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Model-based quantification of metabolic interactions from dynamic microbial-community data.

Mark Hanemaaijer1,2, Brett G Olivier1, Wilfred F M Röling2

  • 1Systems Bioinformatics, Amsterdam Insititute for Molecules, Medicines and Systems, VU Amsterdam, The Netherlands.

Plos One
|March 10, 2017
PubMed
Summary
This summary is machine-generated.

Inferring microbial metabolic fluxes is key. A model-based approach using synthetic co-cultures of Clostridium acetobutylicum and Wolinella succinogenes successfully identified these fluxes and interactions, highlighting nitrogen

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Area of Science:

  • Microbial Ecology
  • Systems Biology
  • Metabolic Engineering

Background:

  • Inferring metabolic-exchange fluxes between microbial species from community data is a significant challenge.
  • Accurate flux determination is crucial for understanding microbial interactions and ecosystem functions.

Purpose of the Study:

  • To apply a model-based approach to integrate experimental data for inferring metabolic-exchange fluxes in a microbial co-culture.
  • To investigate the influence of environmental conditions, specifically nitrogen source, on interspecies hydrogen transfer rates.

Main Methods:

  • Designed a synthetic anaerobic co-culture of Clostridium acetobutylicum and Wolinella succinogenes.
  • Utilized stoichiometric models representing the current physiological understanding of the microorganisms.
  • Integrated community-level data including species abundances and metabolite concentrations.

Main Results:

  • The model-based approach successfully inferred the identity and magnitude of metabolic-exchange fluxes, revealing unexpected interactions.
  • Identified that the nitrogen source significantly influences the rate of interspecies hydrogen transfer.
  • Model predictions indicated potential for engineering strategies by identifying optimal metabolic exchange rates.

Conclusions:

  • Model-based integration of heterogeneous data is effective for inferring metabolic-exchange fluxes from community-level experimental data.
  • The study highlights the strengths and limitations of current physiological understanding in microbial ecology.
  • Findings suggest specific requirements for further physiological studies to refine models and understand microbial interactions.