| Title: | Constraint based modeling in R using metabolic reconstruction databases |
|---|---|
| Description: | This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. |
| Authors: | Anand K. Gavai, Hannes Hettling |
| Maintainer: | Anand K. Gavai <[email protected]>, Hannes Hettling <[email protected]> |
| License: | file LICENSE |
| Version: | 1.43.0 |
| Built: | 2024-10-30 04:19:34 UTC |
| Source: | https://github.com/bioc/BiGGR |
This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package aids in performing flux balance analysis (FBA). Metabolic networks and estimated fluxes can be visualized using hypergraphs.
| Package: | BiGGR |
| Type: | Package |
| Version: | 0.99.0 |
| Date: | 2013-07-10 |
| Depends: | R (>= 2.14.0), rsbml, hyperdraw, LIM |
| Imports: | hypergraph |
| License: | GPL (>=2) |
| URL: | http://www.bioconductor.org/ |
| Copyright: | see inst/COPYRIGHTS for the license of the BiGG database |
| biocViews: | NetworkAnalysis, Visualization, Metabolomics |
| LazyLoad: | yes |
| Packaged: | 2013-08-05 12:44:08 UTC; hettling |
| Built: | R 3.0.0; ; 2013-08-05 12:44:22 UTC; unix |
Index:
E.coli_iAF1260 Ecoli dataset with ORFs and thermodynamic information E.coli_iJR904 Ecoli genome-scale model Glycolysis Metabolic reconstruction of Glycolysis pathway H.pylori_ilT341 H.pylori in silico genome-scale characterization of single and double deletion mutants H.sapiens_Recon_1 Reconstruction of human metabolism from the BiGG database M.barkeri_iAF692 Metabolic reconstruction of M.barkeri from the BiGG database Recon2 Human metabolic reconstruction Recon2 S.aureus_iSB619 Metabolic reconstruction of S.aureus from the BiGG database S.cerevisiae_iND750 Metabolic reconstruction of S.cerevisiae from the BiGG database buildSBMLFromBiGG Build an SBML model from a given reactions file obtained from the BiGG database buildSBMLFromGenes Build an SBML model for specific genes in a given database buildSBMLFromPathways Build an SBML model for specific pathway(s) in a given database buildSBMLFromReactionIDs Build an SBML model for specific reactions in a given database createLIMFromBiGG Create a LIM model object from a BiGG database file createLIMFromSBML Create a LIM model object from an SBML file extractGeneAssociations Extract informations on genes from a given database extractPathways Extract all pathways from given database getPathwaysForSBML Extract all pathways from a database that are relevant for a given SBML model getRates Get Optimized Rates sbml2hyperdraw Returns a graph representation of an SBML model
Further information is available in the following vignettes:
BiGGR |
BiGGR (source, pdf) |
Anand K. Gavai, Hannes Hettling
Maintainer: Anand K. Gavai <[email protected]>, Hannes Hettling <[email protected]>
rsbml
# library("BiGGR") library(help="BiGGR") ##load reaction identifiers from package examples file.name <- system.file("extdata", "Glycolysis_TCA_recon1_reactionIDs.txt", package="BiGGR") reaction.ids <- scan(file.name, what=" ") ##load database data("H.sapiens_Recon_1") ##build SBML model sbml.model <- buildSBMLFromReactionIDs(reaction.ids, H.sapiens_Recon_1) ##following term is to be maximized maximize <- "R_ATPS4m - R_NDPK1m - R_HEX1 - R_PFK - R_PGK + R_PYK" ##specify the external metabolites of the system externals <- c("M_glc_DASH_D_e", "M_lac_DASH_L_e", "M_ala_DASH_L_e", "M_gln_DASH_L_c", "M_h2o_e", "M_co2_e", "M_o2_e", "M_h_e", "M_pi_c", "M_o2s_m", "M_nh4_m", "M_adp_c", "M_atp_c", "M_nadp_c", "M_nadph_c", "M_h_c") ##specify the values of following fluxes: ##R_GLCt1r=0.4, R_O2t=2.4, R_L_LACt2r=R_GLNtm=0 equation.vars <- c("R_GLCt1r", "R_O2t", "R_L_LACt2r", "R_GLNtm") equation.values <- c(0.4, 2.4, 0.0, 0.0) eqns <- list(equation.vars, equation.values) ##create LIM file limfile.name <- tempfile() createLIMFromSBML(sbml.model, maximize, equations=eqns, externals=externals, file.name=limfile.name) rates <- getRates(limfile.name) relevant.species <- c("M_glc_DASH_D_c", "M_g6p_c", "M_f6p_c", "M_fdp_c", "M_dhap_c", "M_g3p_c", "M_13dpg_c", "M_3pg_c", "M_2pg_c", "M_pep_c", "M_pyr_c") ##generate graphical representation hd <- sbml2hyperdraw(sbml.model, rates=rates, relevant.species=relevant.species, layoutType="dot", plt.margins=c(20, 0, 20, 0)) ##plot hypergraph plot(hd)# library("BiGGR") library(help="BiGGR") ##load reaction identifiers from package examples file.name <- system.file("extdata", "Glycolysis_TCA_recon1_reactionIDs.txt", package="BiGGR") reaction.ids <- scan(file.name, what=" ") ##load database data("H.sapiens_Recon_1") ##build SBML model sbml.model <- buildSBMLFromReactionIDs(reaction.ids, H.sapiens_Recon_1) ##following term is to be maximized maximize <- "R_ATPS4m - R_NDPK1m - R_HEX1 - R_PFK - R_PGK + R_PYK" ##specify the external metabolites of the system externals <- c("M_glc_DASH_D_e", "M_lac_DASH_L_e", "M_ala_DASH_L_e", "M_gln_DASH_L_c", "M_h2o_e", "M_co2_e", "M_o2_e", "M_h_e", "M_pi_c", "M_o2s_m", "M_nh4_m", "M_adp_c", "M_atp_c", "M_nadp_c", "M_nadph_c", "M_h_c") ##specify the values of following fluxes: ##R_GLCt1r=0.4, R_O2t=2.4, R_L_LACt2r=R_GLNtm=0 equation.vars <- c("R_GLCt1r", "R_O2t", "R_L_LACt2r", "R_GLNtm") equation.values <- c(0.4, 2.4, 0.0, 0.0) eqns <- list(equation.vars, equation.values) ##create LIM file limfile.name <- tempfile() createLIMFromSBML(sbml.model, maximize, equations=eqns, externals=externals, file.name=limfile.name) rates <- getRates(limfile.name) relevant.species <- c("M_glc_DASH_D_c", "M_g6p_c", "M_f6p_c", "M_fdp_c", "M_dhap_c", "M_g3p_c", "M_13dpg_c", "M_3pg_c", "M_2pg_c", "M_pep_c", "M_pyr_c") ##generate graphical representation hd <- sbml2hyperdraw(sbml.model, rates=rates, relevant.species=relevant.species, layoutType="dot", plt.margins=c(20, 0, 20, 0)) ##plot hypergraph plot(hd)
Creates an SBML model containing all species, reactions and compartments that occur in a reactions file obtained from the BiGG database.
