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February 23 2022
  • Published Works

Comparative analyses of parasites with a comprehensive database of geno-scale metabolic models

PLoS Comput Biol. 2022 Feb 23;18(2):e1009870. doi: 10.1371/journal.pcbi.1009870. Online ahead of print. ABSTRACT Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within […]

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February 7 2022
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Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions

PLoS Comput Biol. 2022 Feb 7;18(2):e1009341. doi: 10.1371/journal.pcbi.1009341. Online ahead of print. ABSTRACT Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial metabolism. The process of automatically generating GENREs includes identifying metabolic reactions supported by sufficient genomic evidence to generate a draft metabolic network. The draft GENRE is then gapfilled with additional reactions […]

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November 28 2021
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Computational approaches to understanding Clostridioides difficile metabolism and virulence

Curr Opin Microbiol. 2022 Feb;65:108-115. doi: 10.1016/j.mib.2021.11.002. Epub 2021 Nov 25. ABSTRACT The progress of infection by Clostridioides difficile is strongly influenced by metabolic cues it encounters as it colonizes the gastrointestinal tract. Both colonization and regulation of virulence have a multi-factorial interaction between host, microbiome, and gene expression cascades. While these connections with metabolism […]

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October 9 2021
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An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

NPJ Syst Biol Appl. 2021 Oct 8;7(1):37. doi: 10.1038/s41540-021-00198-2. ABSTRACT Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network […]

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October 5 2021
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Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis

mSystems. 2021 Oct 26;6(5):e0091921. doi: 10.1128/mSystems.00919-21. Epub 2021 Oct 5. ABSTRACT The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) […]

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August 23 2021
  • Published Works

Perinatal Outcomes of Asynchronous Influenza Vaccination, CearĂ¡, Brazil, 2013-2018

Emerg Infect Dis. 2021;27(9):2409-2420. doi: 10.3201/eid2709.203791. ABSTRACT In CearĂ¡, Brazil, seasonal influenza transmission begins before national annual vaccination campaigns commence. To assess the perinatal consequences of this misalignment, we tracked severe acute respiratory infection (SARI), influenza, and influenza immunizations during 2013-2018. Among 3,297 SARI cases, 145 (4.4%) occurred in pregnant women. Statewide vaccination coverage was […]

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July 14 2021
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Multidimensional Clinical Surveillance of Pseudomonas aeruginosa Reveals Complex Relationships between Isolate Source, Morphology, and Antimicrobial Resistance

mSphere. 2021 Aug 25;6(4):e0039321. doi: 10.1128/mSphere.00393-21. Epub 2021 Jul 14. ABSTRACT Antimicrobial susceptibility in Pseudomonas aeruginosa is dependent on a complex combination of host and pathogen-specific factors. Through the profiling of 971 clinical P. aeruginosa isolates from 590 patients and collection of paired patient metadata, we show that antimicrobial resistance is associated with not only […]

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June 22 2021
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Untargeted Metabolomics Reveals Species-Specific Metabolite Production and Shared Nutrient Consumption by Pseudomonas aeruginosa and Staphylococcus aureus

mSystems. 2021 Jun 29;6(3):e0048021. doi: 10.1128/mSystems.00480-21. Epub 2021 Jun 22. ABSTRACT While bacterial metabolism is known to impact antibiotic efficacy and virulence, the metabolic capacities of individual microbes in cystic fibrosis lung infections are difficult to disentangle from sputum samples. Here, we show that untargeted metabolomic profiling of supernatants of multiple strains of Pseudomonas aeruginosa […]

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June 14 2021
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Mechanistic models of microbial community metabolism

Mol Omics. 2021 Jun 14;17(3):365-375. doi: 10.1039/d0mo00154f. ABSTRACT Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks […]

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March 30 2021
  • Published Works

Collaborating with our community to increase code sharing

PLoS Comput Biol. 2021 Mar 30;17(3):e1008867. doi: 10.1371/journal.pcbi.1008867. eCollection 2021 Mar. NO ABSTRACT PMID:33784294 | PMC:PMC8009435 | DOI:10.1371/journal.pcbi.1008867

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Recent Posts

  • Identifying metabolic shifts in Crohn’s disease using’ omics-driven contextualized computational metabolic network models
  • Ten simple rules for launching an academic research career
  • Metabolic Network Models of the Gardnerella Pangenome Identify Key Interactions with the Vaginal Environment
  • Enterococci enhance Clostridioides difficile pathogenesis
  • Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae
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Computational Systems Biology Laboratory; The research group of Dr. Jason Papin in the Department of Biomedical Engineering at the University of Virginia.

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The research group of Dr. Jason Papin in the Department of Biomedical Engineering at the University of Virginia

  • Email: papinlab@virginia.edu
  • Phone (434) 924-8195

  • Home
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  • Meet Our Team
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