Title:Modeling Microbial Community Networks: Methods and Tools
Volume: 22
Issue: 4
Author(s): Marco Cappellato, Giacomo Baruzzo, Ilaria Patuzzi and Barbara Di Camillo*
Affiliation:
- Department of Information Engineering, University of Padova, Padova,Italy
Keywords:
Microbiota, microbiota analysis, microbial interactions, network inference, relationship models, synthetic count data.
Abstract:
In the current research landscape, microbiota composition studies are of extreme interest,
since it has been widely shown that resident microorganisms affect and shape the ecological niche
they inhabit. This complex micro-world is characterized by different types of interactions. Understanding
these relationships provides a useful tool for decoding the causes and effects of communities’
organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition
of the whole microbial community present in a sample. Sequencing data can then be investigated
through statistical and computational method coming from network theory to infer the network of interactions
among microbial species.
Since there are several network inference approaches in the literature, in this paper we tried to shed
light on their main characteristics and challenges, providing a useful tool not only to those interested
in using the methods, but also to those who want to develop new ones. In addition, we focused on the
frameworks used to produce synthetic data, starting from the simulation of network structures up to
their integration with abundance models, with the aim of clarifying the key points of the entire generative
process.
