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Category Archives: Published Works

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July 16 2025
  • Published Works

A systems biology approach to evaluate potential probiotic candidates for women’s vaginal health

bioRxiv [Preprint]. 2025 Jun 21:2025.06.16.659967. doi: 10.1101/2025.06.16.659967. ABSTRACT Probiotic supplements are marketed for diverse health benefits, yet species inclusion often lacks functional rationale. We surveyed 352 U.S. probiotic products and found 36 unique microbial species, with most supplements containing only one species and no clear link between species and intended health benefit. To evaluate probiotic […]

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July 11 2025
  • Published Works

Ten simple rules for developing a training program

PLoS Comput Biol. 2025 Jul 11;21(7):e1013200. doi: 10.1371/journal.pcbi.1013200. eCollection 2025 Jul. NO ABSTRACT PMID:40644437 | DOI:10.1371/journal.pcbi.1013200

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June 20 2025
  • Published Works

Reframing the role of the objective function in its proper context for metabolic network modeling

Cell Syst. 2025 Jun 18;16(6):101298. doi: 10.1016/j.cels.2025.101298. ABSTRACT The “objective function” is a core concept in metabolic network modeling. Its use has enabled the analysis of large data to drive deeper understanding of cellular metabolism. This commentary reframes how the objective function is discussed to enhance its value and clarify misunderstandings in metabolic network modeling. […]

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May 22 2025
  • Published Works

Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions

Nat Commun. 2025 May 22;16(1):4768. doi: 10.1038/s41467-025-59965-y. ABSTRACT Bacterial vaginosis (BV) is the most prevalent vaginal condition among reproductive-age women presenting with vaginal complaints. Despite its significant impact on women’s health, limited knowledge exists regarding the microbial community composition and metabolic interactions associated with BV. In this study, we analyze metagenomic data obtained from human […]

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December 13 2024
  • Published Works

Applying a polysaccharide lyase from Stenotrophomonas maltophilia to disrupt alginate exopolysaccharide produced by Pseudomonas aeruginosa clinical isolates

Appl Environ Microbiol. 2024 Dec 13:e0185324. doi: 10.1128/aem.01853-24. Online ahead of print. ABSTRACT Pseudomonas aeruginosa is considered one of the most challenging, drug-resistant, opportunistic pathogens partly due to its ability to synthesize robust biofilms. Biofilm is a mixture of extracellular polymeric substances (EPS) that encapsulates microbial cells, leading to immune evasion, antibiotic resistance, and thus […]

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November 18 2024
  • Published Works

Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens

PLoS Biol. 2024 Nov 18;22(11):e3002907. doi: 10.1371/journal.pbio.3002907. Online ahead of print. ABSTRACT Bacterial pathogens pose a major risk to human health, leading to tens of millions of deaths annually and significant global economic losses. While bacterial infections are typically treated with antibiotic regimens, there has been a rapid emergence of antimicrobial resistant (AMR) bacterial strains […]

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September 26 2024
  • Published Works

Celebrating a body of work

PLoS Comput Biol. 2024 Sep 26;20(9):e1012441. doi: 10.1371/journal.pcbi.1012441. eCollection 2024 Sep. NO ABSTRACT PMID:39325699 | DOI:10.1371/journal.pcbi.1012441

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April 26 2024
  • Published Works

Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment

PLoS Comput Biol. 2024 Apr 26;20(4):e1012031. doi: 10.1371/journal.pcbi.1012031. eCollection 2024 Apr. ABSTRACT With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a […]

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February 29 2024
  • Published Works

Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism

PLoS Comput Biol. 2024 Feb 29;20(2):e1011919. doi: 10.1371/journal.pcbi.1011919. Online ahead of print. ABSTRACT Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches […]

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December 27 2023
  • Published Works

A computational model of Pseudomonas syringae metabolism unveils a role for branched-chain amino acids in Arabidopsis leaf colonization

PLoS Comput Biol. 2023 Dec 27;19(12):e1011651. doi: 10.1371/journal.pcbi.1011651. Online ahead of print. ABSTRACT Bacterial pathogens adapt their metabolism to the plant environment to successfully colonize their hosts. In our efforts to uncover the metabolic pathways that contribute to the colonization of Arabidopsis thaliana leaves by Pseudomonas syringae pv tomato DC3000 (Pst DC3000), we created iPst19, […]

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Recent Posts

  • Internal Grant Review: A Pre-Submission Program for Early-Career Clinical and Translational Researchers
  • Integrative genome-scale metabolic model of GABAergic neurons reveals metabolic signatures across the mild cognitive impairment – Alzheimer disease continuum
  • Benchmarking biochemical networks generated by large language models
  • Integrating multi-omics data with network modeling to characterize Pseudomonas aeruginosa persister cell metabolism
  • Mucin-induced metabolic reprogramming in Pseudomonas aeruginosa clinical isolates
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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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Contact Info

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

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