buildSBMLFromBiGG(reactions.filename, model.id=character(0), model.name=character(0))buildSBMLFromBiGG(reactions.filename, model.id=character(0), model.name=character(0))
reactions.filename |
name of the file containing the reactions extracted from BiGG |
model.id |
id for the SBML model created by the
function. Defaults to |
model.name |
name for the SBML model created by the
function. Defaults to |
a rsbml Model object containing all reactions, species and
compartments that are associated with the reactions in the given input file.
Note that it can be the case that species can be present in multiple compartments.
In order to avoid any ambiguities in the model returned by the
function, each species identifier is composed of the
species identifier given in the reactions file and the compartment
identifier, joined by "_". Example: adp in compartment c (cytosol) has
the id adp_c.
Anand Gavai, Hannes Hettling
Schellenberger, J., Park, J. O., Conrad, T. C., and Palsson, B. , BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions, BMC Bioinformatics, 11:213, (2010). http://bigg.ucsd.edu/
##build model from file Reactions.txt from the package examples path <- system.file("extdata", "Reactions.txt", package="BiGGR") model <- buildSBMLFromBiGG(path, model.id="myid")##build model from file Reactions.txt from the package examples path <- system.file("extdata", "Reactions.txt", package="BiGGR") model <- buildSBMLFromBiGG(path, model.id="myid")
Creates an SBML model containing all species, reactions and compartments that are associated with (a) specific gene(s) in the database document (e.g. Recon2) passed as an argument.
buildSBMLFromGenes(query, database, logical.fun="any")buildSBMLFromGenes(query, database, logical.fun="any")
query |
a |
database |
an object of class |
logical.fun |
function which specifies the logical
relation of the query genes within the reactions
(e.g. |
The function all as argument logical.fun
would mean that all genes in the query have to be associated with
a certain reaction from the database in order to be included in the returned
model. The default any means that a reaction is included if any
of the query genes are associated with it. Custom functions are
possible if they take a vector of type logical as an argument
and return a logical. The argument of logical.fun is a
vector of type logical having the same length as the
query and for each gene the value is TRUE if it is associated
with a specific reaction. See 'examples' section for an example of a
custom function as logical.fun.
a rsbml Model object containing all reactions, species and
compartments that are present in the database and are associated
with the query
gene(s) or NULL if none of the genes in the database match the query.
If the reactions in the database document provided in the argument
database do not contain any "<notes>" with tags with gene information
indicated by the string "GENE*ASSOCIATION" (the star stands for any
character),
no gene association information can be
extracted and thus the returned SBML mdel is empty..
Anand Gavai, Hannes Hettling
Thiele, I. et al. Nat Biotech, 2013
buildSBMLFromPathways
extractGeneAssociations
##Query genes in Recon 2 database data("Recon2") database <- Recon2 m1 <- buildSBMLFromGenes("8884.1", database) m2 <- buildSBMLFromGenes(c("8884.1", "6509.1"), database) ##different databases data(H.pylori_ilT341) database <- H.pylori_ilT341 m3 <- buildSBMLFromGenes("HP0069", database) data(M.barkeri_iAF692) database <- M.barkeri_iAF692 m4 <- buildSBMLFromGenes(c("MBd0456", "MBd4814", "MBd4098"), database) data(S.aureus_iSB619) database <- S.aureus_iSB619 m5 <- buildSBMLFromGenes(c("SA0594", "SA1599", "SA0950", "SA0259"), database) database <- Recon2 query <- c("218.1", "223.1") m6 <- buildSBMLFromGenes(query, database) m7 <- buildSBMLFromGenes(query, database, logical.fun="all") ##m6 has more reactions than m7 ## because m7 has only reactions which match both genes in the query length(m6@reactions) > length(m7@reactions) ##Custom logical function: Get model with all reactions ## which are not associated with the query gene m8 <- buildSBMLFromGenes(query, database, logical.fun=function(x)!any(x))##Query genes in Recon 2 database data("Recon2") database <- Recon2 m1 <- buildSBMLFromGenes("8884.1", database) m2 <- buildSBMLFromGenes(c("8884.1", "6509.1"), database) ##different databases data(H.pylori_ilT341) database <- H.pylori_ilT341 m3 <- buildSBMLFromGenes("HP0069", database) data(M.barkeri_iAF692) database <- M.barkeri_iAF692 m4 <- buildSBMLFromGenes(c("MBd0456", "MBd4814", "MBd4098"), database) data(S.aureus_iSB619) database <- S.aureus_iSB619 m5 <- buildSBMLFromGenes(c("SA0594", "SA1599", "SA0950", "SA0259"), database) database <- Recon2 query <- c("218.1", "223.1") m6 <- buildSBMLFromGenes(query, database) m7 <- buildSBMLFromGenes(query, database, logical.fun="all") ##m6 has more reactions than m7 ## because m7 has only reactions which match both genes in the query length(m6@reactions) > length(m7@reactions) ##Custom logical function: Get model with all reactions ## which are not associated with the query gene m8 <- buildSBMLFromGenes(query, database, logical.fun=function(x)!any(x))
Creates an SBML model containing all species, reactions and compartments that are part of (a) given pathway(s) in the database document (e.g. Recon2) passed as an argument.
buildSBMLFromPathways(query, database, match.exact=TRUE)buildSBMLFromPathways(query, database, match.exact=TRUE)
query |
a |
database |
an object of class |
match.exact |
|
a rsbml Model object containing all reactions, species and
compartments that are present in the database for the query
pathway(s) or NULL if none of the pathways in the database match the query.
If the reactions in the database document provided in the argument
database do not contain any "<notes>" with tags with pathway information
indicated by the string "SUBSYSTEM", no pathway information can be
extracted and thus the SBML model returned will be empty.
