U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Using Systems Biology to Understand Complex Microbial Communities
Published: March 27, 2012
Posted: May 08, 2012

The ability to effectively model and predict integrated functional properties across complex groups of microbes is critical to understanding major environmental processes. Advances in this area would also facilitate development of novel bioengineering approaches utilizing the unique functional compartmentalization that enables microbial communities to efficiently perform complex cooperative processes. In a new perspective essay, DOE researchers Karsten Zengler and Bernhard Palsson of the University of California San Diego describe a conceptual approach to extend systems biology tools developed to understand metabolic functions of single organisms to more complex multispecies communities. This is a considerable challenge since detailed physiological information is only available for the small fraction of microbes that can be cultivated. Cultivation independent approaches such as metagenomics provide a snapshot of overall functional potential but little information on dynamic processes or interactions between members. Building on preliminary successes with modeling interactions in simple two member partnerships, the authors suggest that a combination of these "bottom up" and "top down" approaches that incorporates efficient targeting of organisms performing processes of interest, high-resolution imaging of spatial process relationships, and more refined environmental 'omics techniques could yield predictive computational models of microbial community function.

Reference: Zengler, K., and B.O. Palsson. 2012. "A Roadmap for the Development of Community Systems (CoSys) Biology," Nature Reviews Microbiology, DOI:10.1038/nrmicro2763. (Reference link)

Contact: Joseph Graber, SC-23.2, (301) 903-1239
Topic Areas:

  • Research Area: Genomic Analysis and Systems Biology
  • Research Area: Microbes and Communities
  • Research Area: Biosystems Design
  • Research Area: Computational Biology, Bioinformatics, Modeling

Division: SC-23.2 Biological Systems Science Division, BER

 

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