecular Systems Biology. In Joshua A. Lerman in Nature Communications, the combined modeling and omics results have predicted immune cell stimulating and inhibiting metabolites. A. Bordbar in Molecular Systems Biology demonstrates an improved method for modeling that improves the predictive power of metabolic models.
Welcome to the Systems Biology for EnteroPathogens program, a multi-institution center, established to deepen our fundamental understanding of the complex processes of microbes and their interactions with the host. This program is one of four centers established by the NIH-National Institute of Allergy and Infectious Diseases.
The outcome of an intracellular bacterial infection - bacterial replication and host cell death versus host cell containment of the pathogen - is a complex process that involves multiple interactions between the host cell and the attacking bacteria. Subtle differences in the host and bacterial genome can have profound effects on all stages of pathogenicity, from host specificity, to invasion, to replication. By using a systems approach that integrates detailed knowledge of the bacterial genome, transcriptome, and proteome with the dynamic changes in metabolism that occur in both host and bacteria upon infection, we can develop a better understanding of these processes.
Our systems approach involves the use of iterative and complementary computational and experimental “omics” methodologies to analyze, identify, quantify, model, and ultimately predict the overall molecular processes involved in the pathogenesis of Salmonella and Yersinia species in macrophages. Our premise is that knowledge gained from the coordinated analysis and modeling of these two genera within the family Enterobacteria will lead to improved control and therapeutic treatment strategies not only for these specific pathogens, but also for related gram negative bacteria.
The high-throughput results and computational models developed under this research contract will be made publically available to the research community. Through release of the data we plan to enable the broader community to: 1) develop and test new algorithms of data extraction and analysis, 2) use the results to compare with similar experiments, and 3) help in providing expert feedback to improve and refine the computational models of these pathogens. Please see omics.pnl.gov for currently available omics data.