Anand Gavai, Hannes Hettling
Thiele, I. et al. Nat Biotech, 2013
data("Recon2") database <- Recon2 ##Get Model for specific pathway m1 <- buildSBMLFromPathways("Arginine and Proline Metabolism", database) ##Get Model for specific pathway "Metabolism": does not exist! m2 <- buildSBMLFromPathways("Metabolism", database) ##Get model of all pathways which contain keyword "metabolism" m3 <- buildSBMLFromPathways("Metabolism", database, match.exact=FALSE) ##Multi-query: query <- c("Transport, endoplasmic reticular", "Arginine and Proline Metabolism") m4 = buildSBMLFromPathways(query, database) m5 = buildSBMLFromPathways(query[1], database) length(m4@species) length(m5@species) ##different database data(H.pylori_ilT341) database <- H.pylori_ilT341 m7 <- buildSBMLFromPathways("Metabolism", database, match.exact=FALSE)data("Recon2") database <- Recon2 ##Get Model for specific pathway m1 <- buildSBMLFromPathways("Arginine and Proline Metabolism", database) ##Get Model for specific pathway "Metabolism": does not exist! m2 <- buildSBMLFromPathways("Metabolism", database) ##Get model of all pathways which contain keyword "metabolism" m3 <- buildSBMLFromPathways("Metabolism", database, match.exact=FALSE) ##Multi-query: query <- c("Transport, endoplasmic reticular", "Arginine and Proline Metabolism") m4 = buildSBMLFromPathways(query, database) m5 = buildSBMLFromPathways(query[1], database) length(m4@species) length(m5@species) ##different database data(H.pylori_ilT341) database <- H.pylori_ilT341 m7 <- buildSBMLFromPathways("Metabolism", database, match.exact=FALSE)
Creates an SBML model containing all species, reactions and compartments that are associated with a number of reaction identifiers in the database document (e.g. Recon2) passed as an argument.
buildSBMLFromReactionIDs(reaction.ids, database)buildSBMLFromReactionIDs(reaction.ids, database)
reaction.ids |
a |
database |
an object of class |
a rsbml Model object containing all reactions, species and
compartments that are present in the database for the query
reaction(s) or NULL if none of the query reaction IDs is found in the database.
Anand Gavai, Hannes Hettling
Thiele, I. et al. Nat Biotech, 2013
##get list of reactions with Recon 2 identifiers from examples path <- system.file("extdata", "Glycolysis_TCA_recon2_reactionIDs.txt", package="BiGGR") reaction.ids <- scan(path, what=" ") data("Recon2") model <- buildSBMLFromReactionIDs(reaction.ids, Recon2)##get list of reactions with Recon 2 identifiers from examples path <- system.file("extdata", "Glycolysis_TCA_recon2_reactionIDs.txt", package="BiGGR") reaction.ids <- scan(path, what=" ") data("Recon2") model <- buildSBMLFromReactionIDs(reaction.ids, Recon2)
Creates a LIM model object from a file containing reactions extreacted from BiGG to be run for simulations of metabolic fluxes
createLIMFromBiGG(reactions.filename, ...)createLIMFromBiGG(reactions.filename, ...)
reactions.filename |
file which contains the reactions extracted from the BiGG database |
... |
arguments passed to createLIMfromSBML |
none
Anand K. Gavai <[email protected]>, Hannes hettling <[email protected]>
Soetaert K, van Oevelen D (2009). LIM: Linear Inverse Model examples and solution methods. R package version 1.3
##maximize flux for reaction R_PYK maximize <- "R_PYK" ##setting equality constraint R_HEX = 1 equation_var <- "R_HEX1" equation_value <- 1 eq <- list(equation_var, equation_value) ##range of possible fluxes for R_PYK constraint <- list("R_PYK", 0, 1000) externals <- c("glc_c", "pyr_c", "h_c","nad_c", "nadh_c", "pi_c", "fad_m", "fadh2_m", "o2_c", "adp_c", "atp_c", "nadp_c", "co2_c", "o2_c", "gdp_c", "gtp_c") ##build LIM model from reactions file in package examples path <- system.file("extdata", "Reactions.txt", package="BiGGR") limfile.name <- tempfile() createLIMFromBiGG(path, maximize, equations=eq, constraints=constraint, externals=externals, file.name=limfile.name)##maximize flux for reaction R_PYK maximize <- "R_PYK" ##setting equality constraint R_HEX = 1 equation_var <- "R_HEX1" equation_value <- 1 eq <- list(equation_var, equation_value) ##range of possible fluxes for R_PYK constraint <- list("R_PYK", 0, 1000) externals <- c("glc_c", "pyr_c", "h_c","nad_c", "nadh_c", "pi_c", "fad_m", "fadh2_m", "o2_c", "adp_c", "atp_c", "nadp_c", "co2_c", "o2_c", "gdp_c", "gtp_c") ##build LIM model from reactions file in package examples path <- system.file("extdata", "Reactions.txt", package="BiGGR") limfile.name <- tempfile() createLIMFromBiGG(path, maximize, equations=eq, constraints=constraint, externals=externals, file.name=limfile.name)
creates a model file to be run for simulations of metabolic fluxes
createLIMFromSBML(model, maximize, equations=NULL, inequalities=NULL, constraints=NULL, externals=NULL, file.name="model.lim")createLIMFromSBML(model, maximize, equations=NULL, inequalities=NULL, constraints=NULL, externals=NULL, file.name="model.lim")
model |
an SBML object of reactions/metabolites participating in a metabolic pathway. |
maximize |
a character vector consisting the tag of the reaction(s) to be maximized or minimized |
equations |
a |
inequalities |
a |
constraints |
a |
externals |
a character vector of metabolites as provided by the user for speficific pathways for which FBA (flux balance analysis needs to be performed) |
file.name |
a character string specifying the name of the LIM file created by the function. |
A model file with with extension ".lim" is created
none
Anand K. Gavai <[email protected]>, Hannes Hettling <[email protected]>
Soetaert K, van Oevelen D (2009). LIM: Linear Inverse Model examples and solution methods. R package version 1.3
