Origins of Scaling Laws in Microbial Dynamics

Abstract

Analysis of high-resolution time series data from the human and mouse gut microbiomes revealed that the gut microbial dynamics can be characterized by several robust and simple scaling laws. It is still unknown if those scaling laws are universal across different body sites, host species, or even free-living microbial communities. Moreover, the underlying mechanisms responsible for those scaling laws remain poorly understood. Here, we demonstrate that those scaling laws are not unique to gut microbiome, but universal across different habitats, from human skin and oral microbiome to marine plankton bacteria and eukarya communities. Since completely shuffled time series yield very similar scaling laws, we conjecture that the universal scaling laws in various microbiomes are largely driven by temporal stochasticity of the host or environmental factors. We leverage a simple population dynamics model with both deterministic inter-species interactions and stochastic noise to confirm our conjecture. In particular, we find that those scaling laws are jointly determined by inter-species interactions and linear multiplicative noises. The presented results deepen our understanding of the nature of scaling laws in microbial dynamics.

Competition for fluctuating resources reproduces statistics of species abundance over time across wide-ranging microbiotas

“…While our CR model assumes pairwise interactions between consumers and resources, the effective interactions between consumers are not necessarily pairwise. To explore whether these higher-order contributions are necessary for recapitulating the data, we considered models explicitly based on pairwise interspecies interactions, which despite differences compared with CR models ( Momeni et al, 2017 ) can also reproduce some properties of experimental time series ( Descheemaeker and de Buyl, 2020 ; Wang and Liu, 2021b ). To further explore the properties of models focused on pairwise interactions, we investigated gLV models in which taxa grow and interact via …”