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A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ - Mol. Syst. Biol. (2007)

Bottom Line: A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates.Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented.The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.

ABSTRACT
An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

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Utilizing iAF1260 as a predictive model. (A) A drawing of central metabolism and the ETS included in iAF1260. Originally, the entire network is unconstrained. (B) Application of transcriptional regulatory effects restricts the total number of pathways, or routes, flux can pass through in the network. (C) Further application of known reaction capacities can result in more accurate predictions. For example, the flux through the NADH dehydrogenase enzymes is split in a 1:1 ratio during a simulation to produce an optimal P/O ratio of approximately 1.4 (Gennis and Stewart, 1996; Noguchi et al, 2004). (D) The non-metabolic activity of the cell can be accounted for through maintenance parameters and these were approximated using experimental data under known media conditions. Chemostat data (see Materials and methods) was used (triangles) and the dotted line shows the modeling predictions with the appropriate maintenance parameters. (E) After the parameters are approximated, the model can then be used to predict the GR (circles), product formation (acetate, squares) and additional uptake rates (oxygen, triangles) under different environmental conditions (for succinate growth in this case).
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f3: Utilizing iAF1260 as a predictive model. (A) A drawing of central metabolism and the ETS included in iAF1260. Originally, the entire network is unconstrained. (B) Application of transcriptional regulatory effects restricts the total number of pathways, or routes, flux can pass through in the network. (C) Further application of known reaction capacities can result in more accurate predictions. For example, the flux through the NADH dehydrogenase enzymes is split in a 1:1 ratio during a simulation to produce an optimal P/O ratio of approximately 1.4 (Gennis and Stewart, 1996; Noguchi et al, 2004). (D) The non-metabolic activity of the cell can be accounted for through maintenance parameters and these were approximated using experimental data under known media conditions. Chemostat data (see Materials and methods) was used (triangles) and the dotted line shows the modeling predictions with the appropriate maintenance parameters. (E) After the parameters are approximated, the model can then be used to predict the GR (circles), product formation (acetate, squares) and additional uptake rates (oxygen, triangles) under different environmental conditions (for succinate growth in this case).

Mentions: Figure 3 demonstrates how we addressed the three modeling issues outlined above when using FBA with iAF1260 to predict the physiological state of E. coli growing aerobically on glucose. Initially, all of the pathways characterized in iAF1260 were represented in a computational framework (Figure 3A). We then constrained the reactions that correspond to ORFs that are not transcribed under aerobic glucose conditions to zero allowable flux in the network using the Boolean gene regulatory rules based on 104 transcription factors established by Covert et al (2004), Figure 3B, effectively eliminating 152 reactions (see Supplementary information). Using the reduced network, we then constrained the maximum allowable P/O ratio of the ETS by using observations and predictions from previous studies. E. coli possesses two NADH dehydrogenase components (NDH-1 (nuo) and NDH-2 (ndh)) and two terminal oxidases (bo-type (cyo) and bd-type (cyd) oxidase) in the system (Calhoun et al, 1993; Gennis and Stewart, 1996). Different combinations of these respiratory components can result in an overall translocation that can range from 2 H+/2e− to 7 H+/2e− in iAF1260. The specific constraint we placed on the system was to split the flux ratio between the two NADH dehydrogenases 1:1 (NDH-1:NDH-2), allowing a P/O ratio between 0.5 and 1.375 (Figure 3C) (Calhoun et al, 1993; Noguchi et al, 2004).


A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ - Mol. Syst. Biol. (2007)

Utilizing iAF1260 as a predictive model. (A) A drawing of central metabolism and the ETS included in iAF1260. Originally, the entire network is unconstrained. (B) Application of transcriptional regulatory effects restricts the total number of pathways, or routes, flux can pass through in the network. (C) Further application of known reaction capacities can result in more accurate predictions. For example, the flux through the NADH dehydrogenase enzymes is split in a 1:1 ratio during a simulation to produce an optimal P/O ratio of approximately 1.4 (Gennis and Stewart, 1996; Noguchi et al, 2004). (D) The non-metabolic activity of the cell can be accounted for through maintenance parameters and these were approximated using experimental data under known media conditions. Chemostat data (see Materials and methods) was used (triangles) and the dotted line shows the modeling predictions with the appropriate maintenance parameters. (E) After the parameters are approximated, the model can then be used to predict the GR (circles), product formation (acetate, squares) and additional uptake rates (oxygen, triangles) under different environmental conditions (for succinate growth in this case).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1911197&req=5

f3: Utilizing iAF1260 as a predictive model. (A) A drawing of central metabolism and the ETS included in iAF1260. Originally, the entire network is unconstrained. (B) Application of transcriptional regulatory effects restricts the total number of pathways, or routes, flux can pass through in the network. (C) Further application of known reaction capacities can result in more accurate predictions. For example, the flux through the NADH dehydrogenase enzymes is split in a 1:1 ratio during a simulation to produce an optimal P/O ratio of approximately 1.4 (Gennis and Stewart, 1996; Noguchi et al, 2004). (D) The non-metabolic activity of the cell can be accounted for through maintenance parameters and these were approximated using experimental data under known media conditions. Chemostat data (see Materials and methods) was used (triangles) and the dotted line shows the modeling predictions with the appropriate maintenance parameters. (E) After the parameters are approximated, the model can then be used to predict the GR (circles), product formation (acetate, squares) and additional uptake rates (oxygen, triangles) under different environmental conditions (for succinate growth in this case).
Mentions: Figure 3 demonstrates how we addressed the three modeling issues outlined above when using FBA with iAF1260 to predict the physiological state of E. coli growing aerobically on glucose. Initially, all of the pathways characterized in iAF1260 were represented in a computational framework (Figure 3A). We then constrained the reactions that correspond to ORFs that are not transcribed under aerobic glucose conditions to zero allowable flux in the network using the Boolean gene regulatory rules based on 104 transcription factors established by Covert et al (2004), Figure 3B, effectively eliminating 152 reactions (see Supplementary information). Using the reduced network, we then constrained the maximum allowable P/O ratio of the ETS by using observations and predictions from previous studies. E. coli possesses two NADH dehydrogenase components (NDH-1 (nuo) and NDH-2 (ndh)) and two terminal oxidases (bo-type (cyo) and bd-type (cyd) oxidase) in the system (Calhoun et al, 1993; Gennis and Stewart, 1996). Different combinations of these respiratory components can result in an overall translocation that can range from 2 H+/2e− to 7 H+/2e− in iAF1260. The specific constraint we placed on the system was to split the flux ratio between the two NADH dehydrogenases 1:1 (NDH-1:NDH-2), allowing a P/O ratio between 0.5 and 1.375 (Figure 3C) (Calhoun et al, 1993; Noguchi et al, 2004).

Bottom Line: A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates.Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented.The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.

ABSTRACT
An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

Show MeSH
Related in: MedlinePlus