LIMEcoli {LIM} | R Documentation |
Linear inverse model specification for performing Flux Balance Analysis of the E.coli metabolism
(as from http://gcrg.ucsd.edu/Downloads/Flux_Balance_Analysis).
The original input file can be found in the package subdirectory /examples/Reactions/E_coli.lim
There are 53 substances:
GLC, G6P, F6P, FDP, T3P2, T3P1, 13PDG, 3PG, 2PG, PEP, PYR, ACCOA, CIT, ICIT, AKG, SUCCOA, SUCC, FUM, MAL, OA, ACTP, ETH, AC, LAC, FOR, D6PGL, D6PGC, RL5P, X5P, R5P, S7P, E4P, RIB, GLX, NAD, NADH, NADP, NADPH, HEXT, Q, FAD, FADH, AMP, ADP, ATP, GL3P, CO2, PI, PPI, O2, COA, GL, QH2
and 13 externals:
Biomass,GLCxt,GLxt,RIBxt,ACxt,LACxt,FORxt,ETHxt,SUCCxt,PYRxt,PIxt,O2xt,CO2xt
There are 70 unknown reactions (named by the gene encoding for it):
GLK1, PGI1, PFKA, FBP, FBA, TPIA, GAPA, PGK, GPMA, ENO, PPSA, PYKA, ACEE, ZWF, PGL, GND, RPIA, RPE, TKTA1, TKTA2, TALA, GLTA, ACNA, ICDA, SUCA, SUCC1, SDHA1, FRDA, FUMA, MDH, DLD1, ADHE2, PFLA, PTA, ACKA, ACS, PCKA, PPC, MAEB, SFCA, ACEA, ACEB, PPA, GLPK, GPSA1, RBSK, NUOA, FDOH, GLPD, CYOA, SDHA2, PNT1A, PNT2A, ATPA, GLCUP, GLCPTS, GLUP, RIBUP, ACUP, LACUP, FORUP, ETHUP, SUCCUP, PYRUP, PIUP, O2TX, CO2TX, ATPM, ADK, Growth
The model contains:
LIMEcoli
LIMEcoli is of type lim
, which is a list of matrices, vectors,
names and values that specify the linear inverse model problem.
see the return value of Setup
for more information about this list
A more complete description of this structures is in vignette("LIM")
Karline Soetaert <k.soetaert@nioo.knaw.nl>
http://gcrg.ucsd.edu/Downloads/Flux_Balance_Analysis
Edwards,J.S., Covert, M., and Palsson, B.., " Metabolic Modeling of Microbes: the Flux Balance Approach", Environmental Microbiology, 4(3): pp. 133-140 (2002).
# 1. parsimonious (simplest) solution pars<-Ldei(LIMEcoli) # 2. the ranges of each reaction xr <-Xranges(LIMEcoli,central=TRUE, full=TRUE) # 3. the optimal solution - solved with linear programming LP <-Linp(LIMEcoli) Optimal <- t(LP$X) # show the results data.frame(pars=pars$X, Optimal,xr[,1:3]) # The central value of linear programming problem is a valid solution # the central point is a valid solution: X <- xr[,"central"] max(abs(LIMEcoli$A%*%X-LIMEcoli$B)) min(LIMEcoli$G%*%X-LIMEcoli$H) # 4. Sample solution space - this takes a while -500 is not enough print(system.time( xs<-Xsample(LIMEcoli,iter=500,type="mirror",test=TRUE) )) pairs(xs[,1:10],pch=".",cex=2) # Print results: data.frame(pars=pars$X,Optimal=Optimal,xr[,1:2], Mean=colMeans(xs),sd=sd(xs)) # Plot results par(mfrow=c(1,2)) nr <- LIMEcoli$NUnknowns ii <- 1:(nr/2) dotchart(Optimal[ii,1],xlim = range(xr),pch=16,cex=0.8) segments(xr[ii,1],1:nr,xr[ii,2],1:nr) ii <- (nr/2+1):nr dotchart(Optimal[ii,1],xlim = range(xr),pch=16,cex=0.8) segments(xr[ii,1],1:nr,xr[ii,2],1:nr) mtext(side= 3, cex=1.5, outer = TRUE, line=-1.5, "E coli Core Metabolism, optimal solution and ranges")