About

Dr. Daniel Segrè obtained a Laurea (B.Sc-M.Sc.) in Physics from the University of Trieste, Italy, and a Ph.D. in Life Sciences at the Weizmann Institute of Science, Israel. Following his postdoctoral training in the Department of Genetics at Harvard Medical School, he joined Boston University as faculty in 2005. He is currently a Professor of Biology and Professor of Computing and Data Science at Boston University, with secondary appointments in Biomedical Engineering and Physics. Dr. Segrè is a core faculty member of the Bioinformatics Program and the Biological Design Center, and he is the founding Director of the Boston University Microbiome Initiative. His research focuses on a quantitative understanding of the principles governing the emergence, evolution, and dynamics of biological systems, including the origin of life and cellular resource allocation. The Segrè laboratory's current research centers on the role of metabolism in natural and artificially designed microbial communities, with applications in biomedicine, metabolic engineering, and environmental sustainability.

Research topics

• Computer Science
• Biology
• Ecology
• Computational biology
• Biochemical engineering
• Engineering
• Biological system
• Artificial Intelligence
• Machine Learning
• Environmental science
• Data science
• Art
• Environmental ethics
• Chemistry
• Paleontology
• Environmental chemistry
• Geology
• Algorithm
• Philosophy
• Oceanography
• Art history
• Astrobiology

Selected publications

• Flux sampling and context-specific genome-scale metabolic models for biotechnological applications

  Trends in biotechnology · 2025-08-07 · 3 citations

  reviewSenior author
  PublisherDOI

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-25

  preprintOpen access

  Abstract 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.

  PublisherOA PDFDOI

• Ligand interaction landscape of transcription factors and essential enzymes in E. coli

  Cell · 2025-01-24 · 11 citations

  article
  PublisherDOI

• MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models

  Genome biology · 2025-03-28 · 4 citations

  articleOpen access

  Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these applications. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a suite of algorithms that helps to identify and visualize errors at the level of connected pathways, rather than individual reactions. We show how MACAW highlights inaccuracies of varying severity in manually curated and automatically generated GSMMs for humans, yeast, and bacteria and helps to identify systematic issues to be addressed in future model construction efforts.

  PublisherOA PDFDOI

• Biophysical metabolic modeling of complex bacterial colony morphology

  Cell Systems · 2025-08-01 · 5 citations

  articleOpen access
  PublisherOA PDFDOI

• Supplementary Text and Supplementary Figures from Dynamic metabolic modeling of ATP allocation during viral infection

  Open MIND · 2025-01-01

  articleSenior author

  Electronic Supplementary Material.pdf

  DOI

• Gut Microbial Signatures of Frailty and Functional Independence in Centenarians and Older Adults

  Innovation in Aging · 2025-12-01

  articleOpen access

  Abstract Frailty and functional limitations decrease quality of life for older adults and are associated with myriad health risks. The gut microbiome is a potential therapeutic avenue for promoting functioning among older adults, but a clearer understanding of mechanisms by which commensal gut bacteria may affect frailty and functional independence is needed. In this study, we analyzed data from an ongoing cohort study, Integrative Longevity Omics (ILO), which has 418 shotgun metagenomics samples of the gut microbiome from centenarians and their offspring. We measured associations of microbial alpha/beta diversity and species with function measures (activities of daily living [ADLs] and instrumental activities of daily living [IADLs]) and Fried frailty phenotype domains (physical activity, fatigability, grip strength, weight loss). Species-level alpha diversity decreased with increasing Pittsburgh fatigability score(β=-0.0051, 95% CI:-0.0098 to -0.0003, p = 0.038), adjusting for age, sex, and education. Beta diversity (Bray-Curtis dissimilarity) was associated with ADLs(R2=0.005, p = 0.002), IADLs(R2=0.005, p = 0.006), and fatigability(R2=0.010, p ≤ 0.001), adjusting for age, sex, and education using PERMANOVA. Finally, we identified several species associated with multiple frailty and functioning scores, including Anareostipes hadrus, which was associated with improved ADLs, IADLs, fatigability, and lower likelihood of substantial weight loss, and is a known anti-inflammatory butyrate producer. Worse ADLs/IADLs and greater fatigability was associated with Clostridium innocuum, a commensal gut bacteria with documented potential for virulence. These results identify microbial species for further analysis in association with other ‘omics layers and for future functional studies to understand their potential mechanistic effects on frailty and functioning outcomes in older adults.

  PublisherOA PDFDOI

• Diet-informed metagenomic clocks for aging in the Integrative Longevity Omics Study

  Innovation in Aging · 2025-12-01

  articleOpen access

  Abstract Aging is a heterogeneous process that unfolds uniquely across individuals and biological systems. Aging clocks, computational models trained on molecular data to estimate biological age, offer a promising framework for investigating aging biology at the individual level. While most clocks rely on epigenetic, transcriptomic, or proteomic data, the human microbiome remains an underutilized source of aging biomarkers despite its central role in host metabolism, immune regulation, and inflammation, all of which are influenced by diet and linked to aging. To explore microbiome-mediated aging, we developed aging clocks using shotgun metagenomics sequencing data from 267 individuals in the Integrative Longevity Omics cohort, including centenarians and their offspring. These clocks estimate biological age based on taxonomic composition across multiple taxonomic levels. To account for dietary influences, we incorporated total caloric intake and a novel metric developed by our group, the Nutrient Variety Index (NVI), which summarizes the distribution of nutrient groups within a diet. Adjusting for caloric intake and NVIs significantly improved clock performance, suggesting that nutrient composition modulates microbiome aging. This integrative approach enhances biological age estimation and has the potential to uncover microbial signatures of healthy aging. Our findings underscore the importance of diet in shaping aging trajectories and highlight the potential of microbiome-based clocks to uncover nutrition-linked aging pathways.

  PublisherOA PDFDOI

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-11

  preprintOpen access

  ABSTRACT 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-GEM ’s practical utility in a real-world use case.

  PublisherDOI

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-18

  preprintOpen access

  SUMMARY 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.

  PublisherOA PDFDOI

Frequent coauthors

• Joshua E. Goldford

  California Institute of Technology

  20 shared

• Ed Reznik

  Memorial Sloan Kettering Cancer Center

  18 shared

• Doron Lancet

  Weizmann Institute of Science

  15 shared

• Kirill S. Korolev

  Boston University

  12 shared

• Daniel Sher

  12 shared

• George M. Church

  Harvard–MIT Division of Health Sciences and Technology

  12 shared

• Ilija Dukovski

  Boston University

  12 shared

• Charles DeLisi

  12 shared

Labs

• Daniel Segrè LabPI

Education

• Ph.D., Computer Science

  University of California, Berkeley

  1991

• M.S., Computer Science

  University of California, Berkeley

  1987

• B.S., Computer Science

  University of California, Berkeley

  1985