sybilccFBA (version 3.0.1)

cfba_moment: Function: cfba_moment: implement MOMENT method

Description

This function uses GPR, kcat, and molecular weights to calculate fluxes according to MOMENT method.

Usage

cfba_moment(model,mod2=NULL, Kcat,MW=NULL,
selected_rxns=NULL,verboseMode=2,objVal=NULL,
RHS=NULL,solver=SYBIL_SETTINGS("SOLVER"),medval=NULL,
                 runFVA = FALSE, fvaRxn = NULL)

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

selected_rxns

optional parameter used to select a set of reactions not all, list of react_id

verboseMode

An integer value indicating the amount of output to stdout: 0: nothing, 1: status messages, 2: like 1 plus with more details, 3: generates files of the LP problem. Default: 2.

RHS

the budget C, for EColi 0.27

objVal

when not null the problem will be to find the minimum budget that give the specified objective value(biomass)

solver

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

medval

median of Kcat values , used for missing values

runFVA

flag to choose to run flux variability default FALSE

fvaRxn

optional parameter to choose set of reaction ids to run FVA on them. Ids are from the irreversible model default all reactions. Ignored when runFVA is not set.

Value

returns a list containing slots:

sol

solution of the problem, instance of class optObj.

prob

object of class sysBiolAlg that contains the linear problem, this can be used for further processing like adding more constraints. To save it, function writeProb can be used.

geneCol

mapping of genes to variables in the problem.

Details

Main steps 1- Add variables for all genes 2- for each selected reaction: parse gpr, 3- Add variables accordingly and constraints 4- Add solvant constraint

References

Adadi, R., Volkmer, B., Milo, R., Heinemann, M., & Shlomi, T. (2012). Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters, 8(7). doi:10.1371/journal.pcbi.1002575

Gelius-Dietrich, G., Desouki, A. A., Fritzemeier, C. J., & Lercher, M. J. (2013). sybil<U+2013>Efficient constraint-based modelling in R. BMC systems biology, 7(1), 125.

See Also

modelorg, optimizeProb

Examples

# NOT RUN {
	library(sybilccFBA)
	data(iAF1260)
	model= iAF1260
 	data(mw)
 	data(kcat)
 	 mod2=mod2irrev(model)
  
	uppbnd(mod2)[react_id(mod2)=="R_EX_glc_e__b"]=1000
	uppbnd(mod2)[react_id(mod2)=="R_EX_glyc_e__b"]=0
	uppbnd(mod2)[react_id(mod2)=="R_EX_ac_e__b"]=0
	uppbnd(mod2)[react_id(mod2)=="R_EX_o2_e__b"]=1000
	lowbnd(mod2)[react_id(mod2)=="R_ATPM"]=0

  sol=cfba_moment(model,mod2,kcat,MW=mw,verbose=2,RHS=0.27,solver="glpkAPI",medval=3600*22.6) 
  bm_rxn = which(obj_coef(mod2)!=0)
  print(sprintf('biomass=%f',sol$sol$fluxes[bm_rxn]))
 # Enzyme concentrations:
 
# }
# NOT RUN {
<!-- % end dontrun -->
# }
# NOT RUN {
data(Ec_core)
model=Ec_core
genedef=read.csv(paste0(path.package("sybilccFBA"), '/extdata/Ec_core_genedef.csv'),
     stringsAsFactors=FALSE)
mw=data.frame(gene=genedef[,'gene'],mw=genedef[,'mw'],stringsAsFactors=FALSE)
mw[mw[,1]=='s0001','mw']=0.001#spontenious
##########
##Kcats
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'
 ########## ----------------
 
mod2=mod2irrev(model)
uppbnd(mod2)[react_id(mod2)=="EX_o2(e)_b"]=1000
lowbnd(mod2)[react_id(mod2)=="ATPM"]=0
uppbnd(mod2)[react_id(mod2)=="ATPM"]=0 

nr=react_num(mod2)
medianVal=median(kcat[,'val'])
# sum(is.na(genedef[,'mw']))

C_mr=0.13#as not all genes exist
sim_name=paste0('Ec_org_med',round(medianVal,2),'_C',100*C_mr)

cpx_stoich =read.csv(paste0(path.package("sybilccFBA"), 
'/extdata/','cpx_stoich_me.csv'),stringsAsFactors=FALSE)


##-----------------
CSList=c("R_EX_glc_e__b","R_EX_glyc_e__b","R_EX_ac_e__b","R_EX_fru_e__b",
"R_EX_pyr_e__b","R_EX_gal_e__b",
"R_EX_lac_L_e__b","R_EX_malt_e__b","R_EX_mal_L_e__b","R_EX_fum_e__b","R_EX_xyl_D_e__b",
"R_EX_man_e__b","R_EX_tre_e__b",
"R_EX_mnl_e__b","R_EX_g6p_e__b","R_EX_succ_e__b","R_EX_gam_e__b","R_EX_sbt_D_e__b",
"R_EX_glcn_e__b",
"R_EX_rib_D_e__b","R_EX_gsn_e__b","R_EX_ala_L_e__b","R_EX_akg_e__b","R_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)

CSList=substring(gsub('_e_','(e)',CSList),3)
 react_name(mod2)[react_id(mod2) %in% CSList]
 msrd=msrd[CSList %in% react_id(mod2) ]    
 CA=CA[CSList %in% react_id(mod2)]  
 CSList=CSList[CSList %in% react_id(mod2)]
 uppbnd(mod2)[react_id(mod2) %in% CSList]=0
 mod2R=mod2
##---------------------
bm_rxn=which(obj_coef(mod2)!=0)
all_flx=NULL
all_flx_MC=NULL
all_rg_MC=NULL
Kcatorg=kcat
solver='glpkAPI'
solverParm=NA

for(cs in 1:length(CSList)){
		print(CSList[cs])
		mod2=mod2R
		uppbnd(mod2)[react_id(mod2) %in% CSList]=0
		uppbnd(mod2)[react_id(mod2)==CSList[cs]]=1000
	sol_org=cfba_moment(model,mod2,kcat,MW=mw,verbose=2,RHS=0.27,solver="glpkAPI",
    medval=3600*medianVal) 
  
	### preparing output -------------------
	all_flx=rbind(all_flx,data.frame(stringsAsFactors=FALSE,cs,csname=CSList[cs],
            rxn_id=react_id(mod2),
            flx=sol_org$sol$fluxes[1:nr], ub=uppbnd(mod2),ubR=uppbnd(mod2R)))
        # print(paste0("nrow all_rg_MC=",nrow(all_rg_MC)))
}

upt=all_flx[all_flx[,'csname']==all_flx[,'rxn_id'],]
bm=all_flx[react_id(mod2)[obj_coef(mod2)!=0]==all_flx[,'rxn_id'],]

cor.test(bm[,'flx'],msrd,method='spearman')

# }