Browse Networks in the Matlab Command Window Using surfNet
Author(s): Siu Hung Joshua Chan, Department of Chemical Engineering, The Pennsylvania State University
Reviewer(s):
INTRODUCTION
In this tutorial, we will demonstrate how to browse a COBRA model in verbal format in the Matlab command window through an initial call and interactive mouse clicking.
MATERIALS
EQUIPMENT SETUP
Start CobraToolbox
initCobraToolbox(false) % false, as we don't want to update;
_____ _____ _____ _____ _____ |
/ ___| / _ \ | _ \ | _ \ / ___ \ | COnstraint-Based Reconstruction and Analysis
| | | | | | | |_| | | |_| | | |___| | | The COBRA Toolbox - 2019
| | | | | | | _ { | _ / | ___ | |
| |___ | |_| | | |_| | | | \ \ | | | | | Documentation:
\_____| \_____/ |_____/ |_| \_\ |_| |_| |
http://opencobra.github.io/cobratoolbox
|
> Checking if git is installed ... Done (version: 2.17.1).
> Checking if the repository is tracked using git ... Done.
> Checking if curl is installed ... Done.
> Checking if remote can be reached ... Done.
> Initializing and updating submodules (this may take a while)... Done.
> Adding all the files of The COBRA Toolbox ... Done.
> Define CB map output... set to svg.
> TranslateSBML is installed and working properly.
> Configuring solver environment variables ...
- [*---] ILOG_CPLEX_PATH: /opt/ibm/ILOG/CPLEX_Studio128/cplex/matlab/x86-64_linux
- [*---] GUROBI_PATH: /opt/gurobi810/linux64/matlab
- [----] TOMLAB_PATH: --> set this path manually after installing the solver ( see
instructions )
- [---*] MOSEK_PATH: /opt/mosek/8/
Done.
> Checking available solvers and solver interfaces ... Done.
> Setting default solvers ... Done.
> Saving the MATLAB path ... Done.
- The MATLAB path was saved as ~/pathdef.m.
> Summary of available solvers and solver interfaces
Support LP MILP QP MIQP NLP
----------------------------------------------------------------------
gurobi active 1 1 1 1 -
ibm_cplex active 1 1 1 1 -
tomlab_cplex active 0 0 0 0 -
glpk active 1 1 - - -
mosek active 1 - 1 - -
matlab active 1 - - - 1
cplex_direct active 0 0 0 - -
dqqMinos active 0 - - - -
pdco active 1 - 1 - -
quadMinos active 0 - - - -
qpng passive - - 1 - -
tomlab_snopt passive - - - - 0
lp_solve legacy 1 - - - -
----------------------------------------------------------------------
Total - 7 3 5 2 1
+ Legend: - = not applicable, 0 = solver not compatible or not installed, 1 = solver installed.
> You can solve LP problems using: 'ibm_cplex' - 'glpk' - 'mosek' - 'matlab' - 'pdco'
> You can solve MILP problems using: 'ibm_cplex' - 'glpk'
> You can solve QP problems using: 'ibm_cplex' - 'mosek' - 'pdco' - 'qpng'
> You can solve MIQP problems using: 'ibm_cplex'
> You can solve NLP problems using: 'matlab'
> Checking for available updates ... skipped
PROCEDURE
Load the E. coli iJO1366 model as an example model.
modelFileName = 'iJO1366.mat';
modelDirectory = getDistributedModelFolder(modelFileName); %Look up the folder for the distributed Models.
modelFileName= [modelDirectory filesep modelFileName]; % Get the full path. Necessary to be sure, that the right model is loaded
iJO1366 = readCbModel(modelFileName);
Warning: Metabolite IDs will be adjusted to COBRA style metID[e] instead of metID_e
Browse a network
Browse the network by starting from an initial metabolite, e.g., D-glucose in the extracellular compartment.
surfNet(iJO1366, 'glc__D[e]')
Met #1195 glc__D[e], D-Glucose, C6H12O6
Consuming reactions:
#164 EX_glc__D_e, Bd: -10 / 1000, D-Glucose exchange
glc__D[e] <=>
#1355 GLCtex_copy1, Bd: -1000 / 1000, Glucose transport via diffusion (extracellular to periplasm)
glc__D[e] <=> glc__D[p]
#1356 GLCtex_copy2, Bd: 0 / 1000, Glucose transport via diffusion (extracellular to periplasm)
glc__D[e] -> glc__D[p]
Producing reactions: none
Show previous steps...
All reactions producing or consuming 'glc__D_e' will have their reaction indices (#xxx), ids (.rxns), bounds (.lb/.ub), names (.rxnNames) and formulae printed on the command window. All reactions and the participating metabolites are hyperlinked. For example, click on the reaction 'GLCtex_copy1'. (This is equivalent to run the following command.)
% called by clicking 'GLCtex_copy1'
surfNet([], 'GLCtex_copy1', 0, 'none', 0, 1, [], 0)
Rxn #1355 GLCtex_copy1, Bd: -1000 / 1000, Glucose transport via diffusion (extracellular to periplasm)
glc__D[e] <=> glc__D[p]
id Met Stoich metNames, metFormulas
Reactant:
#1195 glc__D[e] -1 D-Glucose, C6H12O6
Product:
#1587 glc__D[p] 1 D-Glucose, C6H12O6
Show previous steps...
Details for the metabolites will appear, e.g., indeices, ids, stoichiometric coefficients, names and chemical formulae. By iteratively clicking on the reactions and metabolites that you are interested in, you can browse through the metabolic network.
Now, say you have gone through a series of metabolites and reactions (glc__D[e], GLCtex_copy1, glc__D[p], GLCptspp, g6p[c]):
Click glc__D_[p]:
% called by clicking 'glc__D_p'
surfNet([], 'glc__D[p]', 0, 'none', 0, 1, [], 0)
Met #1587 glc__D[p], D-Glucose, C6H12O6
Consuming reactions:
#1336 GLCDpp, Bd: 0 / 1000, Glucose dehydrogenase (ubiquinone-8 as acceptor) (periplasm)
q8[c] + glc__D[p] + h2o[p] -> q8h2[c] + glcn[p] + h[p]
#1352 GLCabcpp, Bd: 0 / 1000, D-glucose transport via ABC system (periplasm)
atp[c] + h2o[c] + glc__D[p] -> adp[c] + glc__D[c] + h[c] + pi[c]
#1353 GLCptspp, Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
#1354 GLCt2pp, Bd: 0 / 1000, D-glucose transport in via proton symport (periplasm)
glc__D[p] + h[p] -> glc__D[c] + h[c]
Producing reactions:
#1252 G1PPpp, Bd: 0 / 1000, Glucose-1-phosphatase
g1p[p] + h2o[p] -> glc__D[p] + pi[p]
#1355 GLCtex_copy1, Bd: -1000 / 1000, Glucose transport via diffusion (extracellular to periplasm)
glc__D[e] <=> glc__D[p]
#1356 GLCtex_copy2, Bd: 0 / 1000, Glucose transport via diffusion (extracellular to periplasm)
glc__D[e] -> glc__D[p]
#1607 LACZpp, Bd: 0 / 1000, B-galactosidase
h2o[p] + lcts[p] -> gal[p] + glc__D[p]
#2463 TREHpp, Bd: 0 / 1000, Alpha,alpha-trehalase (periplasm)
h2o[p] + tre[p] -> 2 glc__D[p]
Show previous steps...
