Jason Papin

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Primary Appointment

Professor, Biomedical Engineering

Education

  • PhD, Bioengineering, University of California, San Diego

Research Disciplines

Biochemistry, Bioinformatics and Genomics, Biomedical Engineering, Biophysics, Biotechnology, Cardiovascular Biology, Computational Biology, Experimental Pathology, Infectious Diseases/Biodefense, Metabolism, Microbiology, Physiology, Structural Biology

Research Interests

Systems biology, infectious disease, cancer, toxicology, metabolic engineering

Research Description

Systems analysis has become a requirement for making sense of high-throughput data and for characterizing properties of biological networks. In order to extend these recent developments to medical applications, there is a pressing need for reconstructing and analyzing the biochemical networks that direct cellular processes. The subsequent analysis of these networks requires high-performance computing and sophisticated mathematical techniques.
Our research goals consist of the construction and analysis of large-scale biochemical networks and their application to human disease. Currently, we are working to develop methods for incorporating high-throughput data with network reconstructions, and we are using these tools to study fundamental problems in infectious disease, cancer, toxicology, and metabolic engineering.

Training

  • Basic Cardiovascular Research Training Grant
  • Biodefense & Infectious Diseases Short-Term Training to Increase Diversity in Biomedical Sciences
  • Biotechnology Training Grant
  • Cancer Research Training in Molecular Biology
  • Global Biothreats Training Program
  • Infectious Diseases Training Program
  • Training in Cell and Molecular Biology
  • Training in Molecular Biophysics

Selected Publications

2024

Dougherty, B. V., Moore, C. J., Rawls, K. D., Jenior, M. L., Chun, B., Nagdas, S., . . . Papin, J. A. (2024). Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism. PLOS COMPUTATIONAL BIOLOGY, 20(2). doi:10.1371/journal.pcbi.1011919

2023

Moore, C. J., Holstege, C. P., & Papin, J. A. (2023). Metabolic modeling of sex-specific liver tissue suggests mechanism of differences in toxicological responses.. PLoS computational biology, 19(8), e1010927. doi:10.1371/journal.pcbi.1010927

Moore, C. J., Holstege, C. P., & Papin, J. A. (2023). Metabolic modeling of sex-specific tissue predicts mechanisms of differences in toxicological responses.. bioRxiv. doi:10.1101/2023.02.07.527430

Mac Gabhann, F., Pitzer, V. E., & Papin, J. A. (2023). The blossoming of methods and software in computational biology. PLOS COMPUTATIONAL BIOLOGY, 19(8). doi:10.1371/journal.pcbi.1011390

Jenior, M. L., Glass, E. M., & Papin, J. A. (2023). Reconstructor: a COBRApy compatible tool for automated genome-scale metabolic network reconstruction with parsimonious flux-based gap-filling. BIOINFORMATICS, 39(6). doi:10.1093/bioinformatics/btad367

Powers, D. A., Jenior, M., Kolling, G., & Papin, J. (2023). Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies. PLOS COMPUTATIONAL BIOLOGY, 19(4). doi:10.1371/journal.pcbi.1011076

Fernandes, P., Sharma, Y., Zulqarnain, F., McGrew, B., Shrivastava, A., Ehsan, L., . . . Syed, S. (2023). Identifying metabolic shifts in Crohn's disease using' omics-driven contextualized computational metabolic network models. SCIENTIFIC REPORTS, 13(1). doi:10.1038/s41598-022-26816-5

Dillard, L. R., Glass, E. M., Lewis, A. L., Thomas-White, K., & Papin, J. A. (2023). Metabolic Network Models of the Gardnerella Pangenome Identify Key Interactions with the Vaginal Environment. MSYSTEMS, 8(1). doi:10.1128/msystems.00689-22

2022

Smith, A. B., Jenior, M. L., Keenan, O., Hart, J. L., Specker, J., Abbas, A., . . . Zackular, J. P. (2022). Enterococci enhance Clostridioides difficile pathogenesis. NATURE, 611(7937), 780-+. doi:10.1038/s41586-022-05438-x

Fawad, J. A., Luzader, D. H., Hanson, G. F., Moutinho Jr, T. J., McKinney, C. A., Mitchell, P. G., . . . Moore, S. R. (2022). Histone Deacetylase Inhibition by Gut Microbe-Generated Short-Chain Fatty Acids Entrains Intestinal Epithelial Circadian Rhythms. GASTROENTEROLOGY, 163(5), 1377-+. doi:10.1053/j.gastro.2022.07.051

Carey, M., Medlock, G., Stolarczyk, M., Petri Jr, W., Guler, J., & Papin, J. (2022). Comparative analyses of parasites with a comprehensive database of geno-scale metabolic models. PLOS COMPUTATIONAL BIOLOGY, 18(2). doi:10.1371/journal.pcbi.1009870

