faculty

Elhanan Borenstein

elbo@uw.edu

University of Washington, 

Biophysical and Structural Biology

Genetics, Genomics & Evolution

Microbiology, Infection & Immunity

Computational study of the human microbiome

Faculty Contact Information

Building: Foege Room: S103B Box: 355065 Phone: 206-685-8165 http://elbo.gs.washington.edu/

Accepting Students For:

Rotation, Autumn
Rotation, Spring
Rotation, Summer
Rotation, Winter
Permanent

Publications

Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss.

McNally CP, Borenstein E.

BMC systems biology. 2018; 12(1):69.

PubMed [journal]
PMID:
29907104
PMCID:
PMC6003207

The Skin Microbiome of the Neotropical Frog Craugastor fitzingeri: Inferring Potential Bacterial-Host-Pathogen Interactions From Metagenomic Data.

Rebollar EA, GutiƩrrez-Preciado A, Noecker C, Eng A, Hughey MC, Medina D, Walke JB, Borenstein E, Jensen RV, Belden LK, Harris RN.

Frontiers in microbiology. 2018; 9:466.

PubMed [journal]
PMID:
29615997
PMCID:
PMC5869913

Taxa-function robustness in microbial communities.

Eng A, Borenstein E.

Microbiome. 2018; 6(1):45.

PubMed [journal]
PMID:
29499759
PMCID:
PMC5833107

BURRITO: An Interactive Multi-Omic Tool for Visualizing Taxa-Function Relationships in Microbiome Data.

McNally CP, Eng A, Noecker C, Gagne-Maynard WC, Borenstein E.

Frontiers in microbiology. 2018; 9:365.

PubMed [journal]
PMID:
29545787
PMCID:
PMC5837987

Adaptation of commensal proliferating Escherichia coli to the intestinal tract of young children with cystic fibrosis.

Matamouros S, Hayden HS, Hager KR, Brittnacher MJ, Lachance K, Weiss EJ, Pope CE, Imhaus AF, McNally CP, Borenstein E, Hoffman LR, Miller SI.

Proceedings of the National Academy of Sciences of the United States of America. 2018; 115(7):1605-1610.

PubMed [journal]
PMID:
29378945
PMCID:
PMC5816161

Research Summary

The Borenstein lab focuses on computational study of the human microbiome and of other complex microbial ecosystems. We are broadly interested in the integration of systems biology approaches, computational modeling, and integrative meta-omic analysis to address fundamental questions in microbial ecology and to gain a systems-level, principled, mechanistic understanding of the microbiome. We develop a variety of computational methods and predictive models for studying the human microbiome, and aim to harness such methods for microbiome-based therapy.