To model complex relationships (one species influencing multiple others), methods employing mathematical and statistical models have been developed recently. In general, these methods cannot capture the complexity of microbial interactions and cannot elucidate how microbes regulate each other. Although other derived approaches can identify the pairwise relationships between microbes by using correlation estimation, they do not identify the nature and the strength of these relationships. In such networks, nodes correspond to organisms and an edge between two nodes represents the significant relationship of two taxa across a set of time series samples. One commonly used method is the similarity-based network construction where the co-occurrences of two species over multiple time-series samples are measured to infer their interaction. There are several approaches in constructing a microbial network. Therefore, identifying competitive and cooperative relationships between microbes is profound importance however, the directional nature of such interactions also poses as a difficult challenge in network construction. Recent studies have also shown that competitive interactions can drive the evolution of cooperation in microbial ecosystems. Competition and cooperation are the two most studied microbial interactions in the recent times with the former dominating the latter in various microbial communities. Įlucidating competitive and cooperative relationship is a challenge in generating a microbial interaction network because of the direction of such interactions. Metagenomic studies and network-based approaches have yield detailed information on the composition of microbial communities, which in turn pave the way to study the structure of microbial ecosystems and their dynamics. However, recent advances in high-throughput sequencing technology have made large scale surveys of microbial communities feasible. Microbial interactions, including mutualism, competition, parasitism and commensalism, are difficult to quantify as the underlying processes usually cannot be observed directly and are often too complex for laboratory experiments. Microbes are the most abundant and diverse organisms on earth and their interactions are crucial in understanding both the ecology and the evolution of microorganisms. The RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial 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 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. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. Microbial interactions are ubiquitous in nature.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |