Modeling microbial metabolic trade-offs in a chemostat

Abstract

12Nature exhibits much higher biodiversity than predicted by theories of 13 competition. One solution for reconciling this "paradox of the plankton" is to 14 imposes metabolic trade-offs, where species need to allocate limited cellular 15 resources into multiple functions. However, two questions exist for metabolic 16 models: first, as many such models have been proposed with diverse 17 assumptions and different results, can we find a universal language to summarize 18 various models into one unified framework? Second, under the pressure of 19 evolution, will there be a single optimal metabolic strategy that finally dominates 20 over all others? In this work, we address these two questions by constructing a 21 generalizable framework to describe the species-environment feedback in 22 chemostat-type resource-competition models. Employing this framework, a 23 fitness landscape based on the strategy-growth rate relationship can be 24 constructed. Species are capable of creating their own fitness landscape by 25 shaping their nutrient environment, which allows for dynamic fitness landscapes 26 and rich ecological behaviors, and is crucial for biodiversity in all the models we 27 examined. A non-invasible strategy corresponds to a species creating a fitness 28 landscape that places itself at the top. Under certain conditions, more than one 29 species is required to complete this task, which leads to evolutionarily stable 30 coexistence. Our approach facilitates quantitative understanding of chemostat 31

Trophic Interactions and the Drivers of Microbial Community Assembly

“…Another prediction of models with purely trophic hierarchies is that the final state of community assembly is independent of the composition of the founding community; that is, provided that the species are present, the system converges to a stable state regardless of their initial abundance, a state in which the surviving set of species partition the energy input optimally [33,54,88,89]. In practice, however, laboratory systems [17,29,90,91], industrial digesters [92] and natural systems [7] have been shown to display alternative assembly trajectories.…”

“…There are many plausible biological mechanisms one could add to a purely trophic community model that could explain the existence of alternative states, also known as 'multistability'. These mechanisms could involve, for instance, phenotypic plasticity [95], simultaneous competition for multiple resources [96], metabolic trade-offs [89], or higher-order interactions [97]. Here, we choose to highlight two simple mechanisms that we think are likely to be ubiquitous across microbiomes: mutual antagonism and positive feedback loops (Figure 4).…”

The effect of resource dynamics on species packing in diverse ecosystems

“…Recent research has shown some unexpected and interesting non-generic phenomena can appear in GCRMs in the presence of additional constraints on parameter values. A common choice of such constraints is the imposition of a "metabolic budget" on the consumer preference matrix [22,27] tying the maintenance cost m i to the total consumption capacity β C iβ [23,28]. These metabolic tradeoffs can be readily incorporated into the cavity calculations and have significant impacts on species packing as will be discussed below.…”

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

“…The copyright holder for this preprint (which this version posted May 14, 2021. ; https://doi.org/10.1101/2021.05.13.444061 doi: bioRxiv preprint In general, our work demonstrates that it is feasible to reproduce time series statistics using consumer-resource models of microbiota dynamics, thereby generating mechanistic hypotheses for further investigation. In future work, more detailed hypotheses can be generated by investigating how time series statistics are affected by modifications to baseline CR dynamics, such as the incorporation of metabolic crossfeeding 6,31 , functional differentiation from genomic analysis [32][33][34] , and physical variables such as pH 35,36 , temperature 37 , and osmolality 38 . In addition, recent studies have shown that evolution can substantially affect the dynamics of human gut microbiotas [39][40][41] .…”