sybilccFBA (version 3.0.1)

simulate_EColi: Simulate Ecoli

Description

calls different methods to simulate metabolism in EColi model.

Usage

simulate_EColi(model, mod2, kcat, mw, budget_C, CSList, atpmz = TRUE, trns_rxns = NULL,
                cpx_stoich = NULL, solver = "glpkAPI")

Arguments

model

An object of class modelorg.

mod2

An object of class modelorg with only irreversible reactions. It can be sent to save time of recalculating it with each call.

kcat

kcat values in unit 1/S. Contains three slots: reaction id,direction(dirxn),value(val)

mw

list of molecular weights of all genes, using function calc_MW, in units g/mol

budget_C

the budget C, for EColi 0.27

CSList

the list of carbon sources to be simulated, given as reaction id's

atpmz

set ATPM reaction to zero, default TRUE

trns_rxns

trnsport reactions to be excluded from budget (may contain other reactions if necessary)

cpx_stoich

giving the stoichiometry of complexes: data frame containing at least two columns 'genes','stoich' 'gene' : delimited string of genes(ordered by geneid(e.g bnumber)), 'stoich': number of subunits of each gene in the same order in 'genes' Default: NULL (e.g. all stoichiometry coefficients equal one)

solver

Single character string giving the solver package to use. See SYBIL_SETTINGS for possible values. Default: SYBIL_SETTINGS("SOLVER").

Value

return a LIST,

gr_data

the objective value (growth rate) of all carbon sources

uptake

the uptake of the different conditions

all_flx

data frame containing carbon source, reaction id, and flux in column flx

rg_MC

reaction gene molecular crowding(MC), data frame containing carbon source, reaction id, and flux in column flx, geneConc, rxnMC: MC for reaction, rgMC: MC for gene/complex, the result is sorted in decreasing order of MC

all_flx_MC

reaction molecular crowding(MC), data frame containing carbon source, reaction id, and flux in column flx, geneConc, rxnMC: MC for reaction, rgMC: MC for gene/complex, result not sorted.

Details

This function is to show examples of simulation

References

Desouki, Abdelmoneim. "Algorithms for improving the predictive power of flux balance analysis." PhD diss., 2016.

See Also

getccFBA_mat,cfba_moment_mr

Examples

# NOT RUN {
 
# }
# NOT RUN {
    data(iML1515)
    #1-get metabolic model
    model=iML1515
    #2-get Molecular weights
    print(load(paste0(path.package("sybilccFBA"), '/extdata/mw_iML1515.RData')))

    mw=mw_iML1515
    mw=rbind(mw,data.frame(Synonym="s0001", mw=0.001))
    colnames(mw)[1]='gene'
    #3-get kcat list
    kl=read.csv(stringsAsFactors=FALSE,paste0(path.package("sybilccFBA"), 
        '/extdata/','allKcats_upd34_dd_h.csv'))
        kl=kl[!is.na(kl[,'ijo_id']),]
        kcat=data.frame(rxn_id=kl[,'ijo_id'],val=kl[,'kcat_max'],dirxn=kl[,'dirxn'],src=kl[,'src'],
        stringsAsFactors=FALSE)
        kcat=kcat[kcat[,'rxn_id']%in% react_id(model),]
        kcat[(is.na(kcat[,'src'])),'src']='Max'
    #4-get complex stoichiometry if used
    cpx_stoich =read.csv(paste0(path.package("sybilccFBA"), 
            '/extdata/','cpx_stoich_me.csv'),stringsAsFactors=FALSE)
    #5-identify Carbon sources to be tested
    csl=c("EX_glc__D(e)_b","EX_glyc(e)_b", "EX_ac(e)_b" ,  "EX_fru(e)_b",  
    "EX_pyr(e)_b",  "EX_gal(e)_b" ,
     "EX_lac__L(e)_b",  "EX_malt(e)_b" ,"EX_mal__L(e)_b", "EX_fum(e)_b" ,  
     "EX_xyl__D(e)_b","EX_man(e)_b", "EX_tre(e)_b",
     "EX_mnl(e)_b",   "EX_g6p(e)_b",   "EX_succ(e)_b", "EX_gam(e)_b",  
     "EX_sbt__D(e)_b", "EX_glcn(e)_b",
     "EX_rib__D(e)_b","EX_gsn(e)_b" ,"EX_ala__L(e)_b",  "EX_akg(e)_b" ,  
     "EX_acgam(e)_b")

    msrd=c(0.66,0.47,0.29,0.54,0.41,0.24,0.41,0.52,0.55,0.47,0.51,0.35,0.48,0.61,
        0.78,0.50,0.40,0.48,0.68,0.41,0.37,0.24,0.24,0.61)
    CA=c(6,3,2,6,3,6,3,12,4,4,5,6,12,6,6,4,6,6,6,5,10,3,5,8)

    #6-get irreversible model
    sum(react_id(model) %in% gsub('_b$','',csl))
    model1=model
    react_rev(model1)[react_id(model) %in% gsub('_b$','',csl)]=TRUE
    mod2=mod2irrev(model1)

     react_name(mod2)[react_id(mod2) %in% csl]
     
     uppbnd(mod2)[react_id(mod2) %in% csl]=0
     uppbnd(mod2)[react_id(mod2) %in% gsub('_b$','_f',csl)]=0

    uppbnd(mod2)[react_id(mod2)=="EX_o2(e)_b"]=1000
    trns_rxns=grepl("tex$",react_id(model))

    ##Call function
    tmp_res=simulate_EColi(model,mod2,mw=mw,budget_C=0.27,kcat=kcat,cpx_stoich=cpx_stoich,
    atpmz=FALSE,trns_rxns=trns_rxns,CSList=csl)
    bm=tmp_res[[1]]
    cor.test(bm[,'flx'],msrd,method='spearman')
     plot(msrd,bm[,'flx'],ylab="Predicted Growth Rate",xlim=c(0,0.8),
                  ylim=c(0,max(bm[,'flx'])),xlab='Measured Growth Rate',
        main=sprintf("Effect of Keff, Corr=%.2f,nkcat=%d",cor(bm[,'flx'],msrd),nrow(kcat)))
    abline(a=0,b=1,col="red",lwd=2,lty=2)

# }