Papin, J. A., Keim-Malpass, J., & Syed, S. (2022). Ten simple rules for launching an academic research career. PLOS COMPUTATIONAL BIOLOGY, 18(12). doi:10.1371/journal.pcbi.1010689

Jenior, M. L., Dickenson, M. E., & Papin, J. A. (2022). Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae. NPJ SYSTEMS BIOLOGY AND APPLICATIONS, 8(1). doi:10.1038/s41540-022-00252-7

Dillard, L. R., Wase, N., Ramakrishnan, G., Park, J. J., Sherman, N. E., Carpenter, R., . . . Papin, J. A. (2022). Leveraging metabolic modeling to identify functional metabolic alterations associated with COVID-19 disease severity. METABOLOMICS, 18(7). doi:10.1007/s11306-022-01904-9

Cadwallader, L., Mac Gabhann, F., Papin, J., & Pitzer, V. E. (2022). Advancing code sharing in the computational biology community. PLOS COMPUTATIONAL BIOLOGY, 18(6). doi:10.1371/journal.pcbi.1010193

Moutinho, T. J. J., Powers, D. A., Hanson, G. F., Levy, S., Baveja, R., Hefner, I., . . . Hourigan, S. K. (2022). Fecal sphingolipids predict parenteral nutrition-associated cholestasis in the neonatal intensive care unit. JOURNAL OF PARENTERAL AND ENTERAL NUTRITION, 46(8), 1903-1913. doi:10.1002/jpen.2374

Moutinho Jr, T. A., Neubert, B., Jenior, M., & Papin, J. (2022). Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions. PLOS COMPUTATIONAL BIOLOGY, 18(2). doi:10.1371/journal.pcbi.1009341

Jenior, M. L., & Papin, J. A. (2022). Computational approaches to understanding Clostridioides difficile metabolism and virulence. CURRENT OPINION IN MICROBIOLOGY, 65, 108-115. doi:10.1016/j.mib.2021.11.002

2021

Jenior, M. L., Leslie, J. L., Powers, D. A., Garrett, E. M., Walker, K. A., Dickenson, M. E., . . . Papin, J. A. (2021). Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis. MSYSTEMS, 6(5). doi:10.1128/mSystems.00919-21

Carey, M. A., Medlock, G. L., Alam, M., Kabir, M., Uddin, M. J., Nayak, U., . . . Gilchrist, C. A. (2021). Megasphaera in the Stool Microbiota Is Negatively Associated With Diarrheal Cryptosporidiosis. CLINICAL INFECTIOUS DISEASES, 73(6), E1242-E1251. doi:10.1093/cid/ciab207

Grimes, K. L., Dunphy, L. J., Kolling, G. L., Papin, J. A., & Colosi, L. M. (2021). Algae-mediated treatment offers apparent removal of a model antibiotic resistance gene. ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS, 60. doi:10.1016/j.algal.2021.102540

Payne, D. D., Renz, A., Dunphy, L. J., Lewis, T., Drager, A., & Papin, J. A. (2021). An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism. NPJ SYSTEMS BIOLOGY AND APPLICATIONS, 7(1). doi:10.1038/s41540-021-00198-2

Jose, Q. F., Junior, F. S., Lima, T. B. R., Viana, V. A. F., Burgoa, J. S. V., Soares, A. M., . . . Lima, A. A. M. (2021). Perinatal Outcomes of Asynchronous Influenza Vaccination, Ceara, Brazil, 2013-2018. EMERGING INFECTIOUS DISEASES, 27(9), 2409-2420. doi:10.3201/eid2709.203791

Dunphy, L. J., Kolling, G. L., Jenior, M. L., Carroll, J., Attai, A. E., Farnoud, F., . . . Papin, J. A. (2021). Multidimensional Clinical Surveillance of Pseudomonas aeruginosa Reveals Complex Relationships between Isolate Source, Morphology, and Antimicrobial Resistance. MSPHERE, 6(4). doi:10.1128/mSphere.00393-21

Dunphy, L. J., Grimes, K. L., Wase, N., Kolling, G. L., & Papin, J. A. (2021). Untargeted Metabolomics Reveals Species-Specific Metabolite Production and Shared Nutrient Consumption by Pseudomonas aeruginosa and Staphylococcus aureus. MSYSTEMS, 6(3). doi:10.1128/mSystems.00480-21

Dillard, L. R., Payne, D. D., & Papin, J. A. (2021). Mechanistic models of microbial community metabolism. MOLECULAR OMICS, 17(3), 365-375. doi:10.1039/d0mo00154f

