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r.getScaledFluxControlCoefficientMatrix() or getScaledConcentrationControlCoefficientMatrix()
give the error message:
Metabolic control analysis only valid for primitive reaction kinetics models. This model has events
However at steady state the events are not active so if should be possible to compute the matrix of control coefficients. In fact getCC does allow the control coefficients to be computed.
r = te.loada("""
J1: $A -> B; k1_A;
J2: B -> $C; k2_B;
at time > 4: k1 = k1*8
A = 10; B = 0; C = 0;
k1 = 0.1; k2 = 0.14;
""")
m = r.simulate(0, 25, 100)
r.plot()
print "Closeness to steady state: ", r.steadyState()
I think I know why this is failing. NLEQ often has difficulty finding the steady state from initial conditions so roadrunner simulates the system before handing it off to NLEQ. This increases the chances of finding a steady state. If you have events then they may be triggered by the simulate call.
Calling
r.getScaledFluxControlCoefficientMatrix() or getScaledConcentrationControlCoefficientMatrix()
give the error message:
Metabolic control analysis only valid for primitive reaction kinetics models. This model has events
However at steady state the events are not active so if should be possible to compute the matrix of control coefficients. In fact getCC does allow the control coefficients to be computed.
r = te.loada("""
J1: $A -> B; k1_A;
J2: B -> $C; k2_B;
at time > 4: k1 = k1*8
A = 10; B = 0; C = 0;
k1 = 0.1; k2 = 0.14;
""")
m = r.simulate(0, 25, 100)
r.plot()
print "Closeness to steady state: ", r.steadyState()
print r.getFullStoichiometryMatrix()
print r.getConservationMatrix();
print r.getReducedJacobian()
print r.getCC ("J1", "k1")
print r.getCC ("J1", "k2")
r.getScaledFluxControlCoefficientMatrix() # <-- Doesn't work
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