Click GLCptspp:
% called by clicking 'GLCptspp'
surfNet([], 'GLCptspp', 0, 'none', 0, 1, [], 0)
Rxn #1353 GLCptspp, Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
id Met Stoich metNames, metFormulas
Reactant:
#784 pep[c] -1 Phosphoenolpyruvate, C3H2O6P
#1587 glc__D[p] -1 D-Glucose, C6H12O6
Product:
#508 g6p[c] 1 D-Glucose 6-phosphate, C6H11O9P
#853 pyr[c] 1 Pyruvate, C3H3O3
Show previous steps...
Click g6p_c:
% called by clicking 'g6p[c]'
surfNet([], 'g6p[c]', 0, 'none', 0, 1, [], 0)
Met #508 g6p[c], D-Glucose 6-phosphate, C6H11O9P
Consuming reactions:
#1283 G6PDH2r, Bd: -1000 / 1000, Glucose 6-phosphate dehydrogenase
g6p[c] + nadp[c] <=> 6pgl[c] + h[c] + nadph[c]
#1284 G6PP, Bd: 0 / 1000, Glucose-6-phosphate phosphatase
g6p[c] + h2o[c] -> glc__D[c] + pi[c]
#2077 PGI, Bd: -1000 / 1000, Glucose-6-phosphate isomerase
g6p[c] <=> f6p[c]
#2461 TRE6PS, Bd: 0 / 1000, Alpha,alpha-trehalose-phosphate synthase (UDP-forming)
g6p[c] + udpg[c] -> h[c] + tre6p[c] + udp[c]
Producing reactions:
#477 AB6PGH, Bd: 0 / 1000, Arbutin 6-phosphate glucohydrolase
arbt6p[c] + h2o[c] -> g6p[c] + hqn[c]
#1214 FFSD, Bd: 0 / 1000, Beta-fructofuranosidase
h2o[c] + suc6p[c] -> fru[c] + g6p[c]
#1231 FRULYSDG, Bd: -1000 / 1000, Fructoselysine phosphate deglycase
frulysp[c] + h2o[c] <=> g6p[c] + lys__L[c]
#1285 G6Pt6_2pp, Bd: 0 / 1000, Glucose-6-phosphate transport via phosphate antiport (periplasm)
2 pi[c] + g6p[p] -> g6p[c] + 2 pi[p]
#1353 GLCptspp, Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
#1500 HEX1, Bd: 0 / 1000, Hexokinase (D-glucose:ATP)
atp[c] + glc__D[c] -> adp[c] + g6p[c] + h[c]
#2082 PGMT, Bd: -1000 / 1000, Phosphoglucomutase
g1p[c] <=> g6p[c]
#2459 TRE6PH, Bd: 0 / 1000, Trehalose-6-phosphate hydrolase
h2o[c] + tre6p[c] -> g6p[c] + glc__D[c]
Show previous steps...
In each click, there is also a button 'Show previous steps...' at the bottom. Clicking on it will show the metabolites and reactions that you have visited in order. This is equivalent to calling:
% called by clicking 'Show previous steps...'
surfNet([], [], 0, 'none', 0, 1, [], 0, struct('showPrev', true))
glc__D[e]>>GLCtex_copy1>>glc__D[p]>>GLCptspp>>g6p[c]>>
You can go back to any of the intermediate metabolites/reactions by clicking the hyperlinked mets/rxns shown.
Call options
Shown below are various call options for including flux vectors and customizing display. All call options are preserved during the interactive browsing by mouse clicking.
Show objective reactions
Omit the 'metrxn' (2nd) argument to print objective reactions:
surfNet(iJO1366)
Rxn #8 BIOMASS_Ec_iJO1366_core_53p95M, Bd: 0 / 1000, E. coli biomass objective function (iJO1366) - core - with 53.95 GAM estimate
0.000223 10fthf[c] + 2.6e-05 2fe2s[c] + 0.000223 2ohph[c] + 0.00026 4fe4s[c] + 0.513689 ala__L[c] + 0.000223 amet[c] +
0.295792 arg__L[c] + 0.241055 asn__L[c] + 0.241055 asp__L[c] + 54.1248 atp[c] + 0.000122 bmocogdp[c] + 2e-06 btn[c] +
0.005205 ca2[c] + 0.005205 cl[c] + 0.000576 coa[c] + 2.5e-05 cobalt2[c] + 0.133508 ctp[c] + 0.000709 cu2[c] +
0.09158 cys__L[c] + 0.026166 datp[c] + 0.027017 dctp[c] + 0.027017 dgtp[c] + 0.026166 dttp[c] + 0.000223 fad[c] +
0.006715 fe2[c] + 0.007808 fe3[c] + 0.26316 gln__L[c] + 0.26316 glu__L[c] + 0.612638 gly[c] + 0.215096 gtp[c] + 48.6015 h2o[c]
+ 0.094738 his__L[c] + 0.290529 ile__L[c] + 0.195193 k[c] + 0.450531 leu__L[c] + 0.343161 lys__L[c] + 0.153686 met__L[c] +
0.008675 mg2[c] + 0.000223 mlthf[c] + 0.000691 mn2[c] + 7e-06 mobd[c] + 0.001831 nad[c] + 0.000447 nadp[c] + 0.013013 nh4[c] +
0.000323 ni2[c] + 0.017868 pe160[c] + 0.054154 pe161[c] + 0.185265 phe__L[c] + 0.000223 pheme[c] + 0.221055 pro__L[c] +
0.000223 pydx5p[c] + 0.000223 ribflv[c] + 0.215792 ser__L[c] + 0.000223 sheme[c] + 0.004338 so4[c] + 0.000223 thf[c] +
0.000223 thmpp[c] + 0.253687 thr__L[c] + 0.056843 trp__L[c] + 0.137896 tyr__L[c] + 5.5e-05 udcpdp[c] + 0.144104 utp[c] +
0.423162 val__L[c] + 0.000341 zn2[c] + 0.019456 kdo2lipid4[e] + 0.013894 murein5px4p[p] + 0.045946 pe160[p] + 0.02106 pe161[p]
-> 53.95 adp[c] + 53.95 h[c] + 53.9457 pi[c] + 0.773903 ppi[c]
id Met Stoich metNames, metFormulas
Reactant:
#1 10fthf[c] -0.000223 10-Formyltetrahydrofolate, C20H21N7O7
#69 2fe2s[c] -0.000026 [2Fe-2S] iron-sulfur cluster, S2Fe2
#82 2ohph[c] -0.000223 2-Octaprenyl-6-hydroxyphenol, C46H70O2
#167 4fe4s[c] -0.00026 [4Fe-4S] iron-sulfur cluster, S4Fe4
#255 ala__L[c] -0.513689 L-Alanine, C3H7NO2
#265 amet[c] -0.000223 S-Adenosyl-L-methionine, C15H23N6O5S
#294 arg__L[c] -0.295792 L-Arginine, C6H15N4O2
#298 asn__L[c] -0.241055 L-Asparagine, C4H8N2O3
#302 asp__L[c] -0.241055 L-Aspartate, C4H6NO4
#307 atp[c] -54.124831 ATP C10H12N5O13P3, C10H12N5O13P3
#314 bmocogdp[c] -0.000122 Bis-molybdopterin guanine dinucleotide, C40H44N20O27P4S4Mo
#317 btn[c] -0.000002 Biotin, C10H15N2O3S
#326 ca2[c] -0.005205 Calcium, Ca
#355 cl[c] -0.005205 Chloride, Cl
#358 coa[c] -0.000576 Coenzyme A, C21H32N7O16P3S
#359 cobalt2[c] -0.000025 Co2+, Co
#377 ctp[c] -0.133508 CTP C9H12N3O14P3, C9H12N3O14P3
#379 cu2[c] -0.000709 Copper, Cu
#383 cys__L[c] -0.09158 L-Cysteine, C3H7NO2S
#392 datp[c] -0.026166 DATP C10H12N5O12P3, C10H12N5O12P3
#401 dctp[c] -0.027017 DCTP C9H12N3O13P3, C9H12N3O13P3
#412 dgtp[c] -0.027017 DGTP C10H12N5O13P3, C10H12N5O13P3
#451 dttp[c] -0.026166 DTTP C10H13N2O14P3, C10H13N2O14P3
#468 fad[c] -0.000223 Flavin adenine dinucleotide oxidized, C27H31N9O15P2
#474 fe2[c] -0.006715 Fe2+ mitochondria, Fe
#475 fe3[c] -0.007808 Iron (Fe3+), Fe
#541 gln__L[c] -0.26316 L-Glutamine, C5H10N2O3
#544 glu__L[c] -0.26316 L-Glutamate, C5H8NO4
#551 gly[c] -0.612638 Glycine, C2H5NO2
#574 gtp[c] -0.215096 GTP C10H12N5O14P3, C10H12N5O14P3
#580 h2o[c] -48.601527 H2O H2O, H2O
#597 his__L[c] -0.094738 L-Histidine, C6H9N3O2
#621 ile__L[c] -0.290529 L-Isoleucine, C6H13NO2
#637 k[c] -0.195193 Potassium, K
#650 leu__L[c] -0.450531 L-Leucine, C6H13NO2
#661 lys__L[c] -0.343161 L-Lysine, C6H15N2O2
#686 met__L[c] -0.153686 L-Methionine, C5H11NO2S
#691 mg2[c] -0.008675 Magnesium, Mg
#694 mlthf[c] -0.000223 5,10-Methylenetetrahydrofolate, C20H21N7O6
#697 mn2[c] -0.000691 Manganese, Mn
#702 mobd[c] -0.000007 Molybdate, MoO4
#720 nad[c] -0.001831 Nicotinamide adenine dinucleotide, C21H26N7O14P2
#722 nadp[c] -0.000447 Nicotinamide adenine dinucleotide phosphate, C21H25N7O17P3
#725 nh4[c] -0.013013 Ammonium, H4N
#726 ni2[c] -0.000323 Nickel, Ni
#780 pe160[c] -0.017868 Phosphatidylethanolamine (dihexadecanoyl, n-C16:0), C37H74N1O8P1
#781 pe161[c] -0.054154 Phosphatidylethanolamine (dihexadec-9enoyl, n-C16:1), C37H70N1O8P1
#800 phe__L[c] -0.185265 L-Phenylalanine, C9H11NO2
#801 pheme[c] -0.000223 Protoheme C34H30FeN4O4, C34H30FeN4O4
#834 pro__L[c] -0.221055 L-Proline, C5H9NO2
#851 pydx5p[c] -0.000223 Pyridoxal 5'-phosphate, C8H8NO6P
#868 ribflv[c] -0.000223 Riboflavin C17H20N4O6, C17H20N4O6
#885 ser__L[c] -0.215792 L-Serine, C3H7NO3
#889 sheme[c] -0.000223 Siroheme C42H36FeN4O16, C42H36FeN4O16
#897 so4[c] -0.004338 Sulfate, O4S
#936 thf[c] -0.000223 5,6,7,8-Tetrahydrofolate, C19H21N7O6
#940 thmpp[c] -0.000223 Thiamine diphosphate, C12H16N4O7P2S
#942 thr__L[c] -0.253687 L-Threonine, C4H9NO3
#977 trp__L[c] -0.056843 L-Tryptophan, C11H12N2O2
#985 tyr__L[c] -0.137896 L-Tyrosine, C9H11NO3
#1001 udcpdp[c] -0.000055 Undecaprenyl diphosphate, C55H89O7P2
#1025 utp[c] -0.144104 UTP C9H11N2O15P3, C9H11N2O15P3
#1026 val__L[c] -0.423162 L-Valine, C5H11NO2
#1039 zn2[c] -0.000341 Zinc, Zn
#1238 kdo2lipid4[e] -0.019456 KDO(2)-lipid IV(A), C84H148N2O37P2
#1676 murein5px4p[p] -0.013894 Two disacharide linked murein units, pentapeptide crosslinked tetrapeptide (A2pm->D-ala) (middle of chain), C77H117N15O40
#1711 pe160[p] -0.045946 Phosphatidylethanolamine (dihexadecanoyl, n-C16:0), C37H74N1O8P1
#1712 pe161[p] -0.02106 Phosphatidylethanolamine (dihexadec-9enoyl, n-C16:1), C37H70N1O8P1
Product:
#240 adp[c] 53.95 ADP C10H12N5O10P2, C10H12N5O10P2
#577 h[c] 53.95 H+, H
#808 pi[c] 53.945662 Phosphate, HO4P
#821 ppi[c] 0.773903 Diphosphate, HO7P2
Show previous steps...
Call with a list of mets/rxns
The 'metrxn' arguement can be a string of id for a metabolite or reaction. It can also be a cell array of ids, e.g.,
surfNet(iJO1366, {'glc__D[p]'; 'GLCptspp'; 'g6p[c]'})
Met #1587 glc__D[p], D-Glucose, C6H12O6
Consuming reactions:
#1336 GLCDpp, Bd: 0 / 1000, Glucose dehydrogenase (ubiquinone-8 as acceptor) (periplasm)
q8[c] + glc__D[p] + h2o[p] -> q8h2[c] + glcn[p] + h[p]
#1352 GLCabcpp, Bd: 0 / 1000, D-glucose transport via ABC system (periplasm)
atp[c] + h2o[c] + glc__D[p] -> adp[c] + glc__D[c] + h[c] + pi[c]
#1353 GLCptspp, Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
#1354 GLCt2pp, Bd: 0 / 1000, D-glucose transport in via proton symport (periplasm)
glc__D[p] + h[p] -> glc__D[c] + h[c]
Producing reactions:
#1252 G1PPpp, Bd: 0 / 1000, Glucose-1-phosphatase
g1p[p] + h2o[p] -> glc__D[p] + pi[p]
#1355 GLCtex_copy1, Bd: -1000 / 1000, Glucose transport via diffusion (extracellular to periplasm)
glc__D[e] <=> glc__D[p]
#1356 GLCtex_copy2, Bd: 0 / 1000, Glucose transport via diffusion (extracellular to periplasm)
glc__D[e] -> glc__D[p]
#1607 LACZpp, Bd: 0 / 1000, B-galactosidase
h2o[p] + lcts[p] -> gal[p] + glc__D[p]
#2463 TREHpp, Bd: 0 / 1000, Alpha,alpha-trehalase (periplasm)
h2o[p] + tre[p] -> 2 glc__D[p]
Rxn #1353 GLCptspp, Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
id Met Stoich metNames, metFormulas
Reactant:
#784 pep[c] -1 Phosphoenolpyruvate, C3H2O6P
#1587 glc__D[p] -1 D-Glucose, C6H12O6
Product:
#508 g6p[c] 1 D-Glucose 6-phosphate, C6H11O9P
#853 pyr[c] 1 Pyruvate, C3H3O3
Met #508 g6p[c], D-Glucose 6-phosphate, C6H11O9P
Consuming reactions:
#1283 G6PDH2r, Bd: -1000 / 1000, Glucose 6-phosphate dehydrogenase
g6p[c] + nadp[c] <=> 6pgl[c] + h[c] + nadph[c]
#1284 G6PP, Bd: 0 / 1000, Glucose-6-phosphate phosphatase
g6p[c] + h2o[c] -> glc__D[c] + pi[c]
#2077 PGI, Bd: -1000 / 1000, Glucose-6-phosphate isomerase
g6p[c] <=> f6p[c]
#2461 TRE6PS, Bd: 0 / 1000, Alpha,alpha-trehalose-phosphate synthase (UDP-forming)
g6p[c] + udpg[c] -> h[c] + tre6p[c] + udp[c]
Producing reactions:
#477 AB6PGH, Bd: 0 / 1000, Arbutin 6-phosphate glucohydrolase
arbt6p[c] + h2o[c] -> g6p[c] + hqn[c]
#1214 FFSD, Bd: 0 / 1000, Beta-fructofuranosidase
h2o[c] + suc6p[c] -> fru[c] + g6p[c]
#1231 FRULYSDG, Bd: -1000 / 1000, Fructoselysine phosphate deglycase
frulysp[c] + h2o[c] <=> g6p[c] + lys__L[c]
#1285 G6Pt6_2pp, Bd: 0 / 1000, Glucose-6-phosphate transport via phosphate antiport (periplasm)
2 pi[c] + g6p[p] -> g6p[c] + 2 pi[p]
#1353 GLCptspp, Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
#1500 HEX1, Bd: 0 / 1000, Hexokinase (D-glucose:ATP)
atp[c] + glc__D[c] -> adp[c] + g6p[c] + h[c]
#2082 PGMT, Bd: -1000 / 1000, Phosphoglucomutase
g1p[c] <=> g6p[c]
#2459 TRE6PH, Bd: 0 / 1000, Trehalose-6-phosphate hydrolase
h2o[c] + tre6p[c] -> g6p[c] + glc__D[c]
Show previous steps...
