EcoliWiki - User contributions [en]

Metabolic Network Reconstructions

2012-07-26T02:32:25Z

JOrth:

{{Pagetop}} 

== Short Description ==
Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for over 60 organisms so far, including ''E. coli''. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of ''E. coli'' have existed for nearly twenty years <ref name='PMID:18609648'/>. The first genome-scale model of ''E. coli'' metabolism was released in 2000, and this model continues to be expanded and updated today.

== Constraint-Based Modeling ==
Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices ('''S'''), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, '''S''' is a sparse matrix. The size of '''S''' is ''m''*''n'' for a network with ''m'' metabolites and ''n'' reactions. The vector '''x''' with length ''m'' can then be defined as the concentrations of all the metabolites and the vector '''v''' with length ''n'' contains the fluxes through each reaction. A mass balance equation can then be written:

d'''x'''/dt = '''Sv'''

or at steady state:

'''0''' = '''Sv'''

This equation defines the space of possible flux distributions ('''v''') allowed by the network. These flux distributions can be further constrained by imposing upper and lower bounds on the flux through each reaction, based on known biochemical data. Flux balance analysis is a method for identifying a particular '''v'''<ref name='PMID:20212490'/>. To perform flux balance analysis, an objective function such as maximum growth must be defined. Linear programming is then used to quickly identify a '''v''' that optimizes this objective, given the constraints of the model. Flux balance analysis and related computational methods can be used along with constraint-based models to predict growth rates in different conditions or with different genetic perturbations<ref name='PMID:18392986'/>, identify missing genes or reactions in a network reconstruction<ref name='PMID:17088549'/>, or produce metabolic engineering strain designs<ref name='PMID:14595777'/>.

== Constraint-Based Models of ''E. coli'' ==
=== ''i''JE660 ===
The first genome-scale metabolic model of ''E. coli'', ''i''JE660, was published in 2000<ref name='PMID:10805808'/>. This model accounts for the products of 660 metabolic genes, and has 627 reactions and 438 metabolites. It includes a biomass reaction based on the measured components of ''E. coli'' biomass that can be used to simulate growth. This model was built using information from textbooks, databases, and extensive literature searches. Reactions were manually curated to ensure correct stoichiometry and use of realistic cofactors.

=== ''i''JR904 ===
In 2003, the ''i''JE660 network was updated to form ''i''JR904<ref name='PMID:12952533'/>. This model is significantly expanded, containing 904 genes, 931 compartments, and 625 metabolites. ''i''JR904 contains explicit gene-protein-reaction interactions, Boolean rules that define which genes are required for each reaction. Reactions were checked for proper charge balancing, and gaps in the model were identified and filled when possible.

=== ''i''AF1260 ===
The next update to the ''E. coli'' genome-scale metabolic model was ''i''AF1260, published in 2007<ref name='PMID:17593909'/>. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the [http://www.ecocyc.org/ EcoCyc] database, and thermodynamic analysis was performed to predict the reversibility of reactions. ''i''AF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution<ref name='PMID:18536691'/>.

=== ''i''JO1366 ===
The latest version of the ''E. coli'' metabolic network model was published in 2011 and named ''i''JO1366 <ref name='PMID:21988831'/>. The model was expanded again, this time containing 2251 reactions, 1136 unique metabolites, and accounting for 1366 genes. Newly characterized reactions and pathways were added, and the scope of the model was increased to include biosynthetic pathways for for cofactors such as iron-sulfur clusters and molybdenum cofactors. The biomass reaction was updated and the growth and non-growth associated maintenance parameters were recalculated. As the latest version of the ''E. coli'' metabolic model, ''i''JO1366 continues to be updated as new discoveries are made.

=== ''E. coli'' core model ===
The core ''E. coli'' model is a small-scale model of the central metabolism of ''E. coli''. It is a modified subset of the ''i''AF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.

== Links ==
''i''JR904, ''i''AF1260, and ''i''JO1366 are available from the [http://bigg.ucsd.edu/ BiGG] database.

The core ''E. coli'' model is available [http://systemsbiology.ucsd.edu/Downloads/E_coli_Core here].

The COBRA Toolbox, a Matlab toolbox for analyzing constraint-based models, is available [http://systemsbiology.ucsd.edu/Downloads/Cobra_Toolbox here].

== References ==
{{RefHelp}}
<references/>
[[Category:Bioinformatics Tools]]

PMID:18609648

2010-03-10T20:33:46Z

JOrth: Fill PMID: Page!

