Spatiotemporal modeling of microbial metabolism
Background Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are...
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| Format: | Article |
| Language: | English |
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BioMed Central
2016
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| Online Access: | http://hdl.handle.net/1721.1/101418 https://orcid.org/0000-0001-8964-8433 https://orcid.org/0000-0003-2895-9443 https://orcid.org/0000-0002-6106-7861 |
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| author | Chen, Jin Phalak, Poonam Gomez, Jose A. Barton, Paul I. Henson, Michael A. Hoeffner, Kai |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Chen, Jin Phalak, Poonam Gomez, Jose A. Barton, Paul I. Henson, Michael A. Hoeffner, Kai |
| author_sort | Chen, Jin |
| collection | MIT |
| description | Background
Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention.
Results
We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution.
Conclusions
Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems. |
| first_indexed | 2024-09-23T17:06:52Z |
| format | Article |
| id | mit-1721.1/101418 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T17:06:52Z |
| publishDate | 2016 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1014182022-09-29T23:46:16Z Spatiotemporal modeling of microbial metabolism Chen, Jin Phalak, Poonam Gomez, Jose A. Barton, Paul I. Henson, Michael A. Hoeffner, Kai Massachusetts Institute of Technology. Department of Chemical Engineering Gomez, Jose A. Hoeffner, Kai Barton, Paul I. Background Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention. Results We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution. Conclusions Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems. 2016-03-02T23:41:07Z 2016-03-02T23:41:07Z 2016-03 2015-09 2016-03-02T04:59:55Z Article http://purl.org/eprint/type/JournalArticle 1752-0509 http://hdl.handle.net/1721.1/101418 Chen, Jin, Jose A. Gomez, Kai Hoffner, Poonam Phalak, Paul I. Barton, and Michael A. Henson. “Spatiotemporal Modeling of Microbial Metabolism.” BMC Syst Biol 10, no. 1 (March 1, 2016). https://orcid.org/0000-0001-8964-8433 https://orcid.org/0000-0003-2895-9443 https://orcid.org/0000-0002-6106-7861 en http://dx.doi.org/10.1186/s12918-016-0259-2 BMC Systems Biology Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ Chen et al. application/pdf BioMed Central BioMed Central |
| spellingShingle | Chen, Jin Phalak, Poonam Gomez, Jose A. Barton, Paul I. Henson, Michael A. Hoeffner, Kai Spatiotemporal modeling of microbial metabolism |
| title | Spatiotemporal modeling of microbial metabolism |
| title_full | Spatiotemporal modeling of microbial metabolism |
| title_fullStr | Spatiotemporal modeling of microbial metabolism |
| title_full_unstemmed | Spatiotemporal modeling of microbial metabolism |
| title_short | Spatiotemporal modeling of microbial metabolism |
| title_sort | spatiotemporal modeling of microbial metabolism |
| url | http://hdl.handle.net/1721.1/101418 https://orcid.org/0000-0001-8964-8433 https://orcid.org/0000-0003-2895-9443 https://orcid.org/0000-0002-6106-7861 |
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