Show metabolite names in reaction formulae
Some models may use generic ids for mets/rxns. In this case, call surfNet() with the 'metNameFlag' (3rd) arguement turned on to show the names for metabolites (.metNames) in the reaction formulae, e.g.,
surfNet(iJO1366, 'fgam[c]', 1)
Met #484 fgam[c], N2-Formyl-N1-(5-phospho-D-ribosyl)glycinamide, C8H13N2O9P
Consuming reactions:
#2207 PRFGS, Bd: 0 / 1000, Phosphoribosylformylglycinamidine synthase
ATP C10H12N5O13P3 + N2-Formyl-N1-(5-phospho-D-ribosyl)glycinamide + L-Glutamine + H2O H2O -> ADP C10H12N5O10P2 + 2-(Formamido)-N1-(5-phospho-D-ribosyl)acetamidine
+ L-Glutamate + H+ + Phosphate
Producing reactions:
#1316 GARFT, Bd: -1000 / 1000, Phosphoribosylglycinamide formyltransferase
10-Formyltetrahydrofolate + N1-(5-Phospho-D-ribosyl)glycinamide <=> N2-Formyl-N1-(5-phospho-D-ribosyl)glycinamide + H+ + 5,6,7,8-Tetrahydrofolate
#1317 GART, Bd: 0 / 1000, GAR transformylase-T
ATP C10H12N5O13P3 + Formate + N1-(5-Phospho-D-ribosyl)glycinamide -> ADP C10H12N5O10P2 + N2-Formyl-N1-(5-phospho-D-ribosyl)glycinamide
+ H+ + Phosphate
Show previous steps...
Hide reaction detials
Turn off the 'showMets' (6th) arguement to suppress details for reactions, e.g.,
surfNet(iJO1366, iJO1366.rxns(1001:1010), [], [], [], 0)
Rxn #1001 DHPPDA2, Bd: 0 / 1000, Diaminohydroxyphosphoribosylaminopryrimidine deaminase (25drapp)
25drapp[c] + h[c] + h2o[c] -> 5apru[c] + nh4[c]
Rxn #1002 DHPS2, Bd: 0 / 1000, Dihydropteroate synthase
4abz[c] + 6hmhptpp[c] -> dhpt[c] + ppi[c]
Rxn #1003 DHPTDCs2, Bd: 0 / 1000, 4,5-dihydroxy-2,3-pentanedione cyclization (spontaneous)
dhptd[c] -> mdhdhf[c]
Rxn #1004 DHPTDNR, Bd: 0 / 0, Dihydropteridine reductase
dhptdn[c] + 3 h[c] + nadph[c] -> nadp[c] + thptdn[c]
Rxn #1005 DHPTDNRN, Bd: 0 / 0, Dihydropteridine reductase (NADH)
dhptdn[c] + 3 h[c] + nadh[c] -> nad[c] + thptdn[c]
Rxn #1006 DHPTPE, Bd: -1000 / 1000, Dihydroneopterin triphosphate 2'-epimerase
ahdt[c] <=> dhmptp[c]
Rxn #1007 DHQS, Bd: 0 / 1000, 3-dehydroquinate synthase
2dda7p[c] -> 3dhq[c] + pi[c]
Rxn #1008 DHQTi, Bd: 0 / 1000, 3-dehydroquinate dehydratase, irreversible
3dhq[c] -> 3dhsk[c] + h2o[c]
Rxn #1009 DIMPtex, Bd: -1000 / 1000, DIMP transport via diffusion (extracellular to periplasm)
dimp[e] <=> dimp[p]
Rxn #1010 DINSt2pp, Bd: 0 / 1000, Deoxyinosine transport in via proton symport (periplasm)
din[p] + h[p] -> din[c] + h[c]
Show previous steps...
Look at one or more flux distributions
First, get a flux distribution by optimizing the biomass production of the model (the standard flux balance analysis
). Then call surfNet with the flux distribution (4th argument) to look at how the flux through pyruvate is distributed: s = optimizeCbModel(iJO1366, 'max', 'one');
surfNet(iJO1366, 'pyr[c]', [], s.x)
Met #853 pyr[c], Pyruvate, C3H3O3
Consuming reactions with non-zero fluxes :
#511 ACHBS (0.28541), Bd: 0 / 1000, 2-aceto-2-hydroxybutanoate synthase
2obut[c] + h[c] + pyr[c] -> 2ahbut[c] + co2[c]
#513 ACLS (0.85886), Bd: 0 / 1000, Acetolactate synthase
h[c] + 2 pyr[c] -> alac__S[c] + co2[c]
#618 ALATA_L (-0.57111), Bd: -1000 / 1000, L-alanine transaminase
akg[c] + ala__L[c] <=> glu__L[c] + pyr[c]
#987 DHDPS (0.36441), Bd: 0 / 1000, Dihydrodipicolinate synthase
aspsa[c] + pyr[c] -> 23dhdp[c] + h[c] + 2 h2o[c]
#1053 DXPS (0.00279), Bd: 0 / 1000, 1-deoxy-D-xylulose 5-phosphate synthase
g3p[c] + h[c] + pyr[c] -> co2[c] + dxyl5p[c]
#2047 PDH (7.96919), Bd: 0 / 1000, Pyruvate dehydrogenase
coa[c] + nad[c] + pyr[c] -> accoa[c] + co2[c] + nadh[c]
#2171 POR5 (0.10684), Bd: -1000 / 1000, Pyruvate synthase
coa[c] + 2 flxso[c] + pyr[c] <=> accoa[c] + co2[c] + 2 flxr[c] + h[c]
#2466 TRPAS2 (-0.05584), Bd: -1000 / 1000, Tryptophanase (L-tryptophan)
h2o[c] + trp__L[c] <=> indole[c] + nh4[c] + pyr[c]
Producing reactions with non-zero fluxes :
#554 ADCL (0.00066), Bd: 0 / 1000, 4-aminobenzoate synthase
4adcho[c] -> 4abz[c] + h[c] + pyr[c]
#666 ANS (0.05584), Bd: 0 / 1000, Anthranilate synthase
chor[c] + gln__L[c] -> anth[c] + glu__L[c] + h[c] + pyr[c]
#813 CHRPL (0.00022), Bd: 0 / 1000, Chorismate pyruvate lyase
chor[c] -> 4hbz[c] + pyr[c]
#908 CYSTL (0.1512), Bd: 0 / 1000, Cystathionine b-lyase
cyst__L[c] + h2o[c] -> hcys__L[c] + nh4[c] + pyr[c]
#978 DHAPT (0.86538), Bd: 0 / 1000, Dihydroxyacetone phosphotransferase
dha[c] + pep[c] -> dhap[c] + pyr[c]
#1353 GLCptspp (10), Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
Show previous steps...
