Biology subjects

Segre, D.

Publications and source records attributed to Segre, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Dynamic flux balance analysis of cancer metabolism using expression-based human cell line models

Aberrant metabolism that supports dysregulated growth is a key feature of cancer. Flux balance analysis, a computational approach that can predict genome-scale metabolic rates based on stoichiometry and mass conservation, has proven valuable in the prediction of pathway utilization and possible drug targets in cancer cells, but deep insight into growth dynamics under physiologically relevant conditions has been limited. To address this gap, we revisited the human metabolism model and its integration of gene-expression data, and we implemented simulations of metabolism in multiple cancer cell lines using static and dynamic flux balance analysis. Our models predict a substantial dependence of intracellular and exchange fluxes, and of the timing of resource utilization, on growth media and specific cell-line makeup. Secretions include lactate and other metabolites that could contribute to shaping the tumor microenvironment. Despite these differences in flux phenotypes, virtual genetic knockout screens predict high similarity of lethality across cell lines and media, and suggest a handful of possible cell-line specific targets. We finally demonstrate the power of dynamic simulations by setting virtual competition assays, which form the foundation for future extensions into dynamic simulations of tumor-stromal interactions in spatially-structured environment.

systems biology↗

bioRxiv2026

Organizing principles in the Nitrogen-Carbon Landscape of Marine Heterotrophic Bacteria

Marine microbes metabolize a wide range of carbon and nitrogen sources, shaping global biogeochemical cycles. Despite being crucial at the global scale, the coupling between carbon and nitrogen remains poorly understood at the level of individual metabolites and bacteria. By phenotyping a library of marine heterotrophic bacteria across increasingly complex carbon and nitrogen sources, we generated a snapshot of this coupling. Growth phenotypes were weakly explained by phylogeny, but could be organized around substrate properties, including C:N stoichiometry and degree of reduction. Beyond explainable average trends along these axes, different strains displayed significant variability. For some bacteria, nitrogen use efficiency was associated with ammonium secretion, consistent with their role in ecological interactions. Furthermore, nonlinearities emerged in how yields depend on combinations of resources, such as mixtures of amino acids and dipeptides. These organizing principles may help understand the ecological role of marine heterotrophic bacteria and parametrize models of biogeochemical cycles.

systems biology↗

bioRxiv2026

Reconstructing heterogeneous metabolic trajectories of E. coli diauxie via a dynamical Maximum Entropy Principle

The glucose-acetate diauxic shift in E. coli is classically described as an abrupt, population-wide switch from glucose to acetate consumption. Recent experiments challenge this view, revealing a robust intermediate regime of co-consumption whose single-cell basis remains unresolved: does it reflect coexisting specialized subpopulations, or genuine mixed metabolic states within individual cells? We first develop a two-state consumer-resource model in which cells optimally grow on either glucose or acetate, and show that observed co-consumption trajectories cannot be decomposed into convex combinations of the two subpopulations -- ruling out discrete metabolic states as a sufficient explanation. Physico-chemical constraints of the metabolic network instead enforce genuine single-cell co-consumption across a continuous spectrum of phenotypes. To resolve this, we apply a dynamical maximum entropy (maximum caliber) framework constrained by batch and chemostat experiments, inferring time-resolved distributions of metabolic fluxes that naturally predict a continuum of single-cell phenotypes spanning glucose overflow, mixed substrate utilization, and acetate consumption -- revealing co-consumption as a dominant, persistent feature of single-cell metabolism around the switch rather than an artifact of population averaging. Finally, we formulate a continuous consumer-resource model over metabolic state space, in which selection, phenotypic diffusion, and moving metabolic boundaries driven by environmental feedback reproduce single-cell co-consumption trajectories and complex dynamical trends inaccessible to discrete models. Together, our results recast diauxic adaptation as a continuous redistribution of single-cell metabolic states rather than a discrete switch.

systems biology↗

bioRxiv2026

Metabolic blueprints of monocultures enable prediction and design of synthetic microbial consortia

Synthetic microbial ecology aims at designing communities with desired properties based on mathematical models of individual organisms. It is unclear whether simplified models harbor enough detail to predict the composition of synthetic communities in metabolically complex environments. Here, we use longitudinal exometabolite data of monocultures for 15 rhizosphere bacteria to parametrize a consumer-resource model, which we use to predict pairwise co-cultures and higher order communities. The capacity to artificially "switch off" cross-feeding interactions in the model demonstrates their importance in ecosystem structure. Leave-one-out and leave-two-out experiments demonstrate that pairwise co-cultures do not necessarily capture inter-species interactions within larger communities and broadly highlight the nonlinearity of interactions. Finally, we demonstrate that our model can be used to identify new sub-communities of three strains with high likelihood of coexistence. Our results establish hybrid mechanistic and data-driven metabolic models as a promising and extendable framework for predicting and engineering microbial communities.

