Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka–Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.
Microbes form complex communities that perform critical roles for the integrity of their environment or the well-being of their hosts. Controlling these microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. However, the lack of an efficient and systematic control framework has limited our ability to manipulate these microbial communities. Here we fill this gap by developing a control framework based on the new notion of structural accessibility. Our framework uses the ecological network of the community to identify minimum sets of its driver species, manipulation of which allows controlling the whole community. We numerically validate our control framework on large communities, and then we demonstrate its application for controlling the gut microbiota of gnotobiotic mice infected with Clostridium difficile and the core microbiota of the sea sponge Ircinia oros . Our results provide a systematic pipeline to efficiently drive complex microbial communities towards desired states.
Microbes form complex and dynamic ecosystems that play key roles in the health of the animals and plants with which they are associated. Such ecosystems are often represented by a directed, signed and weighted ecological network, where nodes represent microbial taxa and edges represent ecological interactions. Inferring the underlying ecological networks of microbial communities is a necessary step towards understanding their assembly rules and predicting their dynamical response to external stimuli. However, current methods for inferring such networks require assuming a particular population dynamics model, which is typically not known a priori. Moreover, those methods require fitting longitudinal abundance data, which is not readily available, and often does not contain the variation that is necessary for reliable inference. To overcome these limitations, here we develop a new method to map the ecological networks of microbial communities using steady-state data. Our method can qualitatively infer the inter-taxa interaction types or signs (positive, negative or neutral) not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/150649 doi: bioRxiv preprint first posted online Jun. 15, 2017; 2 without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can quantitatively infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental datasets of microbial communities. Our method offers a novel framework to infer microbial interactions and reconstruct ecological networks, and represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.
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