Daniel Segrè

The paper shows that some bacteria grow better on mixed carbon sources than on individual ones, and links this to their genetic makeup and metabolic preferences.

This study uses dynamic flux balance analysis to model cancer cell metabolism, revealing dependencies on growth media and cell-line specificities, and identifying potential therapeutic targets.

hypeR-GEM connects metabolite signatures to enzyme-coding genes using genome-scale metabolic models, showing concordance with proteomic and transcriptomic data in real-world applications.

This study investigates how marine heterotrophic bacteria couple carbon and nitrogen metabolism, revealing patterns based on substrate properties and strain variability.

This paper develops a dynamical maximum entropy framework to model single-cell metabolic states during E. coli diauxie, revealing co-consumption as a continuous rather than discrete phenomenon.

This study shows that transgenic rice genotypes overexpressing PSY genes reduce methane emissions by altering rhizosphere microbial hydrogen cycling through changes in gene activity and metabolic pathways.

The paper uses consumer-resource models parametrized from monoculture exometabolite data to predict and design synthetic microbial consortia, revealing the importance of cross-feeding interactions and nonlinear dynamics.

The paper establishes and characterizes a marine bacterial library, identifying genetic variations, growth patterns, and methodological challenges in strain maintenance and analysis.

The paper develops a chemotaxis module for COMETS, a microbial ecosystem simulation tool, and applies it to Pseudomonas simiae to study chemotaxis and metabolism coupling.

This study compares two metagenomic classifiers, finds classifier-specific results, and recommends integrating multiple classifiers to better capture age-associated taxa.

The paper introduces SoilMicrobeDB, a genome-based database improving metagenomic classification of soil microorganisms, particularly fungi and uncultivated taxa, and shows it enhances classification accuracy and fungal abundance estimates.

The paper develops a computational framework to model ATP allocation during viral infection, revealing new metabolic pathways and resource competition dynamics between host and virus.

This study investigates how different molecular classes of dissolved organic matter (DOM) are utilized by heterotrophic marine bacteria in the Eastern Mediterranean Sea, revealing diverse utilization patterns among bacterial clades.

The paper introduces MACAW, a workflow for detecting errors in genome-scale metabolic models, showing that error types vary with curation methods and that pathway-level errors are particularly challenging.

The paper models bacterial colony morphology by integrating metabolic reactions and biomass propagation, showing realistic colony shapes and metabolic ring formation.

The paper introduces MIND, a platform for comparing and integrating microbial interaction networks from diverse datasets.

This paper introduces MASS, an algorithm for selecting representative phenotypes from complex datasets, applied to microbial data to identify predictive environmental conditions.

The paper optimizes the production of a non-standard amino acid in E. coli by combining computational modeling and experimental validation of metabolic interventions.

The paper uses metabolic modeling and sequence analysis to show that coenzyme redundancy reduces protein costs and promotes coenzyme specificity in E. coli.

This paper introduces a dynamic flux balance model to simulate extracellular enzyme secretion and activity in microbial communities, finding optimal secretion rates that maximize growth or biomass yield based on environmental conditions.

BowSaw identifies high-order trait interactions linked to complex phenotypes using random forest structure, revealing microbial combinations associated with Crohn's disease.

This paper introduces an evolutionary algorithm combining machine learning and dynamic flux balance analysis to design microbial communities by optimizing environmental conditions for target phenotypes.

The paper compares the thermodynamic properties of biological and non-biological compounds in redox chemical space, finding metabolites have lower Gibbs energies and specific functional group enrichments.

This paper develops a Python tool for studying string chemistries and introduces a network-pruning algorithm to generate minimal metabolic networks.

The paper shows that under certain boundary conditions, early life could have relied on an organo-sulfur metabolism driven by a modified reductive TCA cycle.

The paper quantifies biosynthetic network robustness in the human oral microbiome using a probabilistic metric to identify metabolic interdependences and uncultivated taxa.

The paper uses in silico models to study metabolic division of labor in microbial communities, finding nonintuitive strategies like splitting the TCA cycle between strains.

The paper uses genome-scale metabolic models to show that costless metabolite exchange can drive stable microbial interactions and synthetic consortia.

The paper uses machine learning to infer missing edges and interaction mechanisms in microbial networks using trait-level data.

This paper investigates butyrate production by F. prausnitzii and B. thetaiotaomicron in monoculture and coculture using experiments and simulations, finding enhanced butyrate production in coculture and complex metabolic strategies.