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Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle. | LitMetric

AI Article Synopsis

  • Understanding the interactions between microbial communities and their surroundings is crucial for predicting biodiversity based on physical and chemical factors, but this remains a challenging task in microbial ecology due to community complexity and incomplete quantitative analyses.
  • To address these challenges, researchers propose using a probabilistic framework based on Event Transition Graph (ETG) theory to model microbial community structures against chemical data.
  • This approach involves reverse engineering probabilities from ETG models to validate experimental observations and predict constraints on microbial communities, which can inform future field experiments and highlight key microbial functions and strains in ecosystems.

Article Abstract

Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360161PMC
http://dx.doi.org/10.3389/fmicb.2018.03298DOI Listing

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