Cadwallader, L., Papin, J. A., Mac Gabhann, F., & Kirk, R. (2021). Collaborating with our community to increase code sharing. PLOS COMPUTATIONAL BIOLOGY, 17(3). doi:10.1371/journal.pcbi.1008867

Dougherty, B. V., Rawls, K. D., Kolling, G. L., Vinnakota, K. C., Wallqvist, A., & Papin, J. A. (2021). Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model. CELL REPORTS, 34(10). doi:10.1016/j.celrep.2021.108836

Rawls, K. D., Dougherty, B. V., Vinnakota, K. C., Pannala, V. R., Wallqvist, A., Kolling, G. L., & Papin, J. A. (2021). Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model. TOXICOLOGY AND APPLIED PHARMACOLOGY, 412. doi:10.1016/j.taap.2020.115390

2020

Lieven, C., Beber, M. E., Olivier, B. G., Bergmann, F. T., Ataman, M., Babaei, P., . . . Zhang, C. (2020). Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.. Nature biotechnology, 38(4), 504. doi:10.1038/s41587-020-0477-4

Lieven, C., Beber, M. E., Olivier, B. G., Bergmann, F. T., Ataman, M., Babaei, P., . . . Zhang, C. (2020). MEMOTE for standardized genome-scale metabolic model testing. NATURE BIOTECHNOLOGY, 38(3), 272-276. doi:10.1038/s41587-020-0446-y

Dougherty, B. V., & Papin, J. A. (2020). Systems biology approaches help to facilitate interpretation of cross-species comparisons. CURRENT OPINION IN TOXICOLOGY, 23-24, 74-79. doi:10.1016/j.cotox.2020.06.002

Jenior, M. L., & Papin, J. A. (2020). Clostridioides difficile: Sometimes It Pays To Be Difficult. CELL HOST & MICROBE, 28(3), 358-359. doi:10.1016/j.chom.2020.08.010

Carey, M. A., Draeger, A., Beber, M. E., Papin, J. A., & Yurkovich, J. T. (2020). Community standards to facilitate development and address challenges in metabolic modeling. MOLECULAR SYSTEMS BIOLOGY, 16(8). doi:10.15252/msb.20199235

Papin, J. A., Mac Gabhann, F., Sauro, H. M., Nickerson, D., & Rampadarath, A. (2020). Improving reproducibility in computational biology research. PLOS COMPUTATIONAL BIOLOGY, 16(5). doi:10.1371/journal.pcbi.1007881

Liu, Y., Moore, J. H., Kolling, G. L., McGrath, J. S., Papin, J. A., & Swami, N. S. (2020). Minimum bactericidal concentration of ciprofloxacin to Pseudomonas aeruginosa determined rapidly based on pyocyanin secretion. SENSORS AND ACTUATORS B-CHEMICAL, 312. doi:10.1016/j.snb.2020.127936

Medlock, G. L., Moutinho, T. J., & Papin, J. A. (2020). Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions. PLOS COMPUTATIONAL BIOLOGY, 16(4). doi:10.1371/journal.pcbi.1007847

Jenior, M. L., Moutinho, T. J. J., Dougherty, B. V., & Papin, J. A. (2020). Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. PLOS COMPUTATIONAL BIOLOGY, 16(4). doi:10.1371/journal.pcbi.1007099

White, J. A., Gaver, D. P., Butera, R. J. J., Choi, B., Dunlop, M. J., Grande-Allen, K. J., . . . Lee, A. P. (2020). Core Competencies for Undergraduates in Bioengineering and Biomedical Engineering: Findings, Consequences, and Recommendations. ANNALS OF BIOMEDICAL ENGINEERING, 48(3), 905-912. doi:10.1007/s10439-020-02468-2

Medlock, G. L., & Papin, J. A. (2020). Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning. CELL SYSTEMS, 10(1), 109-+. doi:10.1016/j.cels.2019.11.006

Hourigan, S. K., Moutinho, T. J. J., Berenz, A., Papin, J., Guha, P., Bangiolo, L., . . . Moore, S. R. (2020). Gram-negative Microbiota Blooms in Premature Twins Discordant for Parenteral Nutrition-associated Cholestasis. JOURNAL OF PEDIATRIC GASTROENTEROLOGY AND NUTRITION, 70(5), 640-644. doi:10.1097/MPG.0000000000002617

Rawls, K., Dougherty, B. V., & Papin, J. (2020). Metabolic Network Reconstructions to Predict Drug Targets and Off-Target Effects. METABOLIC FLUX ANALYSIS IN EUKARYOTIC CELLS: METHODS AND PROTOCOLS, 2088, 315-330. doi:10.1007/978-1-0716-0159-4_14

