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Filamentous phages: masters of a microbial sharing economy.

ABSTRACT: Bacteriophage ("bacteria eaters") or phage is the collective term for viruses that infect bacteria. While most phages are pathogens that kill their bacterial hosts, the filamentous phages of the sub-class Inoviridae live in cooperative relationships with their bacterial hosts, akin to the principal behaviours found in the modern-day sharing economy: peer-to-peer support, to offset any burden. Filamentous phages impose very little burden on bacteria and offset this by providing service to help build better biofilms, or provision of toxins and other factors that increase virulence, or modified behaviours that provide novel motile activity to their bacterial hosts. Past, present and future biotechnology applications have been built on this phage-host cooperativity, including DNA sequencing technology, tools for genetic engineering and molecular analysis of gene expression and protein production, and phage-display technologies for screening protein-ligand and protein-protein interactions. With the explosion of genome and metagenome sequencing surveys around the world, we are coming to realize that our knowledge of filamentous phage diversity remains at a tip-of-the-iceberg stage, promising that new biology and biotechnology are soon to come.

SUBMITTER: Hay ID

PROVIDER: S-EPMC6549030 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Project description:This work aims to provide a reliable and intelligent prediction model for future trends in sharing economy. Moreover, it presents valuable insights for decision-making and policy development by relevant governmental bodies. Furthermore, the study introduces a predictive system that incorporates an enhanced Harris Hawk Optimization (HHO) algorithm and a K-Nearest Neighbor (KNN) forecasting framework. The method utilizes an improved simulated annealing mechanism and a Gaussian bare bone structure to improve the original HHO, termed SGHHO. To achieve optimal prediction performance and identify essential features, a refined simulated annealing mechanism is employed to mitigate the susceptibility of the original HHO algorithm to local optima. The algorithm employs a mechanism that boosts its global search ability by generating fresh solution sets at a specific likelihood. This mechanism dynamically adjusts the equilibrium between the exploration and exploitation phases, incorporating the Gaussian bare bone strategy. The best classification model (SGHHO-KNN) is developed to mine the key features with the improvement of both strategies. To assess the exceptional efficacy of the SGHHO algorithm, this investigation conducted a series of comparative trials employing the function set of IEEE CEC 2014. The outcomes of these experiments unequivocally demonstrate that the SGHHO algorithm outperforms the original HHO algorithm on 96.7% of the functions, substantiating its remarkable superiority. The algorithm can achieve the optimal value of the function on 67% of the tested functions and significantly outperforms other competing algorithms. In addition, the key features selected by the SGHHO-KNN model in the prediction experiment, including " Form of sharing economy in your region " and " Attitudes to the sharing economy ", are important for predicting the future trends of the sharing economy in this study. The results of the prediction demonstrate that the proposed model achieves an accuracy rate of 99.70% and a specificity rate of 99.38%. Consequently, the SGHHO-KNN model holds great potential as a reliable tool for forecasting the forthcoming trajectory of the sharing economy.

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Filamentous phages: masters of a microbial sharing economy.

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