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Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation
Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbati...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3475664/ https://www.ncbi.nlm.nih.gov/pubmed/23093930 http://dx.doi.org/10.1371/journal.pcbi.1002746 |
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author | Birch, Elsa W. Ruggero, Nicholas A. Covert, Markus W. |
author_facet | Birch, Elsa W. Ruggero, Nicholas A. Covert, Markus W. |
author_sort | Birch, Elsa W. |
collection | PubMed |
description | Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism. |
format | Online Article Text |
id | pubmed-3475664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34756642012-10-23 Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation Birch, Elsa W. Ruggero, Nicholas A. Covert, Markus W. PLoS Comput Biol Research Article Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism. Public Library of Science 2012-10-18 /pmc/articles/PMC3475664/ /pubmed/23093930 http://dx.doi.org/10.1371/journal.pcbi.1002746 Text en © 2012 Birch et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Birch, Elsa W. Ruggero, Nicholas A. Covert, Markus W. Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation |
title | Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation |
title_full | Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation |
title_fullStr | Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation |
title_full_unstemmed | Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation |
title_short | Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation |
title_sort | determining host metabolic limitations on viral replication via integrated modeling and experimental perturbation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3475664/ https://www.ncbi.nlm.nih.gov/pubmed/23093930 http://dx.doi.org/10.1371/journal.pcbi.1002746 |
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