All reactions involving pyruvate with non-zero fluxes are printed. The flux values are in the parentheses following the reaction ids. Note that reactions stated as consuming or producing the metabolite have taken the directions of the fluxes into account. Therefore, supplying a different flux distribution or not supplying may give different display. By default, only reactions with non-zero fluxes are printed if a flux distribution is supplied. Turn the 'nonzeroFluxFlag' (5th) argument off to show all reactions:
surfNet(iJO1366, 'pyr[c]', [], s.x, 0)
Met #853 pyr[c], Pyruvate, C3H3O3
Consuming reactions:
#511 ACHBS (0.28541), Bd: 0 / 1000, 2-aceto-2-hydroxybutanoate synthase
2obut[c] + h[c] + pyr[c] -> 2ahbut[c] + co2[c]
#513 ACLS (0.85886), Bd: 0 / 1000, Acetolactate synthase
h[c] + 2 pyr[c] -> alac__S[c] + co2[c]
#618 ALATA_L (-0.57111), Bd: -1000 / 1000, L-alanine transaminase
akg[c] + ala__L[c] <=> glu__L[c] + pyr[c]
#987 DHDPS (0.36441), Bd: 0 / 1000, Dihydrodipicolinate synthase
aspsa[c] + pyr[c] -> 23dhdp[c] + h[c] + 2 h2o[c]
#1053 DXPS (0.00279), Bd: 0 / 1000, 1-deoxy-D-xylulose 5-phosphate synthase
g3p[c] + h[c] + pyr[c] -> co2[c] + dxyl5p[c]
#2047 PDH (7.96919), Bd: 0 / 1000, Pyruvate dehydrogenase
coa[c] + nad[c] + pyr[c] -> accoa[c] + co2[c] + nadh[c]
#2067 PFL (0), Bd: 0 / 1000, Pyruvate formate lyase
coa[c] + pyr[c] -> accoa[c] + for[c]
#2171 POR5 (0.10684), Bd: -1000 / 1000, Pyruvate synthase
coa[c] + 2 flxso[c] + pyr[c] <=> accoa[c] + co2[c] + 2 flxr[c] + h[c]
#2172 POX (0), Bd: 0 / 1000, Pyruvate oxidase
h2o[c] + pyr[c] + q8[c] -> ac[c] + co2[c] + q8h2[c]
#2198 PPS (0), Bd: 0 / 1000, Phosphoenolpyruvate synthase
atp[c] + h2o[c] + pyr[c] -> amp[c] + 2 h[c] + pep[c] + pi[c]
#2466 TRPAS2 (-0.05584), Bd: -1000 / 1000, Tryptophanase (L-tryptophan)
h2o[c] + trp__L[c] <=> indole[c] + nh4[c] + pyr[c]
Producing reactions:
#507 ACGAptspp (0), Bd: 0 / 1000, N-Acetyl-D-glucosamine transport via PEP:Pyr PTS (periplasm)
pep[c] + acgam[p] -> acgam6p[c] + pyr[c]
#516 ACMANAptspp (0), Bd: 0 / 1000, N-acetyl-D-mannosamine transport via PTS (periplasm)
pep[c] + acmana[p] -> acmanap[c] + pyr[c]
#518 ACMUMptspp (0), Bd: 0 / 1000, N-acetylmuramate transport via PEP:Pyr PTS (periplasm)
pep[c] + acmum[p] -> acmum6p[c] + pyr[c]
#522 ACNML (0), Bd: 0 / 1000, N-Acetylneuraminate lyase
acnam[c] -> acmana[c] + pyr[c]
#554 ADCL (0.00066), Bd: 0 / 1000, 4-aminobenzoate synthase
4adcho[c] -> 4abz[c] + h[c] + pyr[c]
#617 ALATA_D2 (0), Bd: 0 / 1000, D-alanine transaminase
ala__D[c] + pydx5p[c] -> pyam5p[c] + pyr[c]
#619 ALATA_L2 (0), Bd: 0 / 1000, Alanine transaminase
ala__L[c] + pydx5p[c] -> pyam5p[c] + pyr[c]
#666 ANS (0.05584), Bd: 0 / 1000, Anthranilate synthase
chor[c] + gln__L[c] -> anth[c] + glu__L[c] + h[c] + pyr[c]
#698 ARBTptspp (0), Bd: 0 / 1000, Arbutin transport via PEP:Pyr PTS (periplasm)
pep[c] + arbt[p] -> arbt6p[c] + pyr[c]
#716 ASCBptspp (0), Bd: 0 / 1000, L-ascorbate transport via PEP:Pyr PTS (periplasm)
pep[c] + ascb__L[p] -> ascb6p[c] + pyr[c]
#813 CHRPL (0.00022), Bd: 0 / 1000, Chorismate pyruvate lyase
chor[c] -> 4hbz[c] + pyr[c]
#814 CHTBSptspp (0), Bd: 0 / 1000, Chitobiose transport via PEP:Pyr PTS (periplasm)
pep[c] + chtbs[p] -> chtbs6p[c] + pyr[c]
#902 CYSDDS (0), Bd: 0 / 1000, D-cysteine desulfhydrase
cys__D[c] + h2o[c] -> h2s[c] + nh4[c] + pyr[c]
#903 CYSDS (0), Bd: 0 / 1000, Cysteine Desulfhydrase
cys__L[c] + h2o[c] -> h2s[c] + nh4[c] + pyr[c]
#908 CYSTL (0.1512), Bd: 0 / 1000, Cystathionine b-lyase
cyst__L[c] + h2o[c] -> hcys__L[c] + nh4[c] + pyr[c]
#927 DAAD (0), Bd: 0 / 1000, D-Amino acid dehydrogenase
ala__D[c] + fad[c] + h2o[c] -> fadh2[c] + nh4[c] + pyr[c]
#942 DAPAL (0), Bd: 0 / 1000, 2,3-diaminopropionate amonnia lyase
23dappa[c] + h2o[c] -> 2 nh4[c] + pyr[c]
#970 DDPGALA (-0), Bd: -1000 / 1000, 2-dehydro-3-deoxy-6-phosphogalactonate aldolase
2dh3dgal6p[c] <=> g3p[c] + pyr[c]
#978 DHAPT (0.86538), Bd: 0 / 1000, Dihydroxyacetone phosphotransferase
