Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes

Abstract

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, due to practical limitations, the most densely sampled time series from the human gut are collected at a frequency of about once per day, while the population doubling times for gut commensals are on the order of minutes-to-hours. Despite this, much of the prior work on human gut microbiome time series modeling has, implicitly or explicitly, assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a continuous flow ecosystem at a dynamical steady state, where population dynamics occur internally and the bacterial population sizes measured in stool represent an endpoint of these internal dynamics. We formalize this idea as stochastic logistic growth of a population held at a constant dilution rate. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We assess our model predictions against densely-sampled human stool metagenomic time series data. Consistent with our model, donors with slower defecation rates tended to harbor a larger proportion of taxa in later growth phases, while faster defecation rates were associated with more taxa in earlier growth phases. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors.

Ecological Stability Emerges at the Level of Strains in the Human Gut Microbiome

“…Many of these macroecological laws can be recapitulated through intuitive ecological models containing few, if any, free parameters ( 32 , 33 , 36 ). Among these successful models is the stochastic logistic model (SLM), which describes the dynamics of a population experiencing rapid stochastic fluctuations induced by environmental noise around a fixed carrying capacity ( 37 ). Whether the populations making up a community exhibit regular, statistically quantifiable dynamics and, if so, whether these dynamics can be explained using simple models are fundamentally macroecological questions.…”

Ecological Stability Emerges at the Level of Strains in the Human Gut Microbiome

“…To test this hypothesis, we assessed the fit and predictive capacity of the stochastic logistic model (SLM)—a model of a population experiencing stochastic fluctuations around a fixed abundance. Recent work in microbial ecology has demonstrated the power of minimal models like the SLM, requiring the fit of no free parameters, to reproduce qualitative and quantitative features of natural microbial community dynamics [1, 59, 58, 11, 80]. Here, we tested the capacity of the SLM to forecast future strain behavior when trained on an initial subset of timepoints.…”

“…We build on this result by demonstrating that at daily temporal resolution, intra-specific diversity tends to fluctuate around a long-term average value within the hosts examined over periods of years. We show, crucially, that the abundance fluctuations of a large majority of strains we detect can be predicted by the stochastic logistic model (SLM) of growth, a model that also recapitulates fluctuations at the species level [1, 11, 80]. Lastly, we find that empirical patterns of strain abundance variation in these hosts follow macroecological laws which have also previously been demonstrated to hold at the species level, including Taylor’s Law and a Gamma abundance fluctuation distribution [1, 11, 60].…”

“…Recent work in microbial ecology has demonstrated the power of minimal models like the SLM, requiring the fit of no free parameters, to reproduce qualitative and quantitative features of natural microbial community dynamics [1,59,58,11,80]. Here, we tested the capacity of the SLM to forecast future strain behavior when trained on an initial subset of timepoints.…”

“…Many of these macroecological laws can be recapitulated through intuitive ecological models containing few if any free parameters [1, 11, 59]. Among these successful models is the Stochastic Logistic Model (SLM), which describes the dynamics of a population experiencing rapid stochastic fluctuations induced by environmental noise around a fixed carrying capacity [80]. Whether the populations making up a community exhibit regular, statistically quantifiable dynamics, and if so, whether these dynamics can be explained using simple models, are fundamentally macroecological questions.…”