##Create a LIM model file from a reactions file in the examples path <- system.file("extdata", "Glycolysis_TCA_recon1_reactionIDs.txt", package="BiGGR") reaction.ids <- scan(path, what=" ") data("H.sapiens_Recon_1") sbml.model <- buildSBMLFromReactionIDs(reaction.ids, H.sapiens_Recon_1) maximize <- c("R_ATPS4m - R_NDPK1m - R_HEX1 - R_PFK - R_PGK + R_PYK") externals <- c("M_glc_DASH_D_e", "M_lac_DASH_L_e", "M_ala_DASH_L_e", "M_gln_DASH_L_c", "M_h2o_e", "M_co2_e", "M_o2_e", "M_h_e", "M_pi_c", "M_o2s_m", "M_nh4_m", "M_adp_c", "M_atp_c", "M_nadp_c", "M_nadph_c", "M_h_c") equation.vars <- c("R_GLCt1r", "R_O2t", "R_L_LACt2r", "R_GLNtm") equation.values <- c(0.4, 2.4, 0.0, 0.0) eqns <- list(equation.vars, equation.values) constraints <- list(c("R_GLCt1r", "R_CYOOm3"), c(-1000, -1000), c(1000, 1000)) limfile.name <- tempfile() createLIMFromSBML(sbml.model, maximize, equations=eqns, inequalities=list("R_O2t", 2.4, "<="), constraints=constraints, externals=externals, file.name=limfile.name)##Create a LIM model file from a reactions file in the examples path <- system.file("extdata", "Glycolysis_TCA_recon1_reactionIDs.txt", package="BiGGR") reaction.ids <- scan(path, what=" ") data("H.sapiens_Recon_1") sbml.model <- buildSBMLFromReactionIDs(reaction.ids, H.sapiens_Recon_1) maximize <- c("R_ATPS4m - R_NDPK1m - R_HEX1 - R_PFK - R_PGK + R_PYK") externals <- c("M_glc_DASH_D_e", "M_lac_DASH_L_e", "M_ala_DASH_L_e", "M_gln_DASH_L_c", "M_h2o_e", "M_co2_e", "M_o2_e", "M_h_e", "M_pi_c", "M_o2s_m", "M_nh4_m", "M_adp_c", "M_atp_c", "M_nadp_c", "M_nadph_c", "M_h_c") equation.vars <- c("R_GLCt1r", "R_O2t", "R_L_LACt2r", "R_GLNtm") equation.values <- c(0.4, 2.4, 0.0, 0.0) eqns <- list(equation.vars, equation.values) constraints <- list(c("R_GLCt1r", "R_CYOOm3"), c(-1000, -1000), c(1000, 1000)) limfile.name <- tempfile() createLIMFromSBML(sbml.model, maximize, equations=eqns, inequalities=list("R_O2t", 2.4, "<="), constraints=constraints, externals=externals, file.name=limfile.name)
A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655
that accounts for 1260 ORFs and thermodynamic information.
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(E.coli_iAF1260)data(E.coli_iAF1260)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Feist, A.M., Henry, C.S., Reed, J.L., Krummenacker, M., Joyce, A.R., Karp, P.D., Broadbelt, L.J., Hatzimanikatis, V., Palsson, B.O., A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information, olecular Systems Biology, 3:121 (2007)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) E.coli_iAF1260 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(E.coli_iAF1260) model <- E.coli_iAF1260@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) E.coli_iAF1260 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(E.coli_iAF1260) model <- E.coli_iAF1260@model ##get all reaction identifiers sapply(model@reactions, id)
An expanded genome-scale model of Escherichia coli K-12 (iJR904
GSM/GPR). The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(E.coli_iJR904)data(E.coli_iJR904)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Reed, J.L., Vo, T.D., Schilling, C.H., and Palsson, B.O., An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR), Genome Biology, 4(9): R54.1-R54.12 (2003).
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) E.coli_iJR904 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(E.coli_iJR904) model <- E.coli_iJR904@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) E.coli_iJR904 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(E.coli_iJR904) model <- E.coli_iJR904@model ##get all reaction identifiers sapply(model@reactions, id)
A metabolic reconstruction for Escherichia from text books.
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(E.coli_textbook)data(E.coli_textbook)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Feist, A.M., Henry, C.S., Reed, J.L., Krummenacker, M., Joyce, A.R., Karp, P.D., Broadbelt, L.J., Hatzimanikatis, V., Palsson, B.O., A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information, olecular Systems Biology, 3:121 (2007)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) E.coli_textbook <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(E.coli_textbook) model <- E.coli_textbook@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) E.coli_textbook <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(E.coli_textbook) model <- E.coli_textbook@model ##get all reaction identifiers sapply(model@reactions, id)
Extracts all information on genes associated to reactions from an rsbml document containing a metabolic reconstruction database (e.g. Recon2). The associated information is parsed from the "<notes>" tag of each reaction's SBML representation.
extractGeneAssociations(database)extractGeneAssociations(database)
database |
an object of class |
a list with length being the number of reactions
in the database passed as argument each entry containing a
character containing the assiciated gene identifiers
and the reaction IDs as names. For reactions without gene
annotation, the list will contain NA.
If the reactions in the database document provided in the argument
database do not contain any "<notes>" with tags with gene information
indicated by the string "GENE*ASSOCIATION" (the star stands for any
character),
no gene association information can be
extracted and thus the returned SBML mdel is empty..
Anand Gavai, Hannes Hettling
Thiele, I. et al. Nat Biotech, 2013
data("Recon2") database <- Recon2 gene.info <- extractGeneAssociations(database)data("Recon2") database <- Recon2 gene.info <- extractGeneAssociations(database)
Extracts all pathway information from an rsbml document containing a metabolic reconstruction database (e.g. Recon2). The pathway information is parsed from the "<notes>" tag of each reaction.
extractPathways(database)extractPathways(database)
database |
an object of class |
a list with length being the number of reactions
in the database passed as argument each entry containing a character with the pathway
information and the reaction IDs as names. For reactions without
pathway annotation, the list will contain NA.
If the reactions in the database document provided in the argument
database do not contain any "<notes>" with tags with pathway information
indicated by the string "SUBSYSTEM", no pathway information can be extracted.
Anand Gavai, Hannes Hettling
Thiele, I. et al. Nat Biotech, 2013
buildSBMLFromPathways
getPathwaysForSBML
data(Recon2) pathways.recon2 <- extractPathways(Recon2)data(Recon2) pathways.recon2 <- extractPathways(Recon2)
Extracts all pathway information from an rsbml document containing a metabolic reconstruction database (e.g. Recon2) and returns the subset of these pathways that is associated with the reactions in the given model. The pathway information is parsed from the "<notes>" tag of each reaction.
getPathwaysForSBML(model, database)getPathwaysForSBML(model, database)
database |
an rsbml object of class |
model |
an rsbml object of class |
A vector of type character that contains all the pathways
relevant for the given model according to the specified database. Note
that duplicate pathways do not appear twice in the return value.
If the reactions in the database document provided in the argument
database do not contain any "<notes>" with tags with pathway information
indicated by the string "SUBSYSTEM", no pathway information can be extracted.