dha[c] + pep[c] -> dhap[c] + pyr[c]
#1094 EDA (0), Bd: 0 / 1000, 2-dehydro-3-deoxy-phosphogluconate aldolase
2ddg6p[c] -> g3p[c] + pyr[c]
#1238 FRUpts2pp (0), Bd: 0 / 1000, Fructose transport via PEP:Pyr PTS (f6p generating) (periplasm)
pep[c] + fru[p] -> f6p[c] + pyr[c]
#1239 FRUptspp (0), Bd: 0 / 1000, D-fructose transport via PEP:Pyr PTS (periplasm)
pep[c] + fru[p] -> f1p[c] + pyr[c]
#1303 GALTptspp (0), Bd: 0 / 1000, Galactitol transport via PEP:Pyr PTS (periplasm)
pep[c] + galt[p] -> galt1p[c] + pyr[c]
#1313 GAMptspp (0), Bd: 0 / 1000, D-glucosamine transport via PEP:Pyr PTS (periplasm)
pep[c] + gam[p] -> gam6p[c] + pyr[c]
#1341 GLCRAL (0), Bd: 0 / 1000, 5-dehydro-4-deoxyglucarate aldolase
5dh4dglc[c] -> 2h3oppan[c] + pyr[c]
#1353 GLCptspp (10), Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
#1519 HOPNTAL (0), Bd: 0 / 1000, 4-hydroxy-2-oxopentanoate aldolase
4h2opntn[c] -> acald[c] + pyr[c]
#1551 ICHORT (0), Bd: 0 / 1000, Isochorismatase
h2o[c] + ichor[c] -> 23ddhb[c] + pyr[c]
#1601 L_LACD2 (0), Bd: 0 / 1000, L-Lactate dehydrogenase (ubiquinone)
lac__L[c] + q8[c] -> pyr[c] + q8h2[c]
#1602 L_LACD3 (0), Bd: 0 / 1000, L-Lactate dehydrogenase (menaquinone)
lac__L[c] + mqn8[c] -> mql8[c] + pyr[c]
#1622 LDH_D (0), Bd: -1000 / 1000, D-lactate dehydrogenase
lac__D[c] + nad[c] <=> h[c] + nadh[c] + pyr[c]
#1623 LDH_D2 (0), Bd: 0 / 1000, D-lactate dehydrogenase
lac__D[c] + q8[c] -> pyr[c] + q8h2[c]
#1711 MALDDH (0), Bd: 0 / 1000, Malate decarboxylating oxidoreductase (decarboxylating)
mal__D[c] + nad[c] -> co2[c] + nadh[c] + pyr[c]
#1725 MALTptspp (0), Bd: 0 / 1000, Maltose transport via PEP:Pyr PTS (periplasm)
pep[c] + malt[p] -> malt6p[c] + pyr[c]
#1736 MANGLYCptspp (0), Bd: 0 / 1000, 2-O-alpha-mannosyl-D-glycerate transport via PEP:Pyr PTS (periplasm)
pep[c] + manglyc[p] -> man6pglyc[c] + pyr[c]
#1739 MANptspp (0), Bd: 0 / 1000, D-mannose transport via PEP:Pyr PTS (periplasm)
pep[c] + man[p] -> man6p[c] + pyr[c]
#1742 MCITL2 (0), Bd: -1000 / 1000, Methylisocitrate lyase
micit[c] <=> pyr[c] + succ[c]
#1745 MCPST (0), Bd: 0 / 1000, 3-mercaptopyruvate sulfurtransferase
cyan[c] + mercppyr[c] -> h[c] + pyr[c] + tcynt[c]
#1761 ME1 (0), Bd: 0 / 1000, Malic enzyme (NAD)
mal__L[c] + nad[c] -> co2[c] + nadh[c] + pyr[c]
#1762 ME2 (0), Bd: 0 / 1000, Malic enzyme (NADP)
mal__L[c] + nadp[c] -> co2[c] + nadph[c] + pyr[c]
#1822 MNLptspp (0), Bd: 0 / 1000, Mannitol transport via PEP:Pyr PTS (periplasm)
pep[c] + mnl[p] -> mnl1p[c] + pyr[c]
#1977 OAADC (0), Bd: 0 / 1000, Oxaloacetate decarboxylase
h[c] + oaa[c] -> co2[c] + pyr[c]
#2266 PYK (0), Bd: 0 / 1000, Pyruvate kinase
adp[c] + h[c] + pep[c] -> atp[c] + pyr[c]
#2269 PYRt2rpp (0), Bd: -1000 / 1000, Pyruvate reversible transport via proton symport (periplasm)
h[p] + pyr[p] <=> h[c] + pyr[c]
#2326 SBTptspp (0), Bd: 0 / 1000, D-sorbitol transport via PEP:Pyr PTS (periplasm)
pep[c] + sbt__D[p] -> pyr[c] + sbt6p[c]
#2342 SERD_D (0), Bd: 0 / 1000, D-serine deaminase
ser__D[c] -> nh4[c] + pyr[c]
#2343 SERD_L (0), Bd: 0 / 1000, L-serine deaminase
ser__L[c] -> nh4[c] + pyr[c]
#2352 SHCHCS3 (0), Bd: 0 / 1000, 2-succinyl-6-hydroxy-2,4-cyclohexadiene 1-carboxylate synthase
2sephchc[c] -> 2shchc[c] + pyr[c]
#2391 SUCptspp (0), Bd: 0 / 1000, Sucrose transport via PEP:Pyr (periplasm)
pep[c] + sucr[p] -> pyr[c] + suc6p[c]
#2464 TREptspp (0), Bd: 0 / 1000, Trehalose transport via PEP:Pyr PTS (periplasm)
pep[c] + tre[p] -> pyr[c] + tre6p[c]
#2558 VPAMTr (0), Bd: -1000 / 1000, Valine-pyruvate aminotransferase
3mob[c] + ala__L[c] <=> pyr[c] + val__L[c]
Show previous steps...
You can also compare multiple flux distributions by supplying them in a matrix format, each column being a flux distribution. For example, get another flux distribution maximizing the biomass production using D-fructose instead of glucose as substrate. Then call surfNet to look at reactions with different fluxes.
