Beardsley Thomas, Behringer Megan, Komarova Natalia L
Department of Mathematics, University of California Irvine, Irvine, California, USA.
Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA.
Stud Appl Math. 2025 Feb;154(2). doi: 10.1111/sapm.70009. Epub 2025 Feb 9.
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.
微生物群落是随时间演化的复杂生物生态系统,新的变体不断产生,而其他变体则消失。了解物种在群落中的相互作用方式有助于我们深入了解驱动生态系统过程的机制。我们研究了连续传代系统,即通过定期抽取子样本并将其接种到新鲜培养基中来维持群落存活。通常收集的数据包括在几个时间点上每个物种的种群百分比。为了利用这类数据,我们构建了一个方程组(基于广义Lotka-Volterra模型)并推导了物种非相互作用的条件。通过将该问题重新表述为寻找可行域的问题,这一点得以实现,而可行域问题可以通过多种高效算法来解决。这种方法为研究微生物群落中的相互作用提供了一种经济高效的途径。