Anand Gavai, Hannes Hettling
Thiele, I. et al. Nat Biotech, 2013
buildSBMLFromPathways
buildSBMLFromGenes
extractPathways
##Build a model from query genes data("Recon2") database <- Recon2 query <- c("218.1", "223.1") ##query gene identifiers m <- buildSBMLFromGenes(query, database) ##extract all pathways for that model getPathwaysForSBML(m, database)##Build a model from query genes data("Recon2") database <- Recon2 query <- c("218.1", "223.1") ##query gene identifiers m <- buildSBMLFromGenes(query, database) ##extract all pathways for that model getPathwaysForSBML(m, database)
getRates takes the model file as the argument and based on the description of the model file generates flux values for "minimum" or "maximum" reaction rates
getRates(modelFile)getRates(modelFile)
modelFile |
The path to a LIM model file as generated for
instance from the functions |
The value returned is one dimentional numeric vector of flux rates for each reaction
Anand K. Gavai <[email protected]>
data("Glycolysis") rates <-getRates(Glycolysis) ratesdata("Glycolysis") rates <-getRates(Glycolysis) rates
Model of Glycolysis pathway
GlycolysisGlycolysis
A LIM model file created as an example
Continuous gene expression levels are mapped from genes to reactions using the gene-protein-reaction (GPR) association rules as found in ReconX databases. The expression level of reactions catalyzed by enzyme complexes (and operator) can be set to the minimum,maximum,mean and median functions. Similarly expression level of the associated genes, and the expression level of reactions catalyzed by isoenzymes (or operator) can also be set to either minimum,maximum,mean and median functions for the associated genes.
Operator Precedence: "AND" followed by "OR"
gprMapping(gene_express,react_gene_map,OR=c("mean","median","min","max"),AND=c("min","max","mean","median"))gprMapping(gene_express,react_gene_map,OR=c("mean","median","min","max"),AND=c("min","max","mean","median"))
gene_express |
The path to a gene expression file with three columns gene_symbol,entrez_id and foldchanges |
react_gene_map |
Database file created from ReconX database using functions such as |
OR |
Takes values from statistical functions such as |
AND |
Takes values from statistical functions such as |
Returns a dataframe with Reaction_id, GPR formulae and Calculated values
Anand K. Gavai <[email protected]>, Hannes Hettling
# Read gene expression data file <- system.file("extdata", "Gene_Symbol_Entrez_Foldchanges.csv", package="BiGGR") gene_express<-read.csv(file,header=TRUE) data(Recon2) gene.info <- extractGeneAssociations(Recon2) gene.info<-do.call(rbind.data.frame,gene.info) colnames(gene.info)<-c("GPR") gene.info$react_id<-row.names(gene.info) gene.info<-gene.info[,c(2,1)] rownames(gene.info)<-NULL react_gene_map<-rmvSpliceVariant(gene.info) gpr.map<-gprMapping(gene_express,react_gene_map,OR="mean",AND="min")# Read gene expression data file <- system.file("extdata", "Gene_Symbol_Entrez_Foldchanges.csv", package="BiGGR") gene_express<-read.csv(file,header=TRUE) data(Recon2) gene.info <- extractGeneAssociations(Recon2) gene.info<-do.call(rbind.data.frame,gene.info) colnames(gene.info)<-c("GPR") gene.info$react_id<-row.names(gene.info) gene.info<-gene.info[,c(2,1)] rownames(gene.info)<-NULL react_gene_map<-rmvSpliceVariant(gene.info) gpr.map<-gprMapping(gene_express,react_gene_map,OR="mean",AND="min")
Continuous gene expression levels are mapped from genes to reactions using the gene-protein-reaction (GPR) association rules as found in ReconX databases. These rules are comprised of AND and OR operators. This function ignores these rules and take average of all genes
gprMappingAvg(gene_express,react_gene_map)gprMappingAvg(gene_express,react_gene_map)
gene_express |
The path to a gene expression file with three columns gene_symbol,entrez_id and foldchanges |
react_gene_map |
Database file created from ReconX database using functions such as |
Returns a dataframe with Reaction_id, GPR formulae and average values
Anand K. Gavai <[email protected]>, Hannes Hettling
# Read gene expression data file <- system.file("extdata", "Gene_Symbol_Entrez_Foldchanges.csv", package="BiGGR") gene_express<-read.csv(file,header=TRUE) data(Recon2) gene.info <- extractGeneAssociations(Recon2) gene.info<-do.call(rbind.data.frame,gene.info) colnames(gene.info)<-c("GPR") gene.info$react_id<-row.names(gene.info) gene.info<-gene.info[,c(2,1)] rownames(gene.info)<-NULL react_gene_map<-rmvSpliceVariant(gene.info) gpr.map.avg<-gprMappingAvg(gene_express,react_gene_map)# Read gene expression data file <- system.file("extdata", "Gene_Symbol_Entrez_Foldchanges.csv", package="BiGGR") gene_express<-read.csv(file,header=TRUE) data(Recon2) gene.info <- extractGeneAssociations(Recon2) gene.info<-do.call(rbind.data.frame,gene.info) colnames(gene.info)<-c("GPR") gene.info$react_id<-row.names(gene.info) gene.info<-gene.info[,c(2,1)] rownames(gene.info)<-NULL react_gene_map<-rmvSpliceVariant(gene.info) gpr.map.avg<-gprMappingAvg(gene_express,react_gene_map)
An Expanded Metabolic Reconstruction of Helicobacter pylori (iIT341
GSM/GPR): An in silico genome-scale characterization of single and
double deletion mutants.
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(H.pylori_ilT341)data(H.pylori_ilT341)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Thiele, I., Vo, T.D., Price, N.D. and Palsson, B., An Expanded Metabolic Reconstruction of Helicobacter pylori (iIT341 GSM/GPR): An in silico genome-scale characterization of single and double deletion mutants, Journal of Bacteriology, 187(16): 5818-5830 (2005)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) H.pylori_ilT341 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(H.pylori_ilT341) model <- H.pylori_ilT341@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) H.pylori_ilT341 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(H.pylori_ilT341) model <- H.pylori_ilT341@model ##get all reaction identifiers sapply(model@reactions, id)
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(H.sapiens_Recon_1)data(H.sapiens_Recon_1)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Duarte, N.D., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., Palsson, B. O., Global reconstruction of the human metabolic network based on genomic and bibliomic data, Proc. Nat Acad. Sci. 104(6):1777-82 (2007)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) H.sapiens_Recon_1 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(H.sapiens_Recon_1) model <- H.sapiens_Recon_1@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) H.sapiens_Recon_1 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(H.sapiens_Recon_1) model <- H.sapiens_Recon_1@model ##get all reaction identifiers sapply(model@reactions, id)
These data were taken from a publication of Lying-Tunell et al. (1980) reporting cerebral metabolic uptakes and release rates in older subjects (n=5). The data were published as micromole/kg/min, but converted to mmole/min for this dataset (see details).
data(lying.tunell.data)data(lying.tunell.data)
An object of class data.frame
Data were taken from table 2 (page 271) of the publication. From the given median and range values, mean and standard deviation was estimated using a method by Hozo et al. (2005). Units were converted from micromole/kg/min to mmole/min assuming a brain mass of 1.4kg.
http://www.ncbi.nlm.nih.gov/pubmed/7468149
Lying-Tunell U, Lindblad BS, Malmlund HO, Persson B: Cerebral blood flow and metabolic rate of oxygen, glucose, lactate, pyruvate, ketone bodies and amino acids. Acta Neurol Scand 1980, 62:265-75.
