Ophelia S. Venturelli

The paper constructs synthetic microbial communities to study interspecies interactions on nitrogen fixation and identifies a community for microbial inoculants.

The paper designs synthetic gut microbial communities using mechanistic and machine learning models to robustly inhibit C. difficile growth in vitro and in vivo.

This paper introduces a data-driven model to predict synthetic microbial community functions from genetic features, enabling predictions for new species not included in training data.

The paper introduces a physics-constrained neural ordinary differential equation model to predict microbial community dynamics and interactions, outperforming existing mechanistic and machine learning approaches.

The paper demonstrates population cycles in a cross-feeding microbial community using an engineered system and a nonlinear dynamical model.

The paper designs human gut microbiome communities using machine learning to optimize responses to dietary fibers, identifying beneficial fiber-species combinations that enhance butyrogenic properties.

This study shows how human gut microbiota interactions influence the long-term growth and evolutionary adaptations of C. difficile, including metabolic shifts and reduced disease severity.

This study identifies gut microbiota interactions that inhibit C. difficile strains across nutrient environments, highlighting strain variability and nutrient context as key factors.

This study shows engineered lysins can target and remodel synthetic human gut microbiomes by altering species preferences through domain recombination.

The paper develops a high-throughput optogenetic platform combined with machine learning to enable multiplexed control of light-sensitive transcription factors in yeast.

The paper develops a physically constrained recurrent neural network and Bayesian experimental design framework to optimize microbial community functions by efficiently navigating complex design spaces.

This paper develops DNA sensors in Bacillus subtilis to detect environmental DNA from multiple pathogens with high specificity and sensitivity, using fluorescent reporters for multiplexed detection.

This paper uses microbial community-scale metabolic modeling to predict individual-specific short chain fatty acid production profiles in the human gut and links these predictions to clinical health markers.

This study shows that controlling the arginine dihydrolase pathway in E. coli affects gut community assembly and butyrate production in vitro and in mice.

The paper uses high-throughput single-cell sequencing to show promoter inversion rates drive population heterogeneity in Bacteroides spp.

This study identifies microbial interactions affecting C. difficile's antibiotic response using human gut communities and transcriptional profiling.

The paper uses LSTM to model synthetic human gut microbiome dynamics, outperforming traditional models and identifying key microbial drivers of metabolite production.

This paper develops a model-guided framework to design high-diversity synthetic human gut communities by optimizing culture conditions and leveraging ecological models.

The paper identifies PULs in B. uniformis that influence fitness and community interactions through glycan degradation and butyrate production.

This study shows that species richness in synthetic human gut communities determines the outcome of C. difficile invasion, with mechanisms like acidification and resource competition playing key roles.

The paper designs a synthetic human gut microbiome to explore butyrate production using a model-guided experimental framework.

The paper quantifies HGT efficiency in a synthetic microbial community and identifies factors influencing plasmid transfer via natural competence.

This study uses a microfluidic platform to quantify how spatial arrangement affects gene expression and growth in microbial consortia, showing distance acts as a low-pass filter and feedback mechanisms influence synchronization.

The paper introduces a scalable nonlinear programming framework for parameter estimation in dynamic biological models, demonstrating its efficiency on large-scale synthetic microbiome data.

The paper introduces MINI-Drop, a method to infer microbial interactions in microfluidic droplets, showing it can accurately determine pairwise and higher-order interactions.

The paper develops a model-guided framework to predict microbial interactions in synthetic human gut microbiome communities and identifies key ecological drivers and network design principles.

This study shows that programming mRNA decay with MazF can redirect cellular resources to synthetic circuits, enhancing target functions like fluorescent reporter expression and gluconate metabolism.