The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model‐guided framework to predict higher‐dimensional consortia from time‐resolved measurements of lower‐order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi‐species community dynamics, as opposed to higher‐order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history‐dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human‐associated intestinal species and illuminated design principles of microbial communities.

1The human gut microbiota comprises a dynamic ecological system that contributes significantly 2 to human health and disease. The ecological forces that govern community assembly and 3 stability in the gut microbiota remain unresolved. We developed a generalizable model-guided 4 framework to predict higher-order consortia from time-resolved measurements of lower-order 5 assemblages. This method was employed to decipher microbial interactions in a diverse 12-6 member human gut microbiome synthetic community. We show that microbial growth 7 parameters and pairwise interactions are the major drivers of multi-species community 8 dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial 9 interaction network as well as a top-down approach to community assembly pinpointed both 10 ecological driver and responsive species that were significantly modulated by microbial inter-

Highlights d Inference of microbial interaction networks in microfluidic droplets d Insight into synthetic microbial communities across different environments d Stochastic model of community assembly to study variability in community states d Elucidation of a complex web of interactions between antibiotics and a consortium

1 Microbial interactions are major drivers of microbial community dynamics and functions. However, 2 microbial interactions are challenging to decipher due to limitations in parallel culturing of sub-3 communities across many environments and accurate absolute abundance quantification of 4 constituent members of the consortium. To this end, we developed Microbial Interaction Network 5Inference in microdroplets (MINI-Drop), a high-throughput method to rapidly infer microbial 6interactions in microbial consortia in microfluidic droplets. Fluorescence microscopy coupled to 7 automated computational droplet and cell detection was used to rapidly determine the absolute 8 abundance of each strain in hundreds to thousands of droplets per experiment. We show that 9MINI-Drop can accurately infer pairwise as well as higher-order interactions using a microbial 10 interaction toolbox of defined microbial interactions mediated by distinct molecular mechanisms. 11MINI-Drop was used to investigate how the molecular composition of the environment alters the 12 interaction network of a three-member consortium. To provide insight into the variation in 13 community states across droplets, we developed a probabilistic model of cell growth modified by 14 microbial interactions. In sum, we demonstrate a robust and generalizable method to probe 15 cellular interaction networks by random encapsulation of sub-communities into microfluidic 16droplets. 17

An optical Kerr medium in a pulsed laser cavity can appreciably affect the pulsed laser output via self-induced ellipse rotation and self-focusing. Stretching of a Q-switched ruby laser pulse has been observed with either of these two effects. Transverse mode selection by self-focusing has also been seen. The results appear in good agreement with theoretical prediction.

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Our knowledge of the relationship between the gut microbiome and health has rapidly expanded in recent years. Diet has been shown to have causative effects on microbiome composition, which can have subsequent implications on health. Soylent 2.0 is a liquid meal replacement drink that satisfies nearly 20% of all recommended daily intakes per serving. This study aims to characterize the changes in gut microbiota composition resulting from a short-term Soylent diet. Fourteen participants were separated into two groups: 5 in the regular diet group and 9 in the Soylent diet group. The regular diet group maintained a diet closely resembling self-reported regular diets. The Soylent diet group underwent three dietary phases: A) a regular diet for 2 days, B) a Soylent-only diet (five servings of Soylent daily and water as needed) for 4 days, and C) a regular diet for 4 days. Daily logs self-reporting diet, Bristol stool ratings, and any abdominal discomfort were electronically submitted. Eight fecal samples per participant were collected using fecal sampling kits, which were subsequently sent to uBiome, Inc. for sample processing and V4 16S rDNA sequencing. Reads were clustered into operational taxonomic units (OTUs) and taxonomically identified against the GreenGenes 16S database. We find that an individual’s alpha-diversity is not significantly altered during a Soylent-only diet. In addition, principal coordinate analysis using the unweighted UniFrac distance metric shows samples cluster strongly by individual and not by dietary phase. Among Soylent dieters, we find a significant increase in the ratio of Bacteroidetes to Firmicutes abundance, which is associated with several positive health outcomes, including reduced risks of obesity and intestinal inflammation.

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