Detecting (the Absence of) Species Interactions in Microbial Ecological Systems

Microbial communities are complex ecological systems of organisms that evolve in time, with new variants created, while others disappear. Understanding how species interact within communities can help us shed light into the mechanisms that drive ecosystem processes. We studied systems with serial propagation, where the community is kept alive by taking a subsample at regular intervals and replating it in fresh medium. The data that are usually collected consist of the % of the population for each of the species, at several time points. In order to utilize this type of data, we formulated a system of equations (based on the generalized Lotka-Volterra model) and derived conditions of species noninteraction. This was possible to achieve by reformulating the problem as a problem of finding feasibility domains, which can be solved by a number of efficient algorithms. This methodology provides a cost-effective way to investigate interactions in microbial communities.

Authors

Institutions

Publication Details

Journal
Studies in Applied Mathematics
Published
2025-02-01
DOI
https://doi.org/10.1111/sapm.70009
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detecting (the Absence of) Species Interactions in Microbial Ecological Systems

Megan G. Behringer, Natalia L. Komarova, Thomas Beardsley
1 links
Studies in Applied Mathematics
Evolutionary Game Theory and Cooperation
article

Detecting (the Absence of) Species Interactions in Microbial Ecological Systems

Megan G. Behringer, Natalia L. Komarova, Thomas Beardsley
article en

Abstract

Microbial communities are complex ecological systems of organisms that evolve in time, with new variants created, while others disappear. Understanding how species interact within communities can help us shed light into the mechanisms that drive ecosystem processes. We studied systems with serial propagation, where the community is kept alive by taking a subsample at regular intervals and replating it in fresh medium. The data that are usually collected consist of the % of the population for each of the species, at several time points. In order to utilize this type of data, we formulated a system of equations (based on the generalized Lotka-Volterra model) and derived conditions of species noninteraction. This was possible to achieve by reformulating the problem as a problem of finding feasibility domains, which can be solved by a number of efficient algorithms. This methodology provides a cost-effective way to investigate interactions in microbial communities.

Studies in Applied MathematicsVol. 154(2)
Vanderbilt University (US), University of California, Irvine (US), University of California San Diego (US)
Openalex Percentile: Top 97%
Evolutionary Game Theory and Cooperation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.