Abstract
BackgroundMicrobial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis.ResultsIn this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI.ConclusionsThe RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0199-2) contains supplementary material, which is available to authorized users.
Highlights
We evaluated the performance of the rule-based microbial network (RMN) algorithm by calculating the following measures: true positive rate (TPR), true negative rate (TNR), F-measure, and Accuracy
In this paper we presented RMN algorithm, a rule-based algorithm using the operational taxonomic unit (OTU)-triplet model with parametric weighting function, to construct microbial regulatory networks
RMN algorithm is both computationally feasible and capable of detecting biologically significant progresses embedded in a microbial community
Summary
Many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. Elucidating competitive and cooperative relationship is a challenge in generating a microbial interaction network because of the direction of such interactions [5]. Recent studies have shown that competitive interactions can drive the evolution of cooperation in microbial ecosystems [9]. Identifying competitive and cooperative relationships between microbes is profound importance; the directional nature of such interactions poses as a difficult challenge in network construction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
