Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions

  1. Babak Momeni  Is a corresponding author
  2. Li Xie
  3. Wenying Shou  Is a corresponding author
  1. Boston College, United States
  2. Fred Hutchinson Cancer Research Center, United States

Abstract

Pairwise models are commonly used to describe many-species communities. In these models, an individual receives additive fitness effects from pairwise interactions with each species in the community ('additivity assumption'). All pairwise interactions are typically represented by a single equation where parameters reflect signs and strengths of fitness effects ('universality assumption'). Here, we show that a single equation fails to qualitatively capture diverse pairwise microbial interactions. We build mechanistic reference models for two microbial species engaging in commonly-found chemical-mediated interactions, and attempt to derive pairwise models. Different equations are appropriate depending on whether a mediator is consumable or reusable, whether an interaction is mediated by one or more mediators, and sometimes even on quantitative details of the community (e.g. relative fitness of the two species, initial conditions). Our results, combined with potential violation of the additivity assumption in many-species communities, suggest that pairwise modeling will often fail to predict microbial dynamics.

Article and author information

Author details

  1. Babak Momeni

    Department of Biology, Boston College, Chestnut Hill, United States
    For correspondence
    momeni@bc.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1271-5196
  2. Li Xie

    Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    No competing interests declared.
  3. Wenying Shou

    Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    For correspondence
    wshou@fredhutch.org
    Competing interests
    Wenying Shou, Reviewing editor, eLife.

Funding

Boston College

  • Babak Momeni

NIH Office of the Director

  • Babak Momeni
  • Li Xie

W. M. Keck Foundation

  • Babak Momeni

Fred Hutchinson Cancer Research Center

  • Li Xie

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Bruce Levin

Version history

  1. Received: January 11, 2017
  2. Accepted: March 18, 2017
  3. Accepted Manuscript published: March 28, 2017 (version 1)
  4. Accepted Manuscript updated: April 3, 2017 (version 2)
  5. Version of Record published: June 13, 2017 (version 3)

Copyright

© 2017, Momeni et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 6,767
    views
  • 1,411
    downloads
  • 207
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Babak Momeni
  2. Li Xie
  3. Wenying Shou
(2017)
Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions
eLife 6:e25051.
https://doi.org/10.7554/eLife.25051

Share this article

https://doi.org/10.7554/eLife.25051

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Ardalan Naseri, Degui Zhi, Shaojie Zhang
    Research Article

    Runs of homozygosity (ROH) segments, contiguous homozygous regions in a genome were traditionally linked to families and inbred populations. However, a growing literature suggests that ROHs are ubiquitous in outbred populations. Still, most existing genetic studies of ROH in populations are limited to aggregated ROH content across the genome, which does not offer the resolution for mapping causal loci. This limitation is mainly due to a lack of methods for the efficient identification of shared ROH diplotypes. Here, we present a new method, ROH-DICE, to find large ROH diplotype clusters, sufficiently long ROHs shared by a sufficient number of individuals, in large cohorts. ROH-DICE identified over 1 million ROH diplotypes that span over 100 SNPs and are shared by more than 100 UK Biobank participants. Moreover, we found significant associations of clustered ROH diplotypes across the genome with various self-reported diseases, with the strongest associations found between the extended HLA region and autoimmune disorders. We found an association between a diplotype covering the HFE gene and hemochromatosis, even though the well-known causal SNP was not directly genotyped or imputed. Using a genome-wide scan, we identified a putative association between carriers of an ROH diplotype in chromosome 4 and an increase in mortality among COVID-19 patients (P-value=1.82×10-11). In summary, our ROH-DICE method, by calling out large ROH diplotypes in a large outbred population, enables further population genetics into the demographic history of large populations. More importantly, our method enables a new genome-wide mapping approach for finding disease-causing loci with multi-marker recessive effects at a population scale.

    1. Computational and Systems Biology
    Iván Plaza-Menacho
    Insight

    A study of two enzymes in the brain reveals new insights into how redox reactions regulate the activity of protein kinases.