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Social dynamics modeling of chrono-nutrition

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Social dynamics modeling of chrono-nutrition. / Stefano, Alessandro Di; Scatà, Marialisa; Vijayakumar, Supreeta et al.
In: PLoS Computational Biology, Vol. 15, No. 1, e1006714, 30.01.2019, p. 1-25.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Stefano, AD, Scatà, M, Vijayakumar, S, Angione, C, Corte, AL & Liò, P 2019, 'Social dynamics modeling of chrono-nutrition', PLoS Computational Biology, vol. 15, no. 1, e1006714, pp. 1-25. https://doi.org/10.1371/journal.pcbi.1006714

APA

Stefano, A. D., Scatà, M., Vijayakumar, S., Angione, C., Corte, A. L., & Liò, P. (2019). Social dynamics modeling of chrono-nutrition. PLoS Computational Biology, 15(1), 1-25. Article e1006714. https://doi.org/10.1371/journal.pcbi.1006714

Vancouver

Stefano AD, Scatà M, Vijayakumar S, Angione C, Corte AL, Liò P. Social dynamics modeling of chrono-nutrition. PLoS Computational Biology. 2019 Jan 30;15(1):1-25. e1006714. doi: 10.1371/journal.pcbi.1006714

Author

Stefano, Alessandro Di ; Scatà, Marialisa ; Vijayakumar, Supreeta et al. / Social dynamics modeling of chrono-nutrition. In: PLoS Computational Biology. 2019 ; Vol. 15, No. 1. pp. 1-25.

Bibtex

@article{c0e806faf69241b5b6ab404981635d91,
title = "Social dynamics modeling of chrono-nutrition",
abstract = "Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the “gut-human behavior axis” and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and “chrono-nutrition” play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between theireating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeuticdietary interventions.",
author = "Stefano, {Alessandro Di} and Marialisa Scat{\`a} and Supreeta Vijayakumar and Claudio Angione and Corte, {Aurelio La} and Pietro Li{\`o}",
year = "2019",
month = jan,
day = "30",
doi = "10.1371/journal.pcbi.1006714",
language = "English",
volume = "15",
pages = "1--25",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "1",

}

RIS

TY - JOUR

T1 - Social dynamics modeling of chrono-nutrition

AU - Stefano, Alessandro Di

AU - Scatà, Marialisa

AU - Vijayakumar, Supreeta

AU - Angione, Claudio

AU - Corte, Aurelio La

AU - Liò, Pietro

PY - 2019/1/30

Y1 - 2019/1/30

N2 - Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the “gut-human behavior axis” and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and “chrono-nutrition” play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between theireating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeuticdietary interventions.

AB - Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the “gut-human behavior axis” and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and “chrono-nutrition” play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between theireating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeuticdietary interventions.

U2 - 10.1371/journal.pcbi.1006714

DO - 10.1371/journal.pcbi.1006714

M3 - Journal article

VL - 15

SP - 1

EP - 25

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 1

M1 - e1006714

ER -