2019

Pannala, V. R., Vinnakota, K. C., Estes, S. K., Trenary, I., O'Brien, T. P., Printz, R. L., . . . Wallqvist, A. (2020). Genome-Scale Model-Based Identification of Metabolite Indicators for Early Detection of Kidney Toxicity. TOXICOLOGICAL SCIENCES, 173(2), 293-312. doi:10.1093/toxsci/kfz228

Carey, M., Medlock, G., Stolarczyk, M., Petri, W., Guler, J., & Papin, J. (2019). Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models. doi:10.1101/772467

Rawls, K. D., Blais, E. M., Dougherty, B. V., Vinnakota, K. C., Pannala, V. R., Wallqvist, A., . . . Papin, J. A. (2019). Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes. TOXICOLOGICAL SCIENCES, 172(2), 279-291. doi:10.1093/toxsci/kfz197

Pannala, V. R., Vinnakota, K. C., Rawl, K. D., Estes, S. K., O'Brien, T. P., Printz, R. L., . . . Wallqvist, A. (2019). Mechanistic identification of biofluid metabolite changes as markers of acetaminophen-induced liver toxicity in rats. TOXICOLOGY AND APPLIED PHARMACOLOGY, 372, 19-32. doi:10.1016/j.taap.2019.04.001

Medlock, G., & Papin, J. (2019). Medusa: software to build and analyze ensembles of genome-scale metabolic network reconstructions. doi:10.1101/547174

Pradhan, D., Papin, J., & Jensen, P. (2019). Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models. doi:10.1101/608430

Jenior, M., Moutinho, T., Dougherty, B., & Papin, J. (2019). Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. doi:10.1101/637124

Carey, M., Dräger, A., Papin, J., & Yurkovich, J. (2019). Community standards to facilitate development and address challenges in metabolic modeling. doi:10.1101/700112

Moutinho, T., Neubert, B., Jenior, M., Carey, M., Medlock, G., Kolling, G., & Papin, J. (2019). Functional Anabolic Network Analysis of Human-associatedLactobacillusStrains. doi:10.1101/746420

Liu, Y., McGrath, J. S., Moore, J. H., Kolling, G. L., Papin, J. A., & Swami, N. S. (2019). Electrofabricated biomaterial-based capacitor on nanoporous gold for enhanced redox amplification. ELECTROCHIMICA ACTA, 318, 828-836. doi:10.1016/j.electacta.2019.06.127

Grimes, K. L., Dunphy, L. J., Loudermilk, E. M., Melara, A. J., Kolling, G. L., Papin, J. A., & Colosi, L. M. (2019). Evaluating the efficacy of an algae-based treatment to mitigate elicitation of antibiotic resistance. CHEMOSPHERE, 237. doi:10.1016/j.chemosphere.2019.124421

Papin, J. A., & Mac Gabhann, F. (2019). Wisdom of crowds in computational biology. PLOS COMPUTATIONAL BIOLOGY, 15(5). doi:10.1371/journal.pcbi.1007032

Gonyar, L. A., Gelbach, P. E., McDuffie, D. G., Koeppel, A. F., Chen, Q., Lee, G., . . . Eby, J. C. (2019). In Vivo Gene Essentiality and Metabolism in Bordetella pertussis. MSPHERE, 4(3). doi:10.1128/mSphere.00694-18

Blazier, A. S., & Papin, J. A. (2019). Reconciling high-throughput gene essentiality data with metabolic network reconstructions. PLOS COMPUTATIONAL BIOLOGY, 15(4). doi:10.1371/journal.pcbi.1006507

Untaroiu, A. M., Carey, M. A., Guler, J. L., & Papin, J. A. (2019). Leveraging the effects of chloroquine on resistant malaria parasites for combination therapies. BMC BIOINFORMATICS, 20. doi:10.1186/s12859-019-2756-y

Dunphy, L. J., Yen, P., & Papin, J. A. (2019). Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa. CELL SYSTEMS, 8(1), 3-+. doi:10.1016/j.cels.2018.12.002

Rawls, K. D., Dougherty, B. V., Blais, E. M., Stancliffe, E., Kolling, G. L., Vinnakota, K., . . . Papin, J. A. (2019). A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools. COMPUTERS IN BIOLOGY AND MEDICINE, 105, 64-71. doi:10.1016/j.compbiomed.2018.12.010

2018

Luzader, D., Moutinho, T. J., Mitchell, P., Papin, J., Hong, C., & Moore, S. (2018). 274 - Gut Microbial Metabolites Modulate the Amplitude and Phase of PER2 and BMAL1 Circadian Rhythms in Intestinal Epithelial Cells and Organoids. Gastroenterology, 154(6), S-67. doi:10.1016/s0016-5085(18)30681-4

Medlock, G., Carey, M., McDuffie, D., Mundy, M., Giallourou, N., Swann, J., . . . Papin, J. (2018). Metabolic mechanisms of interaction within a defined gut microbiota. doi:10.1101/250860

