Microbial interactions: from networks to models

By Karoline Faust, Jeroen Raes. Nature Reviews Microbiology, 2012

3,546 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b9357c45f1f684ffa023f7e8ad32c1ac1ca70747

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Citations in the corpus

Soil Bacterial and Fungal Response to Wildfires in the Canadian Boreal Forest Across a Burn Severity Gradient (2019, methodology): “For the purposes of this paper, we interpret co-occurrences or modules as generally representing shared niches rather than necessarily representing direct interactions between organisms (Faust and Raes, 2012; Montoya et al., 2015).”
Sparse and Compositionally Robust Inference of Microbial Ecological Networks (2014, methodology): “Recent methods such as Sparse Correlations for Compositional data (SparCC) [17] and Compositionally Corrected by REnormalization and PErmutation (CCREPE) [9, 11, 19] are designed to account for these compositional biases and represent the state of the art in the field.”
The responses and adaptations of microbial communities to salinity in farmland soils: a molecular ecological network analysis. (2017, background): “In the network, positive links could be attributed to niche overlap and cross-feeding, while negative relationships could be attributed to competition and amensalism (Faust and Raes, 2012).”
Extracellular enzyme kinetics scale with resource availability (2014, background): “…the spa- tial distribution and metabolism of individual cells and populations to maximize community growth through mechanisms such as quorum sensing, substrate chan- neling and syntrophy (Faust and Raes 2012; Huang et al. 2013; Elias and Banin 2012; Strickland et al. 2013; Zhuang et al. 2013).”
Core microbiomes for sustainable agroecosystems (2018, background): “Network theory provides a ‘bird’s-eye’ view of potential microorganism–microorganism interactions 49,60,61 , by allowing us to understand which species are most likely to mediate interactions among many others within microbiomes 17,29,62–64 .”
Disentangling Interactions in the Microbiome: A Network Perspective (2016, methodology): “Some of the methods, such as correlation-based methods, are quite popular due to their speed and ease of use [18], while others, such as probabilistic graphical models, have not been used extensively to address biological questions, but have seen good success in other fields and gained a reputation ”
Network analysis methods for studying microbial communities: A mini review (2021, methodology): “Apart from these classical approaches, more complex methods have been proposed based on probabilistic graphical, Gaussian graphical and complex multiple regression models to construct microbial interaction networks [6,21].”