Hozo SP, Djulbegovic B, Hozo I: Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol 2005, 5:13.
## Not run: ##The dataset was generated as follows: ##Uptake rates given in micromole/kg/min from Lying-Tunell (1980), n=5 old patients ##converted to mmol/min and assuming a brain mass of 1.4 kg brain.mass <- 1.4 ## in kg oxygen.median <- 1679 * brain.mass / 1000 oxygen.range <- c(1184, 1872) * brain.mass / 1000 glucose.median <- 203 * brain.mass / 1000 glucose.range <- c(187, 321) * brain.mass / 1000 lactate.median <- -9.2 * brain.mass / 1000 lactate.range <- c(-68, 7.9) * brain.mass / 1000 pyruvate.median <- -2.4 * brain.mass / 1000 pyruvate.range <- c(-10, -brain.mass) * brain.mass / 1000 glutamine.median <- -11 * brain.mass / 1000 glutamine.range <- c(-61, 22) * brain.mass / 1000 ##This implements eq 4 from Hozo et al. to estimate ##sample mean from median and range ##m: median, a: minimum, b: maximum, n: number of samples estimate.sample.mean <- function(m, a, b, n) (a + 2*m + b)/4 + (a-2*m + b)/(4*n) ##This implements eq 16 from Hozo et al. to estimate ##sample standard deviation from median and range ##m: median, a: minimum, b: maximum, n: number of samples estimate.sample.sd <- function(m, a, b, n) sqrt((((a - 2*m + b)^2)/4 + (b-a)^2)/12) ##Calculate mean and standard deviation from median and range values using the method of Hoxo et al. oxygen.mean <- estimate.sample.mean(oxygen.median, oxygen.range[1], oxygen.range[2], 5) oxygen.sd <- estimate.sample.sd(oxygen.median, oxygen.range[1], oxygen.range[2], 5) glucose.mean <- estimate.sample.mean(glucose.median, glucose.range[1], glucose.range[2], 5) glucose.sd <- estimate.sample.sd(glucose.median, glucose.range[1], glucose.range[2], 5) lactate.mean <- estimate.sample.mean(lactate.median, lactate.range[1], lactate.range[2], 5) lactate.sd <- estimate.sample.sd(lactate.median, lactate.range[1], lactate.range[2], 5) pyruvate.mean <- estimate.sample.mean(pyruvate.median, pyruvate.range[1], pyruvate.range[2], 5) pyruvate.sd <- estimate.sample.sd(pyruvate.median, pyruvate.range[1], pyruvate.range[2], 5) glutamine.mean <- estimate.sample.mean(glutamine.median, glutamine.range[1], glutamine.range[2], 5) glutamine.sd <- estimate.sample.sd(glutamine.median, glutamine.range[1], glutamine.range[2], 5) lying.tunell.data <- data.frame(median=c(oxygen.median, glucose.median, lactate.median, pyruvate.median, glutamine.median), mean=c(oxygen.mean, glucose.mean, lactate.mean, pyruvate.mean, glutamine.mean), sd=c(oxygen.sd, glucose.sd, lactate.sd, pyruvate.sd, glutamine.sd), low=c(oxygen.range[1], glucose.range[1], lactate.range[1], pyruvate.range[1], glutamine.range[1]), high=c(oxygen.range[2], glucose.range[2], lactate.range[2], pyruvate.range[2], glutamine.range[2]), row.names=c("o2", "glucose", "lactate", "pyruvate", "glutamine")) ## End(Not run) ##load data data(lying.tunell.data) ##get median value for glucose uptake lying.tunell.data["glucose", "median"]## Not run: ##The dataset was generated as follows: ##Uptake rates given in micromole/kg/min from Lying-Tunell (1980), n=5 old patients ##converted to mmol/min and assuming a brain mass of 1.4 kg brain.mass <- 1.4 ## in kg oxygen.median <- 1679 * brain.mass / 1000 oxygen.range <- c(1184, 1872) * brain.mass / 1000 glucose.median <- 203 * brain.mass / 1000 glucose.range <- c(187, 321) * brain.mass / 1000 lactate.median <- -9.2 * brain.mass / 1000 lactate.range <- c(-68, 7.9) * brain.mass / 1000 pyruvate.median <- -2.4 * brain.mass / 1000 pyruvate.range <- c(-10, -brain.mass) * brain.mass / 1000 glutamine.median <- -11 * brain.mass / 1000 glutamine.range <- c(-61, 22) * brain.mass / 1000 ##This implements eq 4 from Hozo et al. to estimate ##sample mean from median and range ##m: median, a: minimum, b: maximum, n: number of samples estimate.sample.mean <- function(m, a, b, n) (a + 2*m + b)/4 + (a-2*m + b)/(4*n) ##This implements eq 16 from Hozo et al. to estimate ##sample standard deviation from median and range ##m: median, a: minimum, b: maximum, n: number of samples estimate.sample.sd <- function(m, a, b, n) sqrt((((a - 2*m + b)^2)/4 + (b-a)^2)/12) ##Calculate mean and standard deviation from median and range values using the method of Hoxo et al. oxygen.mean <- estimate.sample.mean(oxygen.median, oxygen.range[1], oxygen.range[2], 5) oxygen.sd <- estimate.sample.sd(oxygen.median, oxygen.range[1], oxygen.range[2], 5) glucose.mean <- estimate.sample.mean(glucose.median, glucose.range[1], glucose.range[2], 5) glucose.sd <- estimate.sample.sd(glucose.median, glucose.range[1], glucose.range[2], 5) lactate.mean <- estimate.sample.mean(lactate.median, lactate.range[1], lactate.range[2], 5) lactate.sd <- estimate.sample.sd(lactate.median, lactate.range[1], lactate.range[2], 5) pyruvate.mean <- estimate.sample.mean(pyruvate.median, pyruvate.range[1], pyruvate.range[2], 5) pyruvate.sd <- estimate.sample.sd(pyruvate.median, pyruvate.range[1], pyruvate.range[2], 5) glutamine.mean <- estimate.sample.mean(glutamine.median, glutamine.range[1], glutamine.range[2], 5) glutamine.sd <- estimate.sample.sd(glutamine.median, glutamine.range[1], glutamine.range[2], 5) lying.tunell.data <- data.frame(median=c(oxygen.median, glucose.median, lactate.median, pyruvate.median, glutamine.median), mean=c(oxygen.mean, glucose.mean, lactate.mean, pyruvate.mean, glutamine.mean), sd=c(oxygen.sd, glucose.sd, lactate.sd, pyruvate.sd, glutamine.sd), low=c(oxygen.range[1], glucose.range[1], lactate.range[1], pyruvate.range[1], glutamine.range[1]), high=c(oxygen.range[2], glucose.range[2], lactate.range[2], pyruvate.range[2], glutamine.range[2]), row.names=c("o2", "glucose", "lactate", "pyruvate", "glutamine")) ## End(Not run) ##load data data(lying.tunell.data) ##get median value for glucose uptake lying.tunell.data["glucose", "median"]
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(M.barkeri_iAF692)data(M.barkeri_iAF692)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Feist, A.M., Scholten, J.C.M., Palsson, B.O., Brockman, F.J., and Ideker, T., "Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri", Molecular Systems Biology, 2(1):msb4100046-E1-E14 (2006)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) M.barkeri_iAF692 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(M.barkeri_iAF692) model <- M.barkeri_iAF692@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) M.barkeri_iAF692 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(M.barkeri_iAF692) model <- M.barkeri_iAF692@model ##get all reaction identifiers sapply(model@reactions, id)
A metabolic reconstruction for tuberculosis.
