Abstract
The activity of a biological community is the outcome of complex processes involving interactions between community members. It is often unclear how to accurately incorporate these interactions into predictive models. Previous work has shown a range of positive and negative metabolic pairwise interactions between species. Here we examine the ability of a modified general Lotka-Volterra model with cell-cell interaction coefficients to predict the overall metabolic rate of a well-mixed microbial community comprised of four heterotrophic natural isolates, experimentally quantifying the strengths of two, three, and four-species interactions. Within this community, interactions between any pair of microbial species were positive, while higher-order interactions, between 3 or more microbial species, slightly modulated community metabolism. For this simple community, the metabolic rate of can be well predicted only with taking into account pairwise interactions. Simulations using the experimentally determined interaction parameters revealed that spatial heterogeneity in the distribution of cells increased the importance of multispecies interactions in dictating function at both the local and global scales.
Highlights
We are surrounded by complex communities of microbes, many that play a fundamental role in our everyday lives
Many wild microbial ecosystems contain hundreds to thousands of species, suggesting that interactions between species likely play an important role in regulating the behavior of such complex cellular networks
We quantify the contribution of interactions between more than two species to the overall metabolic rate of a mixture of four freshwater bacteria
Summary
We are surrounded by complex communities of microbes, many that play a fundamental role in our everyday lives. Microbial ecosystems in nature are typically composed of hundreds or thousands of microbial species, heterogeneously distributed in space and time. Working together, these networks of microorganisms are critical in environmental remediation, food production, wastewater treatment, and human health and disease and there is great interest designing synthetic microbial ecosystems for new biotechnologies. Understanding the properties of the community interaction network will help identify strategies to study and manipulate microbial networks. Developing a quantitative understanding of how ecosystems of microbes interact will be essential to predicting how microbial networks respond to environmental or biological changes and aid in designing synthetic communities with tailored functionality [3]
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