. {{{#!highlight python


#
##
### Identifying how much ATP A. woodii can produce from different combinations of carbon sources
##
#


m = ScrumPy.Model('Awoodii.spy')

# TASK 1 ----------------------------------- minimise importers of substrate and set ATPase
# load lp object
lp = m.GetLP()

# set some constraints

#1) block all transporters
# identify all transporters
for reaction in m.sm.cnames:
        if "_tx" in reaction:
                print reaction

# now block them by setting a fixed flux
for reaction in m.sm.cnames:
        if "_tx" in reaction:
                lp.SetFixedFlux({reaction:0})


# unblock substrates to be investigated
lp.ClearFluxConstraints(['CO_s_tx','BUTANEDIOL_s_tx'])

# make a dictionary of all fermentation products
f_product_constraints = {}
for reaction in m.sm.cnames:
        if "f_tx" in reaction: # fermentation products have the suffix "f_tx"
                f_product_constraints[reaction] = (None,0) # None, 0 indicates only negative flux (export) is allowed

# now set the bounds on lp using the dictionary just made
lp.SetFluxBounds(c_constraints)


lp.SetFixedFlux({'ADENOSINETRIPHOSPHATASE-RXN':1}) # set a fixed flux of 1 (arbitrary number) on the ATPase reaction

# set the objective of minimising these two substrate transporters (minimising is default objective)
lp.SetObjective(['CO_s_tx','BUTANEDIOL_s_tx'])

lp.Solve() # solve
sol_1 = lp.GetPrimSol() # get solution and save to variable

# now explore what the solution is


for reaction in sol_1: # for each reaction in solution
       if "tx" in reaction: # if the reaction is a transporter
               print reaction, sol_1[reaction] # print the name and flux carried by transporter (can also use round() function here to format value)

#double check that the ATPase has a flux unit 1
print sol_1['ADENOSINETRIPHOSPHATASE-RXN']

# calculate the ATP yield per carbon mole
# ATPase_flux / carbon_flux
1 / (0.41*4+1.22)

# also check that carbon is balanced
# flux x carbon_atoms = carbon_flux
0.41*4+1.22
1.43 * 2
}}}