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(M.tuberculosis_iNJ661)data(M.tuberculosis_iNJ661)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Feist, A.M., Henry, C.S., Reed, J.L., Krummenacker, M., Joyce, A.R., Karp, P.D., Broadbelt, L.J., Hatzimanikatis, V., Palsson, B.O., A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information, olecular Systems Biology, 3:121 (2007)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) M.tuberculosis_iNJ661 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(M.tuberculosis_iNJ661) model <- M.tuberculosis_iNJ661@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) M.tuberculosis_iNJ661 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(M.tuberculosis_iNJ661) model <- M.tuberculosis_iNJ661@model ##get all reaction identifiers sapply(model@reactions, id)
A metabolic reconstruction for P. putida.
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(P.putida_iJN746)data(P.putida_iJN746)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Feist, A.M., Henry, C.S., Reed, J.L., Krummenacker, M., Joyce, A.R., Karp, P.D., Broadbelt, L.J., Hatzimanikatis, V., Palsson, B.O., A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information, olecular Systems Biology, 3:121 (2007)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) P.putida_iJN746 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(P.putida_iJN746) model <- P.putida_iJN746@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) P.putida_iJN746 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(P.putida_iJN746) model <- P.putida_iJN746@model ##get all reaction identifiers sapply(model@reactions, id)
The dataset was generated by downloading the SBML file of the
reconstruction (http://www.ebi.ac.uk/biomodels-main/MODEL1109130000) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(Recon2)data(Recon2)
An sbml object of class rsbml
http://www.ebi.ac.uk/biomodels-main/MODEL1109130000
Thiele I, Swainston N, et al.,"A community-driven global reconstruction of human metabolism", Nature Biotechnology 31, 419-425 (2013), doi:10.1038/nbt.2488
## Not run: ##The dataset was generated as follows: ##MODEL1109130000.xml was downloaded from http://www.ebi.ac.uk/biomodels-main/MODEL1109130000 ##Recon2 <- rsbml_read("MODEL1109130000.xml") ## End(Not run) ##load data and get all reaction IDs data(Recon2) model <- Recon2@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##MODEL1109130000.xml was downloaded from http://www.ebi.ac.uk/biomodels-main/MODEL1109130000 ##Recon2 <- rsbml_read("MODEL1109130000.xml") ## End(Not run) ##load data and get all reaction IDs data(Recon2) model <- Recon2@model ##get all reaction identifiers sapply(model@reactions, id)
Removes alternative splicing information from the database.
rmvSpliceVariant(gene.info)rmvSpliceVariant(gene.info)
gene.info |
A reaction gene maping from the ReconX database created from functions |
A n x 2 dimentional dataframe of Reaction-Gene(Entrez number) mapping from ReconX database
Anand Gavai <[email protected]>, Hannes Hettling
Thiele, I. et al. Nat Biotech, 2013
data(Recon2) gene.info <- extractGeneAssociations(Recon2) gene.info<-do.call(rbind.data.frame,gene.info) colnames(gene.info)<-c("GPR") gene.info$react_id<-row.names(gene.info) gene.info<-gene.info[,c(2,1)] rownames(gene.info)<-NULL react_gene_map<-rmvSpliceVariant(gene.info)data(Recon2) gene.info <- extractGeneAssociations(Recon2) gene.info<-do.call(rbind.data.frame,gene.info) colnames(gene.info)<-c("GPR") gene.info$react_id<-row.names(gene.info) gene.info<-gene.info[,c(2,1)] rownames(gene.info)<-NULL react_gene_map<-rmvSpliceVariant(gene.info)
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(S.aureus_iSB619)data(S.aureus_iSB619)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Becker, S.A. and Palsson, B.O., Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation, BMC Microbiology, 5(1):8 (2005)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) S.aureus_iSB619 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(S.aureus_iSB619) model <- S.aureus_iSB619@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) S.aureus_iSB619 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(S.aureus_iSB619) model <- S.aureus_iSB619@model ##get all reaction identifiers sapply(model@reactions, id)
The dataset was generated by downloading the SBML file of the
reconstruction (http://bigg.ucsd.edu/bigg/exportSelect.pl) which was subsequently converted
into an object of class SBML using the rsbml_read function
from the rsbml package.
data(S.cerevisiae_iND750)data(S.cerevisiae_iND750)
An sbml object of class rsbml
Note that the files in the BiGG database fail the unit
consistancy check of the rsbml_read function. To avoid
unit checking when creating SBML objects, the substance units in the reaction tags
were parsed out from the database SBML files (see example below).
