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Microbial community modeling using reliability theory.
Zilles, Julie L; Rodríguez, Luis F; Bartolerio, Nicholas A; Kent, Angela D.
Afiliação
  • Zilles JL; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • Rodríguez LF; Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • Bartolerio NA; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • Kent AD; Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
ISME J ; 10(8): 1809-14, 2016 08.
Article em En | MEDLINE | ID: mdl-26882268
ABSTRACT
Linking microbial community composition with the corresponding ecosystem functions remains challenging. Because microbial communities can differ in their functional responses, this knowledge gap limits ecosystem assessment, design and management. To develop models that explicitly incorporate microbial populations and guide efforts to characterize their functional differences, we propose a novel approach derived from reliability engineering. This reliability modeling approach is illustrated here using a microbial ecology dataset from denitrifying bioreactors. Reliability modeling is well-suited for analyzing the stability of complex networks composed of many microbial populations. It could also be applied to evaluate the redundancy within a particular biochemical pathway in a microbial community. Reliability modeling allows characterization of the system's resilience and identification of failure-prone functional groups or biochemical steps, which can then be targeted for monitoring or enhancement. The reliability engineering approach provides a new perspective for unraveling the interactions between microbial community diversity, functional redundancy and ecosystem services, as well as practical tools for the design and management of engineered ecosystems.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Consórcios Microbianos / Modelos Biológicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Consórcios Microbianos / Modelos Biológicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article