Dunphy, L., Yen, P., & Papin, J. (2018). Network analysis reveals differential metabolic functionality in antibiotic-resistantPseudomonas aeruginosa. doi:10.1101/303289

Blazier, A., & Papin, J. (2018). Reconciling high-throughput gene essentiality data with metabolic network reconstructions. doi:10.1101/415448

Medlock, G., & Papin, J. (2018). Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning. doi:10.1101/460071

Bourne, P. E., Lewitter, F., Markel, S., & Papin, J. A. (2018). One thousand simple rules. PLOS COMPUTATIONAL BIOLOGY, 14(12). doi:10.1371/journal.pcbi.1006670

Medlock, G. L., Carey, M. A., McDuffie, D. G., Mundy, M. B., Giallourou, N., Swann, J. R., . . . Papin, J. A. (2018). Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota. CELL SYSTEMS, 7(3), 245-+. doi:10.1016/j.cels.2018.08.003

Carey, M. A., Covelli, V., Brown, A., Medlock, G. L., Haaren, M., Cooper, J. G., . . . Guler, J. L. (2018). Influential Parameters for the Analysis of Intracellular Parasite Metabolomics. MSPHERE, 3(2). doi:10.1128/mSphere.00097-18

Carey, M. A., & Papin, J. A. (2018). Ten simple rules for biologists learning to program. PLOS COMPUTATIONAL BIOLOGY, 14(1). doi:10.1371/journal.pcbi.1005871

2017

Dunphy, L. J., & Papin, J. A. (2018). Biomedical applications of genome-scale metabolic network reconstructions of human pathogens. CURRENT OPINION IN BIOTECHNOLOGY, 51, 70-79. doi:10.1016/j.copbio.2017.11.014

Bartelt, L. A., Bolick, D. T., Mayneris-Perxachs, J., Kolling, G. L., Medlock, G. L., Zaenker, E. I., . . . Guerrant, R. L. (2017). Cross-modulation of pathogen-specific pathways enhances malnutrition during enteric co-infection with Giardia lamblia and enteroaggregative Escherichia coli. PLOS PATHOGENS, 13(7). doi:10.1371/journal.ppat.1006471

Blais, E. M., Rawls, K. D., Dougherty, B. V., Li, Z. I., Kolling, G. L., Ye, P., . . . Papin, J. A. (2017). Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. NATURE COMMUNICATIONS, 8. doi:10.1038/ncomms14250

Moutinho, T., Panagides, J., Biggs, M., Medlock, G., Kolling, G., & Papin, J. (2017). Novel co-culture plate enables growth dynamic-based assessment of contact-independent microbial interactions. doi:10.1101/145615

Carey, M., Covelli, V., Brown, A., Medlock, G., Haaren, M., Cooper, J., . . . Guler, J. (2017). Influential parameters for the analysis of intracellular parasite metabolomics. doi:10.1101/190421

Nussinov, R., & Papin, J. A. (2017). How can computation advance microbiome research?. PLOS COMPUTATIONAL BIOLOGY, 13(9). doi:10.1371/journal.pcbi.1005547

Yen, P., & Papin, J. A. (2017). History of antibiotic adaptation influences microbial evolutionary dynamics during subsequent treatment. PLOS BIOLOGY, 15(8). doi:10.1371/journal.pbio.2001586

Moutinho, T. J. J., Panagides, J. C., Biggs, M. B., Medlock, G. L., Kolling, G. L., & Papin, J. A. (2017). Novel co-culture plate enables growth dynamic-based assessment of contact-independent microbial interactions. PLOS ONE, 12(8). doi:10.1371/journal.pone.0182163

Carey, M. A., Papin, J. A., & Guler, J. L. (2017). Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance. BMC GENOMICS, 18. doi:10.1186/s12864-017-3905-1

Janes, K. A., Chandran, P. L., Ford, R. M., Lazzara, M. J., Papin, J. A., Peirce, S. M., . . . Lauffenburger, D. A. (2017). An engineering design approach to systems biology. INTEGRATIVE BIOLOGY, 9(7), 574-583. doi:10.1039/c7ib00014f

Bolick, D. T., Mayneris-Perxachs, J., Medlock, G. L., Kolling, G. L., Papin, J. A., Swann, J. R., & Guerrant, R. L. (2017). Increased Urinary Trimethylamine N-Oxide Following Cryptosporidium Infection and Protein Malnutrition Independent of Microbiome Effects. JOURNAL OF INFECTIOUS DISEASES, 216(1), 64-71. doi:10.1093/infdis/jix234

Liu, A., Archer, A. M., Biggs, M. B., & Papin, J. A. (2017). Growth-altering microbial interactions are responsive to chemical context. PLOS ONE, 12(3). doi:10.1371/journal.pone.0164919

