Abstract
BackgroundMicrobial Interaction Networks (MINs) provide important information for understanding bacterial communities. MINs can be inferred by examining microbial abundance profiles. Abundance profiles are often interpreted with the Lotka Volterra model in research. However existing research fails to consider a biologically meaningful underlying mathematical model for MINs or to address the possibility of multiple solutions.ResultsIn this paper we present IMPARO, a method for inferring microbial interactions through parameter optimisation. We use biologically meaningful models for both the abundance profile, as well as the MIN. We show how multiple MINs could be inferred with similar reconstructed abundance profile accuracy, and argue that a unique solution is not always satisfactory. Using our method, we successfully inferred clear interactions in the gut microbiome which have been previously observed in in-vitro experiments.ConclusionsIMPARO was used to successfully infer microbial interactions in human microbiome samples as well as in a varied set of simulated data. The work also highlights the importance of considering multiple solutions for MINs.
Highlights
Microbial Interaction Networks (MINs) provide important information for understanding bacterial communities
Heterogeneity and sparsity were varied—through varying α and p respectively—to investigate how Inferring microbial interactions through parameter optimisation (IMPARO) responded to microbial samples of varying nature
Existence of multiple solutions As we have mentioned in the literature review, it is possible to find multiple solutions for the problem of inferring microbial interactions when the accuracy is measured through reconstructed abundance profiles
Summary
Microbial Interaction Networks (MINs) provide important information for understanding bacterial communities. MINs can be inferred by examining microbial abundance profiles. Microbes are the most abundant, widespread organisms on Earth. They can be found in the biosphere, including all animals and plants, and most habitats in the oceans [1, 2], on land, or in air. In the past, studying microbial communities through cultivation in laboratories was challenging [13]. As over 99% [14, 15] of microbial species on earth are yet to be identified, the inability to cultivate and separate some microbial species in a laboratory environment have hindered progress on the study of microbiota
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