%0 Journal Article %1 Steinway2015Inference %A Steinway, Steven N. %A Biggs, Matthew B. %A Loughran, Thomas P. %A Papin, Jason A. %A Albert, Reka %D 2015 %I Public Library of Science %J PLoS Comput Biol %K gut-microbiome metabolic-networks networks %N 6 %P e1004338+ %R 10.1371/journal.pcbi.1004338 %T Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome %U http://dx.doi.org/10.1371/journal.pcbi.1004338 %V 11 %X We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth. The community of bacteria that live in our intestines (called the ” gut microbiome”) is important to normal intestinal function, and destruction of this community has a causative role in diseases including obesity, diabetes, and even neurological disorders. Clostridum difficile is an opportunistic pathogenic bacterium that causes potentially life-threatening intestinal inflammation and diarrhea and frequently occurs after antibiotic treatment, which wipes out the normal intestinal bacterial community. We use a mathematical model to identify how the normal bacterial community interacts and how this community changes with antibiotic treatment and C. difficile infection. We use this model to identify bacteria that may inhibit C. difficile growth. Our model and subsequent experiments indicate that Barnesiella intestinihominis inhibits C. difficile growth. This result suggests that B. intestinihominis could potentially be used as a probiotic to treat or prevent C. difficile infection.

@article{Steinway2015Inference, abstract = {We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth. The community of bacteria that live in our intestines (called the ” gut microbiome”) is important to normal intestinal function, and destruction of this community has a causative role in diseases including obesity, diabetes, and even neurological disorders. Clostridum difficile is an opportunistic pathogenic bacterium that causes potentially life-threatening intestinal inflammation and diarrhea and frequently occurs after antibiotic treatment, which wipes out the normal intestinal bacterial community. We use a mathematical model to identify how the normal bacterial community interacts and how this community changes with antibiotic treatment and C. difficile infection. We use this model to identify bacteria that may inhibit C. difficile growth. Our model and subsequent experiments indicate that Barnesiella intestinihominis inhibits C. difficile growth. This result suggests that B. intestinihominis could potentially be used as a probiotic to treat or prevent C. difficile infection.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Steinway, Steven N. and Biggs, Matthew B. and Loughran, Thomas P. and Papin, Jason A. and Albert, Reka}, biburl = {https://www.bibsonomy.org/bibtex/2faa62a355cd4789fe902313e17b80a79/karthikraman}, citeulike-article-id = {13657210}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1004338}, day = 23, doi = {10.1371/journal.pcbi.1004338}, interhash = {5b636788ff3689b278430033d35e3612}, intrahash = {faa62a355cd4789fe902313e17b80a79}, journal = {PLoS Comput Biol}, keywords = {gut-microbiome metabolic-networks networks}, month = jun, number = 6, pages = {e1004338+}, posted-at = {2015-07-02 05:57:07}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome}, url = {http://dx.doi.org/10.1371/journal.pcbi.1004338}, volume = 11, year = 2015 }