Biggs, M. B., & Papin, J. A. (2017). Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA. PLOS COMPUTATIONAL BIOLOGY, 13(3). doi:10.1371/journal.pcbi.1005413

Bartell, J. A., Blazier, A. S., Yen, P., Thogersen, J. C., Jelsbak, L., Goldberg, J. B., & Papin, J. A. (2017). Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. NATURE COMMUNICATIONS, 8. doi:10.1038/ncomms14631

Nussinov, R., Papin, J. A., & Vakser, I. (2017). Computing the Dynamic Supramolecular Structural Proteome. PLOS COMPUTATIONAL BIOLOGY, 13(1). doi:10.1371/journal.pcbi.1005290

2016

Biggs, M. B., Medlock, G. L., Moutinho, T. J., Lees, H. J., Swann, J. R., Kolling, G. L., & Papin, J. A. (2017). Systems-level metabolism of the altered Schaedler flora, a complete gut microbiota. ISME JOURNAL, 11(2), 426-438. doi:10.1038/ismej.2016.130

Liu, A., Archer, A., Biggs, M., & Papin, J. (2016). Growth-Altering Microbial Interactions Are Highly Sensitive to Environmental Context. doi:10.1101/079251

Biggs, M., & Papin, J. (2016). Managing Uncertainty in Metabolic Network Structure and Improving Predictions Using EnsembleFBA. doi:10.1101/077636

Yen, P., & Papin, J. (2016). History of Antibiotic Adaptation Influences Microbial Evolutionary Dynamics During Subsequent Treatment. doi:10.1101/089334

Mayneris-Perxachs, J., Bolick, D. T., Leng, J., Medlock, G. L., Kolling, G. L., Papin, J. A., . . . Guerrant, R. L. (2016). Protein- and zinc-deficient diets modulate the murine microbiome and metabolic phenotype. AMERICAN JOURNAL OF CLINICAL NUTRITION, 104(5), 1253-1262. doi:10.3945/ajcn.116.131797

Nussinov, R., & Papin, J. A. (2016). Computing Biology. PLOS COMPUTATIONAL BIOLOGY, 12(7). doi:10.1371/journal.pcbi.1005050

Chaiboonchoe, A., Ghamsari, L., Dohai, B., Ng, P., Khraiwesh, B., Jaiswal, A., . . . Salehi-Ashtiani, K. (2016). Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation. MOLECULAR BIOSYSTEMS, 12(8), 2394-2407. doi:10.1039/c6mb00237d

Biggs, M. B., & Papin, J. A. (2016). Metabolic network-guided binning of metagenomic sequence fragments. BIOINFORMATICS, 32(6), 867-874. doi:10.1093/bioinformatics/btv671

2015

Steinway, S. N., Biggs, M. B., Loughran, T. P. J., Papin, J. A., & Albert, R. (2015). Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome. PLOS COMPUTATIONAL BIOLOGY, 11(6). doi:10.1371/journal.pcbi.1004338

Varga, J. J., Barbier, M., Mulet, X., Bielecki, P., Bartell, J. A., Owings, J. P., . . . Goldberg, J. B. (2015). Genotypic and phenotypic analyses of a Pseudomonas aeruginosa chronic bronchiectasis isolate reveal differences from cystic fibrosis and laboratory strains. BMC GENOMICS, 16. doi:10.1186/s12864-015-2069-0

Ebrahim, A., Almaas, E., Bauer, E., Bordbar, A., Burgard, A. P., Chang, R. L., . . . Thiele, I. (2015). Do genome-scale models need exact solvers or clearer standards?. MOLECULAR SYSTEMS BIOLOGY, 11(10). doi:10.15252/msb.20156157

Nussinov, R., Bonhoeffer, S., Papin, J. A., & Sporns, O. (2015). From "What Is?" to "What Isn't?" Computational Biology. PLOS COMPUTATIONAL BIOLOGY, 11(7). doi:10.1371/journal.pcbi.1004318

Biggs, M. B., Medlock, G. L., Kolling, G. L., & Papin, J. A. (2015). Metabolic network modeling of microbial communities. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE, 7(5), 317-334. doi:10.1002/wsbm.1308

D'Auria, K. M., Bloom, M. J., Reyes, Y., Gray, M. C., Van Opstal, E. J., Papin, J. A., & Hewlett, E. L. (2015). High temporal resolution of glucosyltransferase dependent and independent effects of Clostridium difficile toxins across multiple cell types. BMC MICROBIOLOGY, 15. doi:10.1186/s12866-015-0361-4