systems biology↗

bioRxiv2025

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models

Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. While well-established tools exist for transcriptomics and other omics layers, the development of robust enrichment resources for metabolomics remains comparatively limited. To address this gap, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites annotation, demonstrating hypeR-GEMs practical utility in a real-world use case.

bioinformatics↗

bioRxiv2025

Biological insights and methodological challenges learned from working with a diverse heterotrophic marine bacterial library

BackgroundOrganized collections of bacterial strains can help bridge the gap between studying model organisms and communities, through comparative experiments between genetically and phenotypically diverse strains. We describe the establishment and initial characterization of a library of 62 marine heterotrophic bacteria, selected to represent a significant fraction of the genome-encoded functional diversity and a wide range of known phytoplankton-bacteria interactions. We focus on important but often undiscussed aspects of collecting and maintaining such a library, verifying strain identity, and applying classical microbiological methods across diverse strains. ResultsCultured strains contain up to hundreds of mutations compared with the reference genomes, with non-synonymous mutations in rpoB and/or rpoC genes observed in [~]15% of the cultures. Most strains grow well at 25{degrees}C, but the dependence of growth rate on temperature and the width of the temperature niche vary between strains in a systematic manner. We describe steps towards designing a universal, defined, minimal media for marine bacteria, revealing that growth inhibition on amino acids and peptides by carbohydrates is widespread. Cell counts obtained from flow cytometry and colony plating differ systematically, as do different methods to assess motility. Finally, we discuss traits potentially related to microbial interactions such as hemolysis, biofilm formation, and antibiotic resistance. Gammaproteobacteria such as Alteromonas, Pseudoalteromonas, and Vibrio reveal consistently robust growth, and activity, perhaps explaining why these clades are well-explored. ConclusionExplicitly discussing the insights and challenges of working with strain libraries will pave the way to robust, reproducible, and generalizable mapping of bacterial traits across diversity.

microbiology↗

bioRxiv2025

Exploring chemotaxis in spatio-temporal stoichiometric models of metabolism: Pseudomonas simiae as a case study

Summary/AbstractChemotaxis is the movement of cells or organisms in the direction of an increasing or decreasing gradient of a chemical, i.e. a chemoattractant or chemorepellent, respectively. In bacteria, this movement is typically manifested through the presence of bias in the random walk generated by run-and-tumble motility. Often, the chemoattractant is consumed by the organism, thus coupling the biophysical, directed motion with the biochemical, metabolic activity in the growing bacterial colony. In this chapter, we describe our approach to simulating chemotaxis, coupled with detailed metabolic activity, within the framework of our modeling software COMETS (Computation of Microbial Ecosystem in Time and Space). By combining genome-scale metabolic models, dynamic flux balance analysis and the newly implemented biophysical chemotaxis model, we built and qualitatively tested a chemotaxis module for COMETS. We applied this model to an automatically constructed genome-scale model of the plant commensal bacterium Pseudomonas simiae WCS417, which we hypothesized would perform chemotaxis on amino acids similar to its close relative Pseudomonas putida. COMETS simulations recapitulated the typical ring colony pattern, which is due to propagation at the colony front, indicative of bacteria growing on a chemoattractant-filled plate. By allowing for detailed examination of spatial distributions and time dynamics of the biomass, as well as concentration profiles of the metabolites involved in the growth and chemotaxis in bacterial colonies, the software method described here enables simulations of complex microbial patterns involving metabolism and directed motility. Beyond testing the appearance of the ring, future model validation could include comparisons of local growth rates and metabolic fluxes, including rates of metabolite uptake and secretion. The newly added COMETS chemotaxis capability integrates flux balance modeling with gradient-induced biomass propagation, paving the way for more realistic simulations of microbial ecosystem dynamics in complex environments, such as plant roots, where chemotaxis may play a fundamental role in microbiome-host dynamics.

systems biology↗

bioRxiv2025

Integrative analysis across metagenomic taxonomic classifiers: A case study of the gut microbiome in aging and longevity

Despite calls for the development of consensus methods, most analyses of shotgun metagenomics data for microbiome studies use a single taxonomic classifier. In this study, we compare inferences from two broadly used classifiers, MetaPhlAn4 (marker-gene-based) and Kraken2 (k-mer-based), applied to stool metagenomic samples from participants in the Integrative Longevity Omics study to measure associations of taxonomic diversity and relative abundance with age, replicating analyses in an independent cohort. We also introduce consensus and meta-analytic approaches to compare and integrate results from multiple classifiers. While many results are consistent across the two classifiers, we find classifier-specific inferences that would be lost when using one classifier alone. When using a correlated meta-analysis approach across classifiers, differential abundance analysis captures more age-associated taxa, including 17 taxa robustly age-associated across cohorts. This study emphasizes the value of employing multiple classifiers and recommends novel approaches that facilitate the integration of results from multiple methodologies.

