Myxobacteria

Hartzell, Trish

doi:10.1002/9780470015902.a0020391.pub2

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…The amoebae Acanthamoeba pyriformis and Luapelamoeba arachisporum form minute sporocarpic fruiting bodies [20]. Moreover, prokaryotes including Myxococcus xanthus and B. subtilis among others have been proposed to form similar fruiting bodies [21,22]. Motile cells attach to one another and form aerial structures that are the hub for sporulation.…”

Section: Introductionmentioning

confidence: 99%

Modelling population dynamics in a unicellular social organism community using a minimal model and evolutionary game theory

et al. 2020

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Most unicellular organisms live in communities and express different phenotypes. Many efforts have been made to study the population dynamics of such complex communities of cells, coexisting as well-coordinated units. Minimal models based on ordinary differential equations are powerful tools that can help us understand complex phenomena. They represent an appropriate compromise between complexity and tractability; they allow a profound and comprehensive analysis, which is still easy to understand. Evolutionary game theory is another powerful tool that can help us understand the costs and benefits of the decision a particular cell of a unicellular social organism takes when faced with the challenges of the biotic and abiotic environment. This work is a binocular view at the population dynamics of such a community through the objectives of minimal modelling and evolutionary game theory. We test the behaviour of the community of a unicellular social organism at three levels of antibiotic stress. Even in the absence of the antibiotic, spikes in the fraction of resistant cells can be observed indicating the importance of bet hedging. At moderate level of antibiotic stress, we witness cyclic dynamics reminiscent of the renowned rock–paper–scissors game. At a very high level, the resistant type of strategy is the most favourable.

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Section: Introductionmentioning

confidence: 99%

Modelling population dynamics in a unicellular social organism community using a minimal model and evolutionary game theory

et al. 2020

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“…Multicellular self-organization is important for biological processes across all kingdoms of life [1][2][3][4][5]. The development of a complicated living system requires many iterative self-organizing steps, from the creation of tissues and organs, to organisms and interacting communities.…”

Section: Introductionmentioning

confidence: 99%

Quantification ofMyxococcus xanthusAggregation and Rippling Behaviors: Deep-learning Transformation of Phase-contrast into Fluorescence Microscopy Images

Zhang

Comstock

Cotter

et al. 2021

Preprint

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Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates. However, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to the cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into fluorescent images. By including an additional histogram-equalized output to the state-of-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies.

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“…Multicellular self-organization is important for biological processes across all kingdoms of life [ 1 , 2 , 3 , 4 , 5 ]. The development of a complicated living system requires many iterative self-organizing steps, from the creation of tissues and organs, to organisms and interacting communities.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-Learning Transformation of Phase-Contrast into Fluorescence Microscopy Images

et al. 2021

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Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well-correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates; however, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into synthesized fluorescent images. By including an additional histogram-equalized output to the state-of-the-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies.

show abstract

Myxobacteria

Cited by 4 publications

References 48 publications

Modelling population dynamics in a unicellular social organism community using a minimal model and evolutionary game theory

Modelling population dynamics in a unicellular social organism community using a minimal model and evolutionary game theory

Quantification ofMyxococcus xanthusAggregation and Rippling Behaviors: Deep-learning Transformation of Phase-contrast into Fluorescence Microscopy Images

Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-Learning Transformation of Phase-Contrast into Fluorescence Microscopy Images

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