A Python package for representing reaction networks, and computing their structures and behaviors. Currently the core of it is implemented as a wrapper of SloppyCell.

Mathematically, a reaction network can be represented as dx/dt = N v(x,p), where x, v and p are the concentration, rate and parameter vectors, respectively and N is the stoichiometry matrix.

Examples of structures:

• Stoichiometry matrix N
• Left and right null spaces of N
• Reduced stoichiometry matrix N_r (a selection of N's rows with trivial left null space) and link matrix L where N = L N_r

Examples of behaviors:

• Dynamics x(t)
• Steady states s = x(inf) and J = v(s, p)
• Parameter sensitivities of them dx(t)/dp, R^s = ds/dp and R^J = dJ/dp
  • elasticities E_p = dv/dp and E_x = dv/dx
  • control matrices C^s = -(N E_s)^-1 N and C^J = I + E_s C^s

• SloppyCell
• pandas
• numpy
• matplotlib (optional; for plotting dynamics)
• scipy (optional; for using rootfinding to get steady states)
• sage (optional; for getting the integer-valued null spaces of a stoichiometry matrix)
• libsbml (optional; for importing and exporting SBML files)

import rxnnet

net = rxnnet.network.Network('net')
net.add_compartment(id='env')
net.add_compartment(id='cell')
net.add_species(id='C1', compartment='env', initial_value=2, is_constant=True)
net.add_species(id='C2', compartment='env', initial_value=1, is_constant=True)
net.add_species(id='X', compartment='cell', initial_value=0)
net.add_reaction(id='R1', eqn='C1<->X', ratelaw='k1*(C1-X)', p={'k1':1})
net.add_reaction(id='R2', eqn='X<->C2', ratelaw='k2*(X-C2)', p={'k2':2})

traj = net.integrate((0,10))
traj.plot()