simulate_EColi: Simulate Ecoli
In sybilccFBA: Cost Constrained Flux Balance Analysis (ccFBA): MetabOlic Modeling with ENzyme kineTics (MOMENT)

Description Usage Arguments Details Value Author(s) References See Also Examples

calls different methods to simulate metabolism in EColi model.

1 2	simulate_EColi(model, mod2, kcat, mw, budget_C, CSList, atpmz = TRUE, trns_rxns = NULL, cpx_stoich = NULL, solver = "glpkAPI")

`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")`.

This function is to show examples of simulation

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.

Abdelmoneim Amer Desouki

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

getccFBA_mat,cfba_moment_mr

 ## 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)


## End(Not run)