scholarly journals Disentangling direct from indirect relationships in association networks

2022 ◽  
Vol 119 (2) ◽  
pp. e2109995119
Author(s):  
Naijia Xiao ◽  
Aifen Zhou ◽  
Megan L. Kempher ◽  
Benjamin Y. Zhou ◽  
Zhou Jason Shi ◽  
...  

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal (P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

2020 ◽  
Vol 21 (11) ◽  
pp. 1054-1059
Author(s):  
Bin Yang ◽  
Yuehui Chen

: Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.


mSystems ◽  
2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Bin Ma ◽  
Zhongmin Dai ◽  
Haizhen Wang ◽  
Melissa Dsouza ◽  
Xingmei Liu ◽  
...  

ABSTRACT Understanding biogeographic patterns is a precursor to improving our knowledge of the function of microbiomes and to predicting ecosystem responses to environmental change. Using natural forest soil samples from 110 locations, this study is one of the largest attempts to comprehensively understand the different patterns of soil archaeal, bacterial, and fungal biogeography at the continental scale in eastern China. These patterns in natural forest sites could ascertain reliable soil microbial biogeographic patterns by eliminating anthropogenic influences. This information provides guidelines for monitoring the belowground ecosystem’s decline and restoration. Meanwhile, the deviations in the soil microbial communities from corresponding natural forest states indicate the extent of degradation of the soil ecosystem. Moreover, given the association between vegetation type and the microbial community, this information could be used to predict the long-term response of the underground ecosystem to the vegetation distribution caused by global climate change. The natural forest ecosystem in Eastern China, from tropical forest to boreal forest, has declined due to cropland development during the last 300 years, yet little is known about the historical biogeographic patterns and driving processes for the major domains of microorganisms along this continental-scale natural vegetation gradient. We predicted the biogeographic patterns of soil archaeal, bacterial, and fungal communities across 110 natural forest sites along a transect across four vegetation zones in Eastern China. The distance decay relationships demonstrated the distinct biogeographic patterns of archaeal, bacterial, and fungal communities. While historical processes mainly influenced bacterial community variations, spatially autocorrelated environmental variables mainly influenced the fungal community. Archaea did not display a distance decay pattern along the vegetation gradient. Bacterial community diversity and structure were correlated with the ratio of acid oxalate-soluble Fe to free Fe oxides (Feo/Fed ratio). Fungal community diversity and structure were influenced by dissolved organic carbon (DOC) and free aluminum (Ald), respectively. The role of these environmental variables was confirmed by the correlations between dominant operational taxonomic units (OTUs) and edaphic variables. However, most of the dominant OTUs were not correlated with the major driving variables for the entire communities. These results demonstrate that soil archaea, bacteria, and fungi have different biogeographic patterns and driving processes along this continental-scale natural vegetation gradient, implying different community assembly mechanisms and ecological functions for archaea, bacteria, and fungi in soil ecosystems. IMPORTANCE Understanding biogeographic patterns is a precursor to improving our knowledge of the function of microbiomes and to predicting ecosystem responses to environmental change. Using natural forest soil samples from 110 locations, this study is one of the largest attempts to comprehensively understand the different patterns of soil archaeal, bacterial, and fungal biogeography at the continental scale in eastern China. These patterns in natural forest sites could ascertain reliable soil microbial biogeographic patterns by eliminating anthropogenic influences. This information provides guidelines for monitoring the belowground ecosystem’s decline and restoration. Meanwhile, the deviations in the soil microbial communities from corresponding natural forest states indicate the extent of degradation of the soil ecosystem. Moreover, given the association between vegetation type and the microbial community, this information could be used to predict the long-term response of the underground ecosystem to the vegetation distribution caused by global climate change. Author Video: An author video summary of this article is available.


2021 ◽  
Vol 97 (4) ◽  
Author(s):  
Lucas Dantas Lopes ◽  
Jingjie Hao ◽  
Daniel P Schachtman

ABSTRACT Soil pH is a major factor shaping bulk soil microbial communities. However, it is unclear whether the belowground microbial habitats shaped by plants (e.g. rhizosphere and root endosphere) are also affected by soil pH. We investigated this question by comparing the microbial communities associated with plants growing in neutral and strongly alkaline soils in the Sandhills, which is the largest sand dune complex in the northern hemisphere. Bulk soil, rhizosphere and root endosphere DNA were extracted from multiple plant species and analyzed using 16S rRNA amplicon sequencing. Results showed that rhizosphere, root endosphere and bulk soil microbiomes were different in the contrasting soil pH ranges. The strongest impact of plant species on the belowground microbiomes was in alkaline soils, suggesting a greater selective effect under alkali stress. Evaluation of soil chemical components showed that in addition to soil pH, cation exchange capacity also had a strong impact on shaping bulk soil microbial communities. This study extends our knowledge regarding the importance of pH to microbial ecology showing that root endosphere and rhizosphere microbial communities were also influenced by this soil component, and highlights the important role that plants play particularly in shaping the belowground microbiomes in alkaline soils.


2021 ◽  
Vol 773 ◽  
pp. 145640
Author(s):  
Lili Rong ◽  
Longfei Zhao ◽  
Leicheng Zhao ◽  
Zhipeng Cheng ◽  
Yiming Yao ◽  
...  

Ecosystems ◽  
2021 ◽  
Author(s):  
Susana Rodríguez-Echeverría ◽  
Manuel Delgado-Baquerizo ◽  
José A. Morillo ◽  
Aurora Gaxiola ◽  
Marlene Manzano ◽  
...  

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