Description Usage Arguments Details Value Author(s) References See Also Examples
This function uses GPR, kcat, and molecular weights to calculate fluxes according to MOMENT method taking into account multifunctional enzymes. Whenever a protein i was involved in more than one reaction, we introduced auxiliary concentration variables xi,j for each of these reactions. These xi,j replaced the global concentration variable gi for the protein in the corresponding equation that limits the flux through this reaction based on the enzyme concentration. The sum of the xi,j is then equal to the total concentration of protein gi included in the global enzyme solvent capacity constraint.
1 2 3 4 |
model |
An object of class |
mod2 |
An object of class |
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. |
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
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C_mu_coef |
used to have C as a linear function of mu (biomass) : C = RHS + C_mu_coef*Biomass |
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. |
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
returns a list containing slots:
sol |
solution of the problem. |
prob |
object of class |
geneCol |
mapping of genes to variables in the problem. |
geneConc |
the concentration of each gene, when the gene is catalyzing more than one reaction there will be a row with 'rxn' column set to NA containing the total. |
rxnMC |
for each reaction (GPR) the molecular crowding of it (total sum to budget) |
rxnGeneMC |
the contribution of each gene to all of its reactions. |
Abdelmoneim Amer Desouki
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–Efficient constraint-based modelling in R. BMC systems biology, 7(1), 125.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## 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_mr=cfba_moment_mr(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_mr$sol$fluxes[bm_rxn]))
# Enzyme concentrations:
gconc=sol_mr$geneConc
## End(Not run)
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