{{RightTOC}}
<protect>
 '''Varma, A, Boesch, BW and Palsson, BO''' (1993) Biochemical production capabilities of escherichia coli.''Biotechnol. Bioeng.'' '''42''':59-73

{|{{Prettytable}} align = 'center' width= '70%'
|-
|{{TableHeadingColor}} |'''Abstract''' || <protect>Microbial metabolism provides at mechanism for the conversion of substrates into useful biochemicals. Utilization of microbes in industrial processes requires a modification of their natural metabolism in order to increase the efficiency of the desired conversion. Redirection of metabolic fluxes forms the basis of the newly defined field of metabolic engineering. In this study we use a flux balance based approach to study the biosynthesis of the 20 amino acids and 4 nucleotides as biochemical products. These amino acids and nucleotides are primary products of biosynthesis as well as important industrial products and precursors for the production of other biochemicals. The biosynthetic reactions of the bacterium Escherichia coli have been formulated into a metabolic network, and growth has been defined as a balanced drain on the metabolite pools corresponding to the cellular composition. Theoretical limits on the conversion of glucose, glycerol, and acetate substrates to biomass as well as the biochemical products have been computed. The substrate that results in the maximal carbon conversion to a particular product is identified. Criteria have been developed to identify metabolic constraints in the optimal solutions. The constraints of stoichiometry, energy, and redox have been determined in the conversions of glucose, glycerol, and acetate substrates into the biochemicals. Flux distributions corresponding to the maximal production of the biochemicals are presented. The goals of metabolic engineering are the optimal redirection of fluxes from generating biomass toward producing the desired biochemical. Optimal biomass generation is shown to decrease in a piecewise linear manner with increasing product formation. In some cases, synergy is observed between biochemical production and growth, leading to an increased overall carbon conversion. Balanced growth and product formation are important in a bioprocess, particularly for nonsecreted products. (c) 1993 John Wiley & Sons, Inc.</protect>
|-
|{{TableHeadingColor}} nowrap |'''Links''' || [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=18609648 PubMed] Online version:[http://dx.doi.org/10.1002/bit.260420109 10.1002/bit.260420109]
|}
</protect>
==Significance {{HelpIcon|Help:Literature}} ==
{{LitSignificance}}

== Useful Materials and Methods {{HelpIcon|Help:Literature}}==
{{LitMaterials}}

==Annotations {{HelpIcon|Help:Literature}}==
<protect>

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|- {{table heading style}}
!|Gene product!!Qualifier!!GO ID!!GO term name!!Evidence Code!!with/from!!Aspect!!Notes!!Status

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==EcoliWiki Links {{HelpIcon|Help:Literature}} ==
{{LitLinks}}

==See also==

==References==
{{RefHelp}}
<references/>

PMID:18609648

2010-03-10T20:33:46Z

JOrth: New PMID: Page!

Metabolic Network Reconstructions

2010-03-10T20:32:37Z

JOrth:

{{Pagetop}} 

== Short Description ==
Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including ''E. coli''. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of ''E. coli'' have existed for nearly twenty years <ref name='PMID:18609648'/>. The first genome-scale model of ''E. coli'' metabolism was released in 2000, and this model continues to be expanded and updated today.

== Constraint-Based Modeling ==
Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices ('''S'''), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, '''S''' is a sparse matrix. The size of '''S''' is ''m''*''n'' for a network with ''m'' metabolites and ''n'' reactions. The vector '''x''' with length ''m'' can then be defined as the concentrations of all the metabolites and the vector '''v''' with length ''n'' contains the fluxes through each reaction. A mass balance equation can then be written:

d'''x'''/dt = '''Sv'''

or at steady state:

'''0''' = '''Sv'''

This equation defines the space of possible flux distributions ('''v''') allowed by the network. These flux distributions can be further constrained by imposing upper and lower bounds on the flux through each reaction, based on known biochemical data. Flux balance analysis is a method for identifying a particular '''v'''<ref name='PMID:20212490'/>. To perform flux balance analysis, an objective function such as maximum growth must be defined. Linear programming is then used to quickly identify a '''v''' that optimizes this objective, given the constraints of the model. Flux balance analysis and related computational methods can be used along with constraint-based models to predict growth rates in different conditions or with different genetic perturbations<ref name='PMID:18392986'/>, identify missing genes or reactions in a network reconstruction<ref name='PMID:17088549'/>, or produce metabolic engineering strain designs<ref name='PMID:14595777'/>.

== Constraint-Based Models of ''E. coli'' ==
=== ''i''JE660 ===
The first genome-scale metabolic model of ''E. coli'', ''i''JE660, was published in 2000<ref name='PMID:10805808'/>. This model accounts for the products of 660 metabolic genes, and has 627 reactions and 438 metabolites. It includes a biomass reaction based on the measured components of ''E. coli'' biomass that can be used to simulate growth. This model was built using information from textbooks, databases, and extensive literature searches. Reactions were manually curated to ensure correct stoichiometry and use of realistic cofactors.

