2018
DOI: 10.1186/s12864-018-4786-7
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Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment

Abstract: BackgroundIndividual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts… Show more

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Cited by 40 publications
(54 citation statements)
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“…New sequencing technologies allow now revealing the dynamics of community shifts and together with modeling approaches lay foundations for the educated design of community function [17]. Progress in sequencing technologies promotes the description of the bio-diversity and metabolic activity of microorganisms in ecological niches [18][19][20][21]. Parallel advancement of computational tools such as Genome-scale metabolic models (GSMM) and respective simulation algorithms such as Flux Balance Analysis (FBA) further enable in silico analysis of microbial interactions [19,20,22,23].…”
Section: Introductionmentioning
confidence: 99%
“…New sequencing technologies allow now revealing the dynamics of community shifts and together with modeling approaches lay foundations for the educated design of community function [17]. Progress in sequencing technologies promotes the description of the bio-diversity and metabolic activity of microorganisms in ecological niches [18][19][20][21]. Parallel advancement of computational tools such as Genome-scale metabolic models (GSMM) and respective simulation algorithms such as Flux Balance Analysis (FBA) further enable in silico analysis of microbial interactions [19,20,22,23].…”
Section: Introductionmentioning
confidence: 99%
“…This phenomenon of regulating cell density has been reported as beneficial within biofilms, and has also been reported for the gut microbiome in mice and in tsetse flies (Thompson et al., ; Enomoto et al., ). Additionally, metabolic exchanges are important among various bacterial members associated with the insect hosts (Ankrah et al., ; Opatovsky et al., ). A nice example is the interaction described in ‘Nutrient provisioning by bacteria’, between the symbionts B. cicadellinicola and S. muelleri , that jointly provide essential nutrients to the glassy‐winged sharpshooter H. vitripennis (Wu et al., ).…”
Section: Community Perspectives Of the Host Microbiomementioning
confidence: 99%
“…The network expansion algorithm computes the scope of a metabolic network from a description of the growth medium (seeds), that is, the family of metabolic compounds which are reachable according to a boolean abstraction of the network dynamics assuming that cycles cannot be self-activated. This algorithm has been widely used to analyse and refine metabolic networks [35,31,10,45,43], including for microbiota analysis [9,38,39,18]. As the scope ignores the stoichiometry of metabolites involved in reactions, it appears to be a good trade-off between the accuracy of metabolic predictions and the precision required for the input data.…”
Section: Value Of Cooperationmentioning
confidence: 99%