bioinformatics↗

bioRxiv2025

Improved detection of fungi and uncultivated microorganisms in soil metagenomes using a comprehensive genome database

Soils harbor diverse microbial communities crucial for ecosystem functioning, but poor genomic representation of many uncultured soil microorganisms limits the utility of existing databases to address some of the most pressing questions in environmental microbiology. To address this, we developed the SoilMicrobeDB, a comprehensive, genome-based reference database to enhance metagenomic classification for soil ecosystems, with a focus on previously underrepresented fungal taxa and uncultured organisms. We evaluated the database using a large soil metagenome dataset, comparing classification rates, analyzing fungal-bacterial ratios against phospholipid fatty acid (PLFA) estimates, and validating lineage abundances with rRNA amplicon sequencing data. Mock community analysis was also conducted to test the precision of community classification and the prevalence of false positives. The SoilMicrobeDB workflow improved metagenomic read classification by over 20% and provided more accurate fungal abundance estimates, particularly for nutrient cycling groups such as ectomycorrhizal fungi. Metagenomic-derived fungal-bacterial ratios were correlated with PLFA and qPCR estimates, and lineage proportions were aligned with relative abundances estimates from rRNA amplicon sequencing. Uncultured taxa represented up to 50% of classifiable soil microbial communities in certain biomes. SoilMicrobeDB offers robust taxonomic and functional profiling of soil communities and provides a scalable and updatable tool for soil microbial ecology research. SoilMicrobeDB is accessible through an interactive platform linking genomes to environmental factors, enabling researchers to explore microbial distributions across soil conditions and potentially leading to new insights into soil ecology and management practices.

bioinformatics↗

bioRxiv2024

Dynamic metabolic modeling of ATP allocation during viral infection

Viral pathogens, like SARS-CoV-2, hijack the hosts macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the hosts survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic Flux Balance Analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modeling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modeling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

systems biology↗

bioRxiv2024

Reduced methane emissions in transgenic rice genotypes are associated with altered rhizosphere microbial hydrogen cycling

Rice paddies contribute substantially to atmospheric methane (CH4) and these emissions are expected to increase as the need to feed the human population grows. Here, we show that two independent rice genotypes overexpressing genes for PLANT PEPTIDES CONTAINING SULFATED TYROSINE (PSY) reduced cumulative CH4 emissions by 38% (PSY1) and 58% (PSY2) over the growth period compared with controls. Genome-resolved metatranscriptomic data from rhizosphere soils reveal lower ratios of gene activities for CH4 production versus consumption, decrease in activity of H2-producing genes, and increase in bacterial H2 oxidation pathways in the PSY genotypes. Metabolic modeling using metagenomic and metabolomic data predicts elevated levels of H2 oxidation and suppressed H2 production in the PSY rhizosphere. The H2-oxidizing bacteria have more genes for utilization of gluconeogenic acids than H2-producing counterparts, and their activities were likely stimulated by the observed enrichment of gluconeogenic acids (mostly amino acids) in PSY root exudates. Together these results suggest that decreased CH4 emission is due to the reduction of H2 available for hydrogenotrophic methanogenesis. The combination of rice phenotypic characterization, microbiome multi-omic analysis, and metabolic modeling described here provides a powerful strategy to discover the mechanisms by which specific plant genotypes can alter biogeochemical cycles to reduce CH4 emissions.

microbiology↗

bioRxiv2024

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models

BackgroundGenome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organisms metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. ResultsWe present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organisms genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organisms genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. ConclusionsMACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

bioinformatics↗

bioRxiv2024

Biophysical metabolic modeling of complex bacterial colony morphology

Microbial colony growth is shaped by the physics of biomass propagation and nutrient diffusion, and by the metabolic reactions that organisms activate as a function of the surrounding environment. While microbial colonies have been explored using minimal models of growth and motility, full integration of biomass propagation and metabolism is still lacking. Here, building upon our framework for Computation of Microbial Ecosystems in Time and Space (COMETS), we combine dynamic flux balance modeling of metabolism with collective biomass propagation and demographic fluctuations to provide nuanced simulations of E. coli colonies. Simulations produced realistic colony morphology, consistent with our experiments. They characterize the transition between smooth and furcated colonies and the decay of genetic diversity. Furthermore, we demonstrate that under certain conditions, biomass can accumulate along "metabolic rings" that are reminiscent of coffee-stain rings, but have a completely different origin. Our approach is a key step towards predictive microbial ecosystems modeling.

biophysics↗

bioRxiv2024

Diversity in the Utilization of Different Molecular Classes of Dissolved Organic Matter by Heterotrophic Marine Bacteria