Jensen, P. A., Dougherty, B. V., Moutinho, T. J. J., & Papin, J. A. (2015). Miniaturized Plate Readers for Low-Cost, High-Throughput Phenotypic Screening. JALA, 20(1), 51-55. doi:10.1177/2211068214555414

2014

Jensen, P. A., & Papin, J. A. (2014). MetDraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data. BIOINFORMATICS, 30(9), 1327-1328. doi:10.1093/bioinformatics/btt758

Bartell, J. A., Yen, P., Varga, J. J., Goldberg, J. B., & Papin, J. A. (2014). Comparative Metabolic Systems Analysis of Pathogenic Burkholderia. JOURNAL OF BACTERIOLOGY, 196(2), 210-226. doi:10.1128/JB.00997-13

Newhook, T. E., Blais, E. M., Lindberg, J. M., Adair, S. J., Xin, W., Lee, J. K., . . . Bauer, T. W. (2014). A Thirteen-Gene Expression Signature Predicts Survival of Patients with Pancreatic Cancer and Identifies New Genes of Interest. PLOS ONE, 9(9). doi:10.1371/journal.pone.0105631

2013

Koskimaki, J. E., Blazier, A. S., Clarens, A. F., & Papin, J. A. (2013). Computational Models of Algae Metabolism for Industrial Applications. Industrial Biotechnology, 9(4), 185-195. doi:10.1089/ind.2013.0012

Blais, E. M., Chavali, A. K., & Papin, J. A. (2013). Linking genome-scale metabolic modeling and genome annotation.. Methods in molecular biology (Clifton, N.J.), 985, 61-83. doi:10.1007/978-1-62703-299-5_4

D'Auria, K. M., Kolling, G. L., Donato, G. M., Warren, C. A., Gray, M. C., Hewlett, E. L., & Papin, J. A. (2013). In Vivo Physiological and Transcriptional Profiling Reveals Host Responses to Clostridium difficile Toxin A and Toxin B. INFECTION AND IMMUNITY, 81(10), 3814-3824. doi:10.1128/IAI.00869-13

Biggs, M. B., & Papin, J. A. (2013). Novel Multiscale Modeling Tool Applied to Pseudomonas aeruginosa Biofilm Formation. PLOS ONE, 8(10). doi:10.1371/journal.pone.0078011

Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., . . . Palsson, B. O. (2013). A community-driven global reconstruction of human metabolism. NATURE BIOTECHNOLOGY, 31(5), 419-+. doi:10.1038/nbt.2488

Walpole, J., Papin, J. A., & Peirce, S. M. (2013). Multiscale Computational Models of Complex Biological Systems. ANNUAL REVIEW OF BIOMEDICAL ENGINEERING, VOL 15, 15, 137-154. doi:10.1146/annurev-bioeng-071811-150104

Walters, D. M., Stokes, J. B., Adair, S. J., Stelow, E. B., Borgman, C. A., Lowrey, B. T., . . . Bauer, T. W. (2013). Clinical, Molecular and Genetic Validation of a Murine Orthotopic Xenograft Model of Pancreatic Adenocarcinoma Using Fresh Human Specimens. PLOS ONE, 8(10). doi:10.1371/journal.pone.0077065

2012

Schmidt, B. J., Papin, J. A., & Musante, C. J. (2013). Mechanistic systems modeling to guide drug discovery and development. DRUG DISCOVERY TODAY, 18(3-4), 116-127. doi:10.1016/j.drudis.2012.09.003

Chavali, A. K., D'Auria, K. M., Hewlett, E. L., Pearson, R. D., & Papin, J. A. (2012). A metabolic network approach for the identification and prioritization of antimicrobial drug targets. TRENDS IN MICROBIOLOGY, 20(3), 113-123. doi:10.1016/j.tim.2011.12.004

Blazier, A. S., & Papin, J. A. (2012). Integration of expression data in genome-scale metabolic network reconstructions. FRONTIERS IN PHYSIOLOGY, 3. doi:10.3389/fphys.2012.00299

D'Auria, K. M., Donato, G. M., Gray, M. C., Kolling, G. L., Warren, C. A., Cave, L. M., . . . Hewlett, E. L. (2012). Systems analysis of the transcriptional response of human ileocecal epithelial cells to Clostridium difficile toxins and effects on cell cycle control. BMC SYSTEMS BIOLOGY, 6. doi:10.1186/1752-0509-6-2

Tilghman, R. W., Blais, E. M., Cowan, C. R., Sherman, N. E., Grigera, P. R., Jeffery, E. D., . . . Parsons, J. T. (2012). Matrix Rigidity Regulates Cancer Cell Growth by Modulating Cellular Metabolism and Protein Synthesis. PLOS ONE, 7(5). doi:10.1371/journal.pone.0037231

Chavali, A. K., Blazier, A. S., Tlaxca, J. L., Jensen, P. A., Pearson, R. D., & Papin, J. A. (2012). Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease. BMC SYSTEMS BIOLOGY, 6. doi:10.1186/1752-0509-6-27

Wagenseller, A. G., Shada, A. L., D'Auria, K., Murphy, C. F., Sun, D., Molhoek, K. R., . . . Slingluff, C. L. (2012). MicroRNAs induced in melanoma treated with combination targeted therapy of temsirolimus and bevacizumab. JOURNAL OF CLINICAL ONCOLOGY, 30(15).