Original uptake rates:
printUptakeBound(iJO1366);
EX_ca2_e -1000
EX_cbl1_e -0.01
EX_cl_e -1000
EX_co2_e -1000
EX_cobalt2_e -1000
EX_cu2_e -1000
EX_fe2_e -1000
EX_fe3_e -1000
EX_glc__D_e -10
EX_h_e -1000
EX_h2o_e -1000
EX_k_e -1000
EX_mg2_e -1000
EX_mn2_e -1000
EX_mobd_e -1000
EX_na1_e -1000
EX_nh4_e -1000
EX_ni2_e -1000
EX_o2_e -1000
EX_pi_e -1000
EX_sel_e -1000
EX_slnt_e -1000
EX_so4_e -1000
EX_tungs_e -1000
EX_zn2_e -1000
Use fructose instead of glucose as substrate:
iJO1366 = changeRxnBounds(iJO1366, {'EX_glc__D_e'; 'EX_fru_e'},...
printUptakeBound(iJO1366);
EX_ca2_e -1000
EX_cbl1_e -0.01
EX_cl_e -1000
EX_co2_e -1000
EX_cobalt2_e -1000
EX_cu2_e -1000
EX_fe2_e -1000
EX_fe3_e -1000
EX_fru_e -10
EX_h_e -1000
EX_h2o_e -1000
EX_k_e -1000
EX_mg2_e -1000
EX_mn2_e -1000
EX_mobd_e -1000
EX_na1_e -1000
EX_nh4_e -1000
EX_ni2_e -1000
EX_o2_e -1000
EX_pi_e -1000
EX_sel_e -1000
EX_slnt_e -1000
EX_so4_e -1000
EX_tungs_e -1000
EX_zn2_e -1000
Run FBA again to get a flux distribution using fructose as substrate. Then look at reactions with different fluxes in the glucose and fructose cases using surfNet.
sFru = optimizeCbModel(iJO1366, 'max', 'one'); % FBA
fluxMatrix = [s.x, sFru.x]; % put two flux vectors in a matrix
% reactions with different fluxes
rxnDiff = abs(fluxMatrix(:, 1) - fluxMatrix(:, 2)) > 1e-6;
surfNet(iJO1366, iJO1366.rxns(rxnDiff), [], fluxMatrix, [], 0)
Rxn #139 EX_fru_e (0, -10), Bd: -10 / 1000, D-Fructose exchange
fru[e] <=>
Rxn #164 EX_glc__D_e (-10, 0), Bd: 0 / 1000, D-Glucose exchange
glc__D[e] ->
Rxn #623 ALAt2pp_copy2 (-0.00511, 0), Bd: -1000 / 1000, L-alanine transport in via proton symport (periplasm)
ala__L[p] + h[p] <=> ala__L[c] + h[c]
Rxn #624 ALAt4pp (0.00511, 0), Bd: 0 / 1000, L-alanine transport in via sodium symport (periplasm)
ala__L[p] + na1[p] -> ala__L[c] + na1[c]
Rxn #1230 FRUK (0, 5.75203), Bd: 0 / 1000, Fructose-1-phosphate kinase
atp[c] + f1p[c] -> adp[c] + fdp[c] + h[c]
Rxn #1238 FRUpts2pp (0, 4.24797), Bd: 0 / 1000, Fructose transport via PEP:Pyr PTS (f6p generating) (periplasm)
pep[c] + fru[p] -> f6p[c] + pyr[c]
Rxn #1239 FRUptspp (0, 5.75203), Bd: 0 / 1000, D-fructose transport via PEP:Pyr PTS (periplasm)
pep[c] + fru[p] -> f1p[c] + pyr[c]
Rxn #1240 FRUtex (-0, 10), Bd: -1000 / 1000, D-fructose transport via diffusion (extracellular to periplasm)
fru[e] <=> fru[p]
Rxn #1353 GLCptspp (10, 0), Bd: 0 / 1000, D-glucose transport via PEP:Pyr PTS (periplasm)
pep[c] + glc__D[p] -> g6p[c] + pyr[c]
Rxn #1356 GLCtex_copy2 (10, 0), Bd: 0 / 1000, Glucose transport via diffusion (extracellular to periplasm)
glc__D[e] -> glc__D[p]
Rxn #1377 GLUt2rpp (0, -0.00511), Bd: -1000 / 1000, L-glutamate transport via proton symport, reversible (periplasm)
glu__L[p] + h[p] <=> glu__L[c] + h[c]
Rxn #1378 GLUt4pp (0, 0.00511), Bd: 0 / 1000, Na+/glutamate symport (periplasm)
glu__L[p] + na1[p] -> glu__L[c] + na1[c]
Rxn #1758 MDH (4.82506, 4.82528), Bd: -1000 / 1000, Malate dehydrogenase
mal__L[c] + nad[c] <=> h[c] + nadh[c] + oaa[c]
Rxn #1837 MOX (0.0016, 0.00138), Bd: -1000 / 1000, Malate oxidase
mal__L[c] + o2[c] <=> h2o2[c] + oaa[c]
Rxn #2048 PDX5PO2 (0.00022, 0), Bd: 0 / 1000, Pyridoxine 5'-phosphate oxidase (anaerboic
nad[c] + pdx5p[c] -> h[c] + nadh[c] + pydx5p[c]
Rxn #2049 PDX5POi (0, 0.00022), Bd: 0 / 1000, Pyridoxine 5'-phosphate oxidase
o2[c] + pdx5p[c] -> h2o2[c] + pydx5p[c]
Rxn #2064 PFK (5.75203, 0), Bd: 0 / 1000, Phosphofructokinase
atp[c] + f6p[c] -> adp[c] + fdp[c] + h[c]
Rxn #2077 PGI (5.91807, -4.08193), Bd: -1000 / 1000, Glucose-6-phosphate isomerase
g6p[c] <=> f6p[c]
Show previous steps...
Customize model data to be displayed
Customize the fields for metabolites and reactions to be printed by supplying the 'field2print' (7th) argument. It is defaulted to be:
{{'metNames','metFormulas'}, {'rxnNames','lb','ub'}}
The first cell contains the metabolite-related fields to be printed and the second cell contains the reaction-related fields to be printed. It can also be inputted as a single cell array of strings, as long as from the size (equal to #mets or #rxns) or from the name of the field (starting with 'met' or 'rxn'), the fields are recognizable to be met- or rxn-related. For example, show the grRules for rxns but omit the bounds and show the constraint sense (csense) associated with each metabolite. Note the difference from the original call:
surfNet(iJO1366, 'fdp[c]', [], [], [], [],...
{'metNames', 'metFormulas', 'rxnNames', 'grRules', 'csense'})
Met #473 fdp[c], D-Fructose 1,6-bisphosphate, C6H10O12P2, csense: E
Consuming reactions:
#1151 FBA, Fructose-bisphosphate aldolase, grRules: b2097 or b1773 or b2925
fdp[c] <=> dhap[c] + g3p[c]
#1153 FBP, Fructose-bisphosphatase, grRules: b3925 or b4232 or b2930
fdp[c] + h2o[c] -> f6p[c] + pi[c]
Producing reactions:
#1230 FRUK, Fructose-1-phosphate kinase, grRules: b2168
atp[c] + f1p[c] -> adp[c] + fdp[c] + h[c]
#2064 PFK, Phosphofructokinase, grRules: b3916 or b1723
atp[c] + f6p[c] -> adp[c] + fdp[c] + h[c]
Show previous steps...
surfNet(iJO1366, 'fdp[c]')
Warning: The 2nd input is neither a metabolite nor reaction of the model.
