crnpy

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crnpy is a python library for the manipulation and analysis of chemical reaction networks.

Install

crnpy can be installed from source. After downloading the repository, run

  $ python setup.py install --user

crnpy requires libSBML <http://sbml.org/Software/libSBML>, SciPy <https://www.scipy.org/scipylib/index.html>, NumPy <http://www.numpy.org/>_ and SymPy <http://www.sympy.org/en/index.html>. To generate the network invariants, pycddlib <http://pycddlib.readthedocs.io/en/latest/quickstart.html#installation> is also required.

Some example scripts require NetworkX <http://networkx.github.io/>_ or PuLP <http://pythonhosted.org/PuLP/>_.

Getting Started

We can create a network from an SBML file

.. code:: python

  >>> from crnpy.crn import CRN, from_sbml, from_react_strings, from_react_file
  >>> crn = from_sbml("examples/data/sbml/enzyme.xml")

from a file containing a list of reactions in human-readable format

.. code:: python

  >>> crn = from_react_file("examples/data/reactions/biomodels/biomd0000000026")

or directly from a list of reaction strings:

.. code:: python

  >>> crn = from_react_strings(["A <-> B", "2A + C <-> D", "D -> E", "E -> 2A + C"])

Now we can explore some properties of the network. For example, we can look at the stoichiometric matrix

.. code:: python

  >>> crn.print_stoich_matrix()
      r0  r0_rev  r1  r1_rev  r2  r3
  A | -1       1  -2       2   0   2 |
  B |  1      -1   0       0   0   0 |
  C |  0       0  -1       1   0   1 |
  D |  0       0   1      -1  -1   0 |
  E |  0       0   0       0   1  -1 |

at the derivatives of the species concentrations and the conservation laws:

.. code:: python

  >>> crn.stoich_matrix * crn.rates
  Matrix([
  [-2*A**2*C*k_r1 - A*k_r0 + B*k_r0_rev + 2*D*k_r1_rev + 2*E*k_r3],
  [                                           A*k_r0 - B*k_r0_rev],
  [                            -A**2*C*k_r1 + D*k_r1_rev + E*k_r3],
  [                             A**2*C*k_r1 - D*k_r1_rev - D*k_r2],
  [                                               D*k_r2 - E*k_r3]])
  >>> crn.cons_laws
  (A + B + 2*D + 2*E, C + D + E)

We can check whether the conditions of the deficiency zero theorem are satisfied:

.. code:: python

  >>> crn.is_ma
  True
  >>> crn.deficiency
  0
  >>> crn.is_weakly_rev
  True

For more information, a tutorial is available as well as some example scripts.

Citation

If you use crnpy for your work, please cite

Elisa Tonello, CrnPy: a python library for the analysis of chemical reaction networks, 2016.