A polyphasic approach was used in which genotypic and phenotypic properties of a gram-negative, obligately anaerobic, rod-shaped bacterium isolated from a black anoxic freshwater mud sample were determined. Based on these results, the name Holophaga foetida gen. nov., sp. nov. is proposed. This microorganism produced dimethylsulfide and methanethiol during growth on trimethoxybenzoate or syringate. The only other compounds utilized were pyruvate and trihydroxybenzenes such as gallate, phloroglucinol, or pyrogallol. The aromatic compounds were degraded to acetate. Although comparison of the signature nucleotide pattern of the five established subclasses of Proteobacteria with the 16S rDNA sequence of Holophaga foetida revealed a relationship to members of the delta-subclass, the phylogenetic position within the radiation of this class is so deep and dependent upon the number and selection of reference sequences that its affiliation to the Proteobacteria must be considered tentative. The type strain is H. foetida strain TMBS4 (DSM 6591).

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With increasing levels of antimicrobial resistance impacting both human and animal health, novel means of treating resistant infections are urgently needed. Bacteriophages and predatory bacteria such as Bdellovibrio bacteriovorus have been proposed as suitable candidates for this role. Microbes also play a key environmental role as producers or recyclers of nutrients such as carbon and nitrogen, and predators have the capacity to be keystone species within microbial communities. To date, many studies have looked at the mechanisms of action of prokaryotic predators, their safety in in vivo models and their role and effectiveness under specific conditions. Mathematical models however allow researchers to investigate a wider range of scenarios, including aspects of predation that would be difficult, expensive, or time-consuming to investigate experimentally. We review here a history of modelling in prokaryote predation, from simple Lotka-Volterra models, through increasing levels of complexity, including multiple prey and predator species, and environmental and spatial factors. We consider how models have helped address questions around the mechanisms of action of predators and have allowed researchers to make predictions of the dynamics of predator–prey systems. We examine what models can tell us about qualitative and quantitative commonalities or differences between bacterial predators and bacteriophage or protists. We also highlight how models can address real-world situations such as the likely effectiveness of predators in removing prey species and their potential effects in shaping ecosystems. Finally, we look at research questions that are still to be addressed where models could be of benefit.

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Aging can be defined as an accumulation of damage, or a loss of function, with increasing age. For bacteria, it has generally been assumed that the mechanism used to cope with aging is the asymmetric segregation of damage at division, so that all of the damage is inherited by one cell and the other is therefore rejuvenated. Another, often neglected, mechanism is to repair the damage; our previous computational modelling work has found that an optimized, fixed rate of repair is fitter than damage segregation in well-mixed environments such as chemostats. The predominant mode of growth for bacteria is in biofilms, however, and here we investigate aging in biofilms using the individual-based model iDynoMiCS. In addition to the previously used damage segregation and fixed repair strategies, we introduced adaptive repair: sensing the current damage levels within the cell and responding to this by investing in damage repair machinery. We found that the optimal method for dealing with cellular damage varies with the environment being investigated. The investment of additional resources into adaptive repair is only beneficial when competition is sufficiently strong, in the chemostat and in biofilms, and the speed at which the fittest strategy becomes apparent depends upon the initial density of cells. When the bacterial cells are dense initially, and thus the competition between strategies is stronger, the adaptive repair strategy emerges as the winner much more rapidly.