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

BER Research Highlights

Developing Synthetic Microbial Communities to Improve Predictions of Their Behavior
Published: May 20, 2014
Posted: October 03, 2014

Microbial communities populate every natural environment, playing critical roles in fundamen­tal biological and environmental processes such as food webs and carbon cycling. Members of microbial communities interact with each other both as competitors and collabora­tors. Understanding the complex interactions within these communities is necessary to predict and eventually manipulate their behavior for biotechnol­ogy applica­tions. Studying natural microbial consortia is extremely challenging, so simple microbial cocultures are often used to gain insights on microbial crossfeeding and communication. However, such studies rarely represent natural systems, and, therefore, more complex synthetic microbial communities are needed to model the development and evolution of microbial populations. Researchers at Harvard and Columbia universities report the development of a system of synthetic microbial communities composed of up to 14 Escherichia coli mutants, each one incapable of synthesizing a different amino acid. Using this system, the investigators could experimentally determine the behavior of the different members of the consortium, identifying mutants that act as keystone species or that promote positive or negative interactions. After several generations, these bacterial populations tend to become enriched in mutants that cannot produce metabolically costly amino acids (those that require more energy to synthesize). The authors hypothesize that such mutants persist in the population by crossfeeding from less abundant microbes that provide needed amino acids. This hypothesis was supported by the observation that the majority of the microorganisms whose genomes have been sequenced do not have the metabolic capacity to produce costly amino acids. These results will enable develop­ment of more accurate predictive models of microbial communities and their iterative improve­ment by experimentation, advancing toward a more comprehensive understanding of microbial communities such as those involved in carbon cycling.   

Reference: Meea, M. T., J. J. Collins, G. M. Church, and H. H. Wang. 2014. “Syntrophic Exchange in Synthetic Microbial Communities,” Proceedings of the National Academy of Sciences (USA) 111(20), E2149-56. DOI:10.1073/pnas.1405641111. (Reference link)

Contact: Pablo Rabinowicz, SC-23.2 (301) 903-0379
Topic Areas:

  • Research Area: Carbon Cycle, Nutrient Cycling
  • Research Area: Genomic Analysis and Systems Biology
  • Research Area: Microbes and Communities
  • Research Area: Sustainable Biofuels and Bioproducts
  • Research Area: Biosystems Design
  • Research Area: Computational Biology, Bioinformatics, Modeling

Division: SC-23.2 Biological Systems Science Division, BER


BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)