Daniel Segrè | Search bioRxiv

bioRxiv2020

The effects of environmental complexity on microbial community yield and structure

Environmental composition is a major, though poorly understood determinant of microbiome dynamics. Here we ask whether general principles govern how key microbial community properties, i.e. yield and diversity, scale with the number of environmental molecules. By assembling hundreds of synthetic consortia, we found that community yield remains constant as a function of environmental complexity, in agreement with additive expectations of an idealized model. However, taxonomic diversity is much lower than expected. By quantifying this deviation with a metric for epistatic interactions between environments, we uncovered simple ecological rules that govern how communities respond nonlinearly to the coupling of different nutrient sets. Our results demonstrate that environmental complexity alone is not sufficient for maintaining microbiome diversity, and provide practical guidance for designing and controlling microbial communities.

ecology↗

bioRxiv2014

Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli

A widely studied problem in systems biology is to predict bacterial phenotype from growth conditions, using mechanistic models such as flux balance analysis (FBA). However, the inverse prediction of growth conditions from phenotype is rarely considered. Here we develop a computational framework to carry out this inverse prediction on a computational model of bacterial metabolism. We use FBA to calculate bacterial phenotypes from growth conditions in E. coli, and then we assess how accurately we can predict the original growth conditions from the phenotypes. Prediction is carried out via regularized multinomial regression. Our analysis provides several important physiological and statistical insights. First, we show that by analyzing metabolic end products we can consistently predict growth conditions. Second, prediction is reliable even in the presence of small amounts of impurities. Third, flux through a relatively small number of reactions per growth source (~10) is sufficient for accurate prediction. Fourth, combining the predictions from two separate models, one trained only on carbon sources and one only on nitrogen sources, performs better than models trained to perform joint prediction. Finally, that separate predictions perform better than a more sophisticated joint prediction scheme suggests that carbon and nitrogen utilization pathways, despite jointly affecting cellular growth, may be fairly decoupled in terms of their dependence on specific assortments of molecular precursors.

Systems Biology↗