The last argument (8th) 'nCharBreak' sets the number of characters printed per line. By default, it is equal to the width of the Matlab command window. Note the difference:
Characters per line = width of the command window (default):
surfNet(iJO1366, [], [], [], [], 0)
Rxn #8 BIOMASS_Ec_iJO1366_core_53p95M, Bd: 0 / 1000, E. coli biomass objective function (iJO1366) - core - with 53.95 GAM estimate
0.000223 10fthf[c] + 2.6e-05 2fe2s[c] + 0.000223 2ohph[c] + 0.00026 4fe4s[c] + 0.513689 ala__L[c] + 0.000223 amet[c] +
0.295792 arg__L[c] + 0.241055 asn__L[c] + 0.241055 asp__L[c] + 54.1248 atp[c] + 0.000122 bmocogdp[c] + 2e-06 btn[c] +
0.005205 ca2[c] + 0.005205 cl[c] + 0.000576 coa[c] + 2.5e-05 cobalt2[c] + 0.133508 ctp[c] + 0.000709 cu2[c] +
0.09158 cys__L[c] + 0.026166 datp[c] + 0.027017 dctp[c] + 0.027017 dgtp[c] + 0.026166 dttp[c] + 0.000223 fad[c] +
0.006715 fe2[c] + 0.007808 fe3[c] + 0.26316 gln__L[c] + 0.26316 glu__L[c] + 0.612638 gly[c] + 0.215096 gtp[c] + 48.6015 h2o[c]
+ 0.094738 his__L[c] + 0.290529 ile__L[c] + 0.195193 k[c] + 0.450531 leu__L[c] + 0.343161 lys__L[c] + 0.153686 met__L[c] +
0.008675 mg2[c] + 0.000223 mlthf[c] + 0.000691 mn2[c] + 7e-06 mobd[c] + 0.001831 nad[c] + 0.000447 nadp[c] + 0.013013 nh4[c] +
0.000323 ni2[c] + 0.017868 pe160[c] + 0.054154 pe161[c] + 0.185265 phe__L[c] + 0.000223 pheme[c] + 0.221055 pro__L[c] +
0.000223 pydx5p[c] + 0.000223 ribflv[c] + 0.215792 ser__L[c] + 0.000223 sheme[c] + 0.004338 so4[c] + 0.000223 thf[c] +
0.000223 thmpp[c] + 0.253687 thr__L[c] + 0.056843 trp__L[c] + 0.137896 tyr__L[c] + 5.5e-05 udcpdp[c] + 0.144104 utp[c] +
0.423162 val__L[c] + 0.000341 zn2[c] + 0.019456 kdo2lipid4[e] + 0.013894 murein5px4p[p] + 0.045946 pe160[p] + 0.02106 pe161[p]
-> 53.95 adp[c] + 53.95 h[c] + 53.9457 pi[c] + 0.773903 ppi[c]
Show previous steps...
40 characters per line:
surfNet(iJO1366, [], [], [], [], 0, [], 40)
Rxn #8 BIOMASS_Ec_iJO1366_core_53p95M, Bd: 0 / 1000, E. coli biomass objective function (iJO1366) - core - with 53.95 GAM estimate
0.000223 10fthf[c] + 2.6e-05 2fe2s[c] +
0.000223 2ohph[c] + 0.00026 4fe4s[c] +
0.513689 ala__L[c] + 0.000223 amet[c]
+ 0.295792 arg__L[c] +
0.241055 asn__L[c] +
0.241055 asp__L[c] + 54.1248 atp[c] +
0.000122 bmocogdp[c] + 2e-06 btn[c] +
0.005205 ca2[c] + 0.005205 cl[c] +
0.000576 coa[c] + 2.5e-05 cobalt2[c] +
0.133508 ctp[c] + 0.000709 cu2[c] +
0.09158 cys__L[c] + 0.026166 datp[c] +
0.027017 dctp[c] + 0.027017 dgtp[c] +
0.026166 dttp[c] + 0.000223 fad[c] +
0.006715 fe2[c] + 0.007808 fe3[c] +
0.26316 gln__L[c] + 0.26316 glu__L[c]
+ 0.612638 gly[c] + 0.215096 gtp[c] +
48.6015 h2o[c] + 0.094738 his__L[c] +
0.290529 ile__L[c] + 0.195193 k[c] +
0.450531 leu__L[c] +
0.343161 lys__L[c] +
0.153686 met__L[c] + 0.008675 mg2[c] +
0.000223 mlthf[c] + 0.000691 mn2[c] +
7e-06 mobd[c] + 0.001831 nad[c] +
0.000447 nadp[c] + 0.013013 nh4[c] +
0.000323 ni2[c] + 0.017868 pe160[c] +
0.054154 pe161[c] + 0.185265 phe__L[c]
+ 0.000223 pheme[c] +
0.221055 pro__L[c] +
0.000223 pydx5p[c] +
0.000223 ribflv[c] +
0.215792 ser__L[c] + 0.000223 sheme[c]
+ 0.004338 so4[c] + 0.000223 thf[c] +
0.000223 thmpp[c] + 0.253687 thr__L[c]
+ 0.056843 trp__L[c] +
0.137896 tyr__L[c] + 5.5e-05 udcpdp[c]
+ 0.144104 utp[c] + 0.423162 val__L[c]
+ 0.000341 zn2[c] +
0.019456 kdo2lipid4[e] +
0.013894 murein5px4p[p] +
0.045946 pe160[p] + 0.02106 pe161[p]
-> 53.95 adp[c] + 53.95 h[c] +
53.9457 pi[c] + 0.773903 ppi[c]
Show previous steps...
80 characters per line:
surfNet(iJO1366, [], [], [], [], 0, [], 80)
Rxn #8 BIOMASS_Ec_iJO1366_core_53p95M, Bd: 0 / 1000, E. coli biomass objective function (iJO1366) - core - with 53.95 GAM estimate
0.000223 10fthf[c] + 2.6e-05 2fe2s[c] + 0.000223 2ohph[c] + 0.00026 4fe4s[c] +
0.513689 ala__L[c] + 0.000223 amet[c] + 0.295792 arg__L[c] +
0.241055 asn__L[c] + 0.241055 asp__L[c] + 54.1248 atp[c] +
0.000122 bmocogdp[c] + 2e-06 btn[c] + 0.005205 ca2[c] + 0.005205 cl[c] +
0.000576 coa[c] + 2.5e-05 cobalt2[c] + 0.133508 ctp[c] + 0.000709 cu2[c] +
0.09158 cys__L[c] + 0.026166 datp[c] + 0.027017 dctp[c] + 0.027017 dgtp[c] +
0.026166 dttp[c] + 0.000223 fad[c] + 0.006715 fe2[c] + 0.007808 fe3[c] +
0.26316 gln__L[c] + 0.26316 glu__L[c] + 0.612638 gly[c] + 0.215096 gtp[c] +
48.6015 h2o[c] + 0.094738 his__L[c] + 0.290529 ile__L[c] + 0.195193 k[c] +
0.450531 leu__L[c] + 0.343161 lys__L[c] + 0.153686 met__L[c] + 0.008675 mg2[c]
+ 0.000223 mlthf[c] + 0.000691 mn2[c] + 7e-06 mobd[c] + 0.001831 nad[c] +
0.000447 nadp[c] + 0.013013 nh4[c] + 0.000323 ni2[c] + 0.017868 pe160[c] +
0.054154 pe161[c] + 0.185265 phe__L[c] + 0.000223 pheme[c] +
0.221055 pro__L[c] + 0.000223 pydx5p[c] + 0.000223 ribflv[c] +
0.215792 ser__L[c] + 0.000223 sheme[c] + 0.004338 so4[c] + 0.000223 thf[c] +
0.000223 thmpp[c] + 0.253687 thr__L[c] + 0.056843 trp__L[c] +
0.137896 tyr__L[c] + 5.5e-05 udcpdp[c] + 0.144104 utp[c] + 0.423162 val__L[c]
+ 0.000341 zn2[c] + 0.019456 kdo2lipid4[e] + 0.013894 murein5px4p[p] +
0.045946 pe160[p] + 0.02106 pe161[p] -> 53.95 adp[c] + 53.95 h[c] +
53.9457 pi[c] + 0.773903 ppi[c]
Show previous steps...
REFERENCES
[1] Orth, J. D., Thiele I., and Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol., 28(3), 245–248 (2010).