=== ''i''JR904 ===
In 2003, the ''i''JE660 network was updated to form ''i''JR904<ref name='PMID:12952533'/>. This model is significantly expanded, containing 904 genes, 931 compartments, and 625 metabolites. ''i''JR904 contains explicit gene-protein-reaction interactions, Boolean rules that define which genes are required for each reaction. Reactions were checked for proper charge balancing, and gaps in the model were identified and filled when possible.

=== ''i''AF1260 ===
The latest update of the ''E. coli'' genome-scale metabolic model is ''i''AF1260, published in 2007<ref name='PMID:17593909'/>. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the [http://www.ecocyc.org/ EcoCyc] database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the ''E. coli'' metabolic model, ''i''AF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. ''i''AF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution<ref name='PMID:18536691'/>.

=== ''E. coli'' core model ===
The core ''E. coli'' model is a small-scale model of the central metabolism of ''E. coli''. It is a modified subset of the ''i''AF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.

== Links ==
''i''JR904 and ''i''AF1260 are available from the [http://bigg.ucsd.edu/ BiGG] database.

The core ''E. coli'' model is available [http://systemsbiology.ucsd.edu/Downloads/E_coli_Core here].

The COBRA Toolbox, a Matlab toolbox for analyzing constraint-based models, is available [http://systemsbiology.ucsd.edu/Downloads/Cobra_Toolbox here].

== References ==
{{RefHelp}}
<references/>
[[Category:Bioinformatics Tools]]

Metabolic Network Reconstructions

2010-03-05T23:20:22Z

JOrth:

{{Pagetop}} 

== Short Description ==
Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including ''E. coli''. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of ''E. coli'' have existed for nearly twenty years <ref name='PMID:18609648'/>. The first genome-scale model of ''E. coli'' metabolism was released in 2000, and this model continues to be expanded and updated today.

== Constraint-Based Modeling ==
Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices ('''S'''), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, '''S''' is a sparse matrix. The size of '''S''' is ''m''*''n'' for a network with ''m'' metabolites and ''n'' reactions. The vector '''x''' with length ''m'' can then be defined as the concentrations of all the metabolites and the vector '''v''' with length ''n'' contains the fluxes through each reaction. A mass balance equation can then be written:

d'''x'''/dt = '''Sv'''

or at steady state:

'''0''' = '''Sv'''

This equation defines the space of possible flux distributions ('''v''') allowed by the network. These flux distributions can be further constrained by imposing upper and lower bounds on the flux through each reaction, based on known biochemical data. Flux balance analysis is a method for identifying a particular '''v'''. To perform flux balance analysis, an objective function such as maximum growth must be defined. Linear programming is then used to quickly identify a '''v''' that optimizes this objective, given the constraints of the model. Flux balance analysis and related computational methods can be used along with constraint-based models to predict growth rates in different conditions or with different genetic perturbations<ref name='PMID:18392986'/>, identify missing genes or reactions in a network reconstruction<ref name='PMID:17088549'/>, or produce metabolic engineering strain designs<ref name='PMID:14595777'/>.

== Constraint-Based Models of ''E. coli'' ==
=== ''i''JE660 ===
The first genome-scale metabolic model of ''E. coli'', ''i''JE660, was published in 2000<ref name='PMID:10805808'/>. This model accounts for the products of 660 metabolic genes, and has 627 reactions and 438 metabolites. It includes a biomass reaction based on the measured components of ''E. coli'' biomass that can be used to simulate growth. This model was built using information from textbooks, databases, and extensive literature searches. Reactions were manually curated to ensure correct stoichiometry and use of realistic cofactors.

=== ''i''JR904 ===
In 2003, the ''i''JE660 network was updated to form ''i''JR904<ref name='PMID:12952533'/>. This model is significantly expanded, containing 904 genes, 931 compartments, and 625 metabolites. ''i''JR904 contains explicit gene-protein-reaction interactions, Boolean rules that define which genes are required for each reaction. Reactions were checked for proper charge balancing, and gaps in the model were identified and filled when possible.

=== ''i''AF1260 ===
The latest update of the ''E. coli'' genome-scale metabolic model is ''i''AF1260, published in 2007<ref name='PMID:17593909'/>. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the [http://www.ecocyc.org/ EcoCyc] database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the ''E. coli'' metabolic model, ''i''AF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. ''i''AF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution<ref name='PMID:18536691'/>.

=== ''E. coli'' core model ===
The core ''E. coli'' model is a small-scale model of the central metabolism of ''E. coli''. It is a modified subset of the ''i''AF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.