Heterotrophic marine bacteria utilize and recycle dissolved organic matter (DOM), impacting biogeochemical cycles. It is currently unclear to what extent distinct DOM components can be utilized by different heterotrophic clades. Here, we ask how a natural microbial community from the Eastern Mediterranean Sea responds to different molecular classes of DOM. These molecular classes - peptides, amino acids, amino sugars, disaccharides, monosaccharides and organic acids - together comprise much of the biomass of living organisms, released upon their death as DOM. Bulk bacterial activity increased after 24-hours for all treatments relative to the control, while glucose and ATP uptake decreased or remained unchanged. The relative abundance of several bacterial families, assessed using 16S rRNA amplicon sequencing, increased in some treatments: peptides promoted an increase in Pseudoalteromonadaceae, disaccharides promoted both Pseudoalteromonadaceae and Alteromonadaceae, and most other treatments were dominated by Vibrionaceae. While some results were consistent with recent laboratory-based studies, for example Pseudoalteromonadaceae favoring peptides, other clades behaved differently. Alteromonadaceae, for example, grew well in the lab on many substrates but dominated in seawater samples when disaccharides were added. These results highlight the diversity in DOM utilization among heterotrophic bacteria and complexities in the response of natural communities. ImportanceThe marine DOM pool contains numerous molecular classes, which change depending on the phytoplankton species, environmental conditions and interactions with other microbes, viruses and predators. In turn, the availability of these macromolecular pools affects the composition and function of the whole microbial community. Tracing the path between different carbon sources to specific microbes is another step towards revealing the dynamic interaction between bacteria and the DOM pool. This is especially important in warm and oligotrophic marine systems (e.g., Eastern Mediterranean Sea) where nutrients are scarce and may therefore affect microbial activity and growth.

microbiology↗

bioRxiv2023

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by the metabolites available in their environment, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. To this end, we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the diverse assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively smaller ones, is necessary and sufficient to recapitulate all of our experimental observations. In addition to pointing to a fundamental principle of community assembly, the divergence-complexity effect has important implications for microbiome engineering applications, as it can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions.

ecology↗

bioRxiv2022

A resource for the comparison and integration of heterogeneous microbiome networks

Naturally occurring microbial communities often comprise thousands of taxa involved in complex networks of interactions. These interactions can be mediated by several mechanisms, including the competition for resources, the exchange of signals and nutrients, cell-cell contact and antibiotic warfare. In addition to direct measurements and computational predictions of interactions, abundant data on microbial co-occurrence associations can be inferred from correlations of taxa across samples, which can be estimated from metagenomic, and amplicon datasets. The analysis and interpretation of interaction and correlation networks are limited by the challenge of comparing across different datasets, due to heterogeneity of the data itself and to the lack of a platform to facilitate such comparisons. Here, we introduce the Microbial Interaction Network Database (MIND) - a web-based platform for the integrative analysis of different types of microbial networks, freely available at http://microbialnet.org/. In addition to containing a growing body of curated data, including amplicon-based co-occurrence networks, genome-scale model-derived networks, metabolic influence networks and horizontal gene transfer networks, MIND allows users to upload and analyze newly generated networks using a JSON format and standard NCBI taxonomy. The platform provides convenient functions to compare and query multiple networks simultaneously, and to visualize and export networks and datasets. Through some illustrative examples, we demonstrate how the platform might facilitate discoveries and help generate new hypotheses on host-associated and environmentally important microbial ecosystems through the power of knowledge integration.

systems biology↗

bioRxiv2022

Prediction of representative phenotypes using multi-output subset selection

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets ("phenomics"). We introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We applied the algorithm to three microbial datasets and identified environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.

systems biology↗

bioRxiv2022

Computational design and construction of an Escherichia coli strain engineered to produce a non-standard amino acid

Introducing heterologous pathways into host cells constitutes a promising strategy for synthesizing nonstandard amino acids (nsAAs) to enable the production of proteins with expanded chemistries. However, this strategy has proven challenging as the expression of heterologous pathways can disrupt cellular homeostasis of the host cell. Here, we sought to optimize the heterologous production of the nsAA para-aminophenylalanine (pAF) in Escherichia coli. First, we incorporated a heterologous pAF biosynthesis pathway into a genome-scale model of E. coli metabolism, and computationally identified metabolic interventions in the hosts native metabolism to improve pAF production. Next, we explored different ways of imposing these flux interventions experimentally and found that the upregulation of flux in chorismate biosynthesis pathway through the elimination of feedback inhibition mechanisms could significantly raise pAF titers ([~]20 fold) while maintaining a reasonable pAF yield-growth rate trade-off. Overall, this study provides a promising strategy for the biosynthesis of nsAAs in engineered cells.

systems biology↗