http://bigg.ucsd.edu/bigg/exportSelect.pl
Duarte, N.C., Herrgard, M.J., and Palsson, B.O., " Reconstruction and Validation of Saccharomyces cerevisiae iND750, a Fully Compartmentalized Genome-scale Metabolic Model", Genome Research, 14: 1298-1309 (2004)
## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) S.cerevisiae_iND750 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(S.cerevisiae_iND750) model <- S.cerevisiae_iND750@model ##get all reaction identifiers sapply(model@reactions, id)## Not run: ##The dataset was generated as follows: ##SBML_export.xml was downloaded from http://bigg.ucsd.edu/bigg/exportSelect.pl ##and a newline was added at the end of the file file <- "SBML_export.xml" string <- paste(readLines(file), collapse="\n") ##Parse out units to avoid validation error string <- gsub("units=\".+?\"", "", string) S.cerevisiae_iND750 <- rsbml_read(text=string) ## End(Not run) ##load data and get all reaction IDs data(S.cerevisiae_iND750) model <- S.cerevisiae_iND750@model ##get all reaction identifiers sapply(model@reactions, id)
This function uses a Markov chain Monte Carlo algorithm to sample
an ensemble of flux vectors that satisfy the constrained posed by
the model. To account for inaccuracy in certain fluxes, the user can
specify uncertain fluxes and provide standard deviations. The function
uses the xsample function from the package limSolve.
sampleFluxEnsemble(model, uncertain.vars=NULL, iter=3000, ...)sampleFluxEnsemble(model, uncertain.vars=NULL, iter=3000, ...)
model |
Either an object of class LIM as generated by
|
uncertain.vars |
An object of class |
iter |
Number of iterations in the Monte Carlo procedure |
... |
Additional arguments to |
A matrix with the posterior flux ensemble. The number of columns
is equal to the number of fluxes in the provided model, the number of
rows is equal to iter.
This function is a wrapper for the function xsample.
Hannes Hettling
K. V. den Meersche, K. Soetaert, and D. V. Oevelen: xsample(): An R function for sampling linear inverse problems,Journal of Statistical Software, Code Snippets, vol. 30, pp. 1-15, 4 2009.
xsample
##get example model file of glycolysis and TCA cycle limfile.path <- system.file("extdata", "Glycolysis_TCA.LIM", package="BiGGR") ##Specify uncertainty of fluxes "R_GLCt1r", "R_O2t" uncertain.vars <- data.frame(var=c("R_GLCt1r", "R_O2t"), value=c(0.4, 2.4), sd=c(0.08, 0.48)) ##sample ensemble ensemble <- sampleFluxEnsemble(limfile.path, uncertain.vars) ##Example in which linear combination of fluxes is constrained atp.reacs <- "R_ATPS4m - R_NDPK1m - R_HEX1 - R_PFK - R_PGK + R_PYK" uncertain.vars <- data.frame(var=atp.reacs, value=10, sd=1) ensemble <- sampleFluxEnsemble(limfile.path, uncertain.vars)##get example model file of glycolysis and TCA cycle limfile.path <- system.file("extdata", "Glycolysis_TCA.LIM", package="BiGGR") ##Specify uncertainty of fluxes "R_GLCt1r", "R_O2t" uncertain.vars <- data.frame(var=c("R_GLCt1r", "R_O2t"), value=c(0.4, 2.4), sd=c(0.08, 0.48)) ##sample ensemble ensemble <- sampleFluxEnsemble(limfile.path, uncertain.vars) ##Example in which linear combination of fluxes is constrained atp.reacs <- "R_ATPS4m - R_NDPK1m - R_HEX1 - R_PFK - R_PGK + R_PYK" uncertain.vars <- data.frame(var=atp.reacs, value=10, sd=1) ensemble <- sampleFluxEnsemble(limfile.path, uncertain.vars)
Convert an SBML model to a RagraphBPH using
hypergraph. Metabolites are displayed as nodes and reactions are
displayed as directed edges connecting the nodes. If a vector of rates
is given, edge widths are weighted according to the rates. For
negative rates, edges are drawn in red and the arrow between the
metabolites is reversed to represent the correct direction of the flux.
sbml2hyperdraw(sbml.model, rates ,relevant.species, relevant.reactions,layoutType, lwd.max, lwd.min, plt.margins)sbml2hyperdraw(sbml.model, rates ,relevant.species, relevant.reactions,layoutType, lwd.max, lwd.min, plt.margins)
sbml.model |
an rsbml |
rates |
a named |
relevant.species |
a |
relevant.reactions |
a |
layoutType |
is a character string representing the layout engine to
be used for visualization. Current supported layouts are
"dot", "twopi","neato","fdp","sfdp" and "circo". Defaults to
"dot". See |
lwd.max |
a |
lwd.min |
a |
plt.margins |
A numerical vector of the form c(bottom, left, top, right) giving additional white space around the graph (in case long node or edge labels fall outside the plotting region). Defaults to c(150,150,150,150). |
Object of class RagraphBPH with the hypergraph representation of the SBML object.
Hannes Hettling <[email protected]>, Anand K. Gavai <[email protected]>
RagraphBPH hyperdraw
##Generate an example model path <- system.file("extdata", "Glycolysis_TCA_recon2_reactionIDs.txt", package="BiGGR") reaction.ids <- scan(path, what=" ") data("Recon2") model <- buildSBMLFromReactionIDs(reaction.ids, Recon2) ##Plot ATP and ADP in cytosol and mitochondrion in model without rates rel.sp <- c("M_adp_c", "M_atp_c", "M_adp_m", "M_atp_m") hd <- sbml2hyperdraw(model, relevant.species=rel.sp) plot(hd) ##Plot model with random rates rates <- rnorm(length(model@reactions)) names(rates) <- sapply(model@reactions, id) hd <- sbml2hyperdraw(model, rates=rates, relevant.species=rel.sp, lwd.max=4) plot(hd)##Generate an example model path <- system.file("extdata", "Glycolysis_TCA_recon2_reactionIDs.txt", package="BiGGR") reaction.ids <- scan(path, what=" ") data("Recon2") model <- buildSBMLFromReactionIDs(reaction.ids, Recon2) ##Plot ATP and ADP in cytosol and mitochondrion in model without rates rel.sp <- c("M_adp_c", "M_atp_c", "M_adp_m", "M_atp_m") hd <- sbml2hyperdraw(model, relevant.species=rel.sp) plot(hd) ##Plot model with random rates rates <- rnorm(length(model@reactions)) names(rates) <- sapply(model@reactions, id) hd <- sbml2hyperdraw(model, rates=rates, relevant.species=rel.sp, lwd.max=4) plot(hd)