Jose Manuel Martí(1,2,✳️), Daniel M. Martínez(1,2,3,✳️), Manuel Peña(2), César Gracia(1,2), Amparo Latorre(1,3,4,5), Andrés Moya(1,3,4,5,:email:) & Carlos P. Garay(1,2,:email:)
1 Institute for Integrative Systems Biology (I2SysBio), 46980, Spain.
2 Instituto de Física Corpuscular, CSIC-UVEG, P.O. 22085, 46071, Valencia, Spain.
3 FISABIO, Avda de Catalunya, 21, 46020, Valencia, Spain.
4 Cavanilles Institute of Biodiversity and Evolutionary Biology, Univ. de Valencia, 46980, Spain.
5 CIBER en Epidemiología y Salud Pública (CIBEResp), Madrid, Spain.
✳️ : Equally contributed
📧 : Correponding authors
Animal microbiota (human included) plays an important role keeping healthy the physiological status of the host. Increasing research activity is dedicated to understand how changes in composition and function of the microbiota are associated to disease or not. We analyze 16S rRNA and whole genome sequencing (WGS) published data from the gut microbiota of 97 individuals monitored in time. Temporal fluctuations in the microbial composition reveal significant differences due to factors such us dietary changes, antibiotic intake, age or disease. Here we show that a fluctuation scaling law describes the temporal changes in the gut microbiota. This law allows to estimate the temporal variability of the microbial population and quantitatively characterizes the path toward disease by a noise-induced phase transition. The estimation of the systemic parameters for follow-up studies may have clinical use and, more generally, applications in other fields where it is important to know if a given community is stable or not.
Human microbiota is tightly associated to the health status of a person. Here we analyse the microbial composition of several subjects under different conditions, over a time span that ranges from days to months. Using the Langevin equation as the basis of our mathematical framework in order to evaluate microbial temporal stability, we prove that we are capable to distinguish stable from unstable microbiotas. This first step will help us to determine how microbiota temporal stability is related to the healthiness of the people, and it will allow the development of a more complete framework in order to deepen the knowledge of this complex system.