IMPARO: inferring microbial interactions through parameter optimisation
- PDF / 1,728,658 Bytes
- 11 Pages / 595 x 791 pts Page_size
- 77 Downloads / 270 Views
BMC Molecular and Cell Biology
RESEARCH
Open Access
IMPARO: inferring microbial interactions through parameter optimisation Rajith Vidanaarachchi1* , Marnie Shaw1 , Sen-Lin Tang2 and Saman Halgamuge1,3 From International Conference on Bioinformatics (InCoB 2019) Jakarta, Indonesia. 10–12 September 2019
Abstract Background: Microbial 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. Results: In 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. Conclusions: IMPARO 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. Keywords: Metagenomics, Inferring interactions, Network dynamics, Microbial interaction network
Background 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. Many studies show that microbes play a important role in the health and well-being of the hosts they are associated with. For example, in the human body, imbalances or changes in microbial communities correlates to various illnesses and other complications [3–9]. In plants, microbes provide essential nutrients, including all economic crops [10–12].
*Correspondence: [email protected] Research School of Electrical, Energy and Materials Engineering, College of Engineering & Computer Science, Australian National University, 2601 Acton, Australia Full list of author information is available at the end of the article 1
In the past, studying microbial communities through cultivation in laboratories was challenging [13]. Also, 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. Due to recent advances in 16S rRNA sequencing and high throughput sequencing, though, scientists can now explore the nature of real-world microbial samples and recognise individual species in these samples. 16S ribosomal RNA has been used by many scientists in order to identify, categorise and classify microbes. Microbia
Data Loading...