== Links ==
''i''JR904 and ''i''AF1260 are available from the [http://bigg.ucsd.edu/ BiGG] database.

The core ''E. coli'' model is available [http://systemsbiology.ucsd.edu/Downloads/E_coli_Core here].

The COBRA Toolbox, a Matlab toolbox for analyzing constraint-based models, is available [http://systemsbiology.ucsd.edu/Downloads/Cobra_Toolbox here].

== References ==
{{RefHelp}}
<references/>
[[Category:Bioinformatics Tools]]

Metabolic Network Reconstructions

2010-03-05T23:19:55Z

JOrth:

{{Pagetop}} 
{{See|Help:Bioinformatics Tools|tools}}

== Short Description ==
Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including ''E. coli''. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of ''E. coli'' have existed for nearly twenty years <ref name='PMID:18609648'/>. The first genome-scale model of ''E. coli'' metabolism was released in 2000, and this model continues to be expanded and updated today.

== Constraint-Based Modeling ==
Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices ('''S'''), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, '''S''' is a sparse matrix. The size of '''S''' is ''m''*''n'' for a network with ''m'' metabolites and ''n'' reactions. The vector '''x''' with length ''m'' can then be defined as the concentrations of all the metabolites and the vector '''v''' with length ''n'' contains the fluxes through each reaction. A mass balance equation can then be written:

d'''x'''/dt = '''Sv'''

or at steady state:

'''0''' = '''Sv'''

This equation defines the space of possible flux distributions ('''v''') allowed by the network. These flux distributions can be further constrained by imposing upper and lower bounds on the flux through each reaction, based on known biochemical data. Flux balance analysis is a method for identifying a particular '''v'''. To perform flux balance analysis, an objective function such as maximum growth must be defined. Linear programming is then used to quickly identify a '''v''' that optimizes this objective, given the constraints of the model. Flux balance analysis and related computational methods can be used along with constraint-based models to predict growth rates in different conditions or with different genetic perturbations<ref name='PMID:18392986'/>, identify missing genes or reactions in a network reconstruction<ref name='PMID:17088549'/>, or produce metabolic engineering strain designs<ref name='PMID:14595777'/>.

== Constraint-Based Models of ''E. coli'' ==
=== ''i''JE660 ===
The first genome-scale metabolic model of ''E. coli'', ''i''JE660, was published in 2000<ref name='PMID:10805808'/>. This model accounts for the products of 660 metabolic genes, and has 627 reactions and 438 metabolites. It includes a biomass reaction based on the measured components of ''E. coli'' biomass that can be used to simulate growth. This model was built using information from textbooks, databases, and extensive literature searches. Reactions were manually curated to ensure correct stoichiometry and use of realistic cofactors.

=== ''i''JR904 ===
In 2003, the ''i''JE660 network was updated to form ''i''JR904<ref name='PMID:12952533'/>. This model is significantly expanded, containing 904 genes, 931 compartments, and 625 metabolites. ''i''JR904 contains explicit gene-protein-reaction interactions, Boolean rules that define which genes are required for each reaction. Reactions were checked for proper charge balancing, and gaps in the model were identified and filled when possible.

=== ''i''AF1260 ===
The latest update of the ''E. coli'' genome-scale metabolic model is ''i''AF1260, published in 2007<ref name='PMID:17593909'/>. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the [http://www.ecocyc.org/ EcoCyc] database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the ''E. coli'' metabolic model, ''i''AF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. ''i''AF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution<ref name='PMID:18536691'/>.

=== ''E. coli'' core model ===
The core ''E. coli'' model is a small-scale model of the central metabolism of ''E. coli''. It is a modified subset of the ''i''AF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.

== Links ==
''i''JR904 and ''i''AF1260 are available from the [http://bigg.ucsd.edu/ BiGG] database.

The core ''E. coli'' model is available [http://systemsbiology.ucsd.edu/Downloads/E_coli_Core here].

The COBRA Toolbox, a Matlab toolbox for analyzing constraint-based models, is available [http://systemsbiology.ucsd.edu/Downloads/Cobra_Toolbox here].

== References ==
{{RefHelp}}
<references/>
[[Category:Bioinformatics Tools]]

Metabolic Network Reconstructions

2010-03-05T21:28:36Z

JOrth: New Template:Bioinformatics_Tools Page

{{Pagetop}} 
{{See|Help:Bioinformatics Tools|tools}}

== Short Description ==
{{UserContentHelp}}

== Links ==
Links to sites that run the software or where it can be obtained

== Requirements ==
Hardware/Software requirements, if applicable

== User notes ==

== See Also ==

== References ==
{{RefHelp}}
<references/>
[[Category:Bioinformatics Tools]]