2011

Chang, R. L., Ghamsari, L., Manichaikul, A., Hom, E. F. Y., Balaji, S., Fu, W., . . . Papin, J. A. (2011). Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. MOLECULAR SYSTEMS BIOLOGY, 7. doi:10.1038/msb.2011.52

Molhoek, K. R., Shada, A. L., Smolkin, M., Chowbina, S., Papin, J., Brautigan, D. L., & Slingluff, C. L. J. (2011). Comprehensive analysis of receptor tyrosine kinase activation in human melanomas reveals autocrine signaling through IGF-1R. MELANOMA RESEARCH, 21(4), 274-284. doi:10.1097/CMR.0b013e328343a1d6

Ghamsari, L., Balaji, S., Shen, Y., Yang, X., Balcha, D., Fan, C., . . . Salehi-Ashtiani, K. (2011). Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii. BMC GENOMICS, 12. doi:10.1186/1471-2164-12-S1-S4

Oberhardt, M. A., Puchalka, J., dos Santos, V. A. P. M., & Papin, J. A. (2011). Reconciliation of Genome-Scale Metabolic Reconstructions for Comparative Systems Analysis. PLOS COMPUTATIONAL BIOLOGY, 7(3). doi:10.1371/journal.pcbi.1001116

Jensen, P. A., Lutz, K. A., & Papin, J. A. (2011). TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks. BMC SYSTEMS BIOLOGY, 5. doi:10.1186/1752-0509-5-147

Jensen, P. A., & Papin, J. A. (2011). Functional integration of a metabolic network model and expression data without arbitrary thresholding. BIOINFORMATICS, 27(4), 541-547. doi:10.1093/bioinformatics/btq702

Benedict, K. F., Mac Gabhann, F., Amanfu, R. K., Chavali, A. K., Gianchandani, E. P., Glaw, L. S., . . . Skalak, T. C. (2011). Systems Analysis of Small Signaling Modules Relevant to Eight Human Diseases. ANNALS OF BIOMEDICAL ENGINEERING, 39(2), 621-635. doi:10.1007/s10439-010-0208-y

Haggart, C. R., Bartell, J. A., Saucerman, J. J., & Papin, J. A. (2011). WHOLE-GENOME METABOLIC NETWORK RECONSTRUCTION AND CONSTRAINT-BASED MODELING. METHODS IN ENZYMOLOGY, VOL 500, 500, 411-433. doi:10.1016/B978-0-12-385118-5.00021-9

2010

Sefcik, L. S., Wilson, J. L., Papin, J. A., & Botchwey, E. A. (2010). Harnessing Systems Biology Approaches to Engineer Functional Microvascular Networks. TISSUE ENGINEERING PART B-REVIEWS, 16(3), 361-370. doi:10.1089/ten.teb.2009.0611

Glass, G., Papin, J. A., & Mandell, J. W. (2010). SIMPLE: A Sequential Immunoperoxidase Labeling and Erasing Method (vol 57, pg 899, 2009). JOURNAL OF HISTOCHEMISTRY & CYTOCHEMISTRY, 58(10), 939. doi:10.1369/jhc.2010.957209

Gianchandani, E. P., Chavali, A. K., & Papin, J. A. (2010). The application of flux balance analysis in systems biology. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE, 2(3), 372-382. doi:10.1002/wsbm.60

Ruppin, E., Papin, J. A., de Figueiredo, L. F., & Schuster, S. (2010). Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks. CURRENT OPINION IN BIOTECHNOLOGY, 21(4), 502-510. doi:10.1016/j.copbio.2010.07.002

Oberhardt, M. A., Goldberg, J. B., Hogardt, M., & Papin, J. A. (2010). Metabolic Network Analysis of Pseudomonas aeruginosa during Chronic Cystic Fibrosis Lung Infection. JOURNAL OF BACTERIOLOGY, 192(20), 5534-5548. doi:10.1128/JB.00900-10

Schmidt, B. J., Lin-Schmidt, X., Chamberlin, A., Salehi-Ashtiani, K., & Papin, J. A. (2010). Metabolic systems analysis to advance algal biotechnology. BIOTECHNOLOGY JOURNAL, 5(7), 660-670. doi:10.1002/biot.201000129