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solveME: fast and reliable solution of nonlinear ME models

solveME toolbox for solving nonlinear ME models. Includes interfaces (as external modules) to the quad MINOS-based Fortran 90 code by Ding Ma and Michael A. Saunders at Stanford University

If you use solveME in a scientific publication, please cite:

Yang, L., Ma, D., Ebrahim, A., Lloyd, C. J., Saunders, M. A., & Palsson, B. O. (2016). solveME: fast and reliable solution of nonlinear ME models. BMC Bioinformatics, 17(1), 391. doi:10.1186/s12859-016-1240-1

and the following for the Quad MINOS solver:

Ma, D., Yang, L., Fleming, R.M.T., Thiele, I., Palsson, B.O., Saunders, M.A. (2017). Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression. Scientific Reports, 7, 40863. doi:10.1038/srep40863

Author: Laurence Yang

Systems Biology Research Group, UCSD

Requirements

  1. Python
    • tested on Python 2.7 and 3.6
  2. cobrapy
    • tested on 0.5.11
    • not yet fully compatible with cobrapy 0.6 release
  3. cobrame (0.0.7): follow Installation instructions here: https://github.com/SBRG/cobrame
  4. gfortran (>=4.6)
    • (Ubuntu) sudo apt-get install gfortran
    • (Arch) sudo pacman -S gcc-fortran
  5. quadMINOS (available for academic use from Prof. Michael A. Saunders at Stanford University)

Installation

  1. Compile quadMINOS

    • copy Makefile.defs into quadLP_root/minos56/ and quadLP_root/qminos56/
      • this step ensures the compiler flags needed to interface with Python are set
    • cd qminos_root
    • cd minos56; make clean; make
    • cd ..
    • cd qminos56; make clean; make
  2. Copy shared libraries to solveme root directory

    • cd [solveme_root]
    • cp [qminos_root]/qminos56/lib/libquadminos.a ./
    • cp [qminos_root]/minos56/lib/libminos.a ./
  3. Run: python setup.py develop

    • or python setup.py develop --user
    • or sudo python setup.py develop to install for all users
      • (if getting an error, [undefined reference to main], try sudo python setup.py develop)
  4. Special NERSC installation steps

    • (shell) module swap PrgEnv-intel PrgEnv-gnu
    • python setup.py config_fc --f90exec=ftn develop --user
      • (need to use the ftn gfortran Cray wrapper, or else get symbol not found error during import)
  5. Troubleshooting common errors

    1. ImportError: .../solveME/solvemepy/qminospy/qwarmLP.so: symbol _gfortran_transfer_real128_write, version GFORTRAN_1.4 not defined in file libgfortran.so.3 with link time reference
      • Problem: setup.py picked up the wrong libgfortran during installation. Typically, if anaconda is installed, an older version is installed, which seems to be used by default.
      • Solution (workaround): pre-load the desired libgfortran version, bypassing the anaconda one:
        • export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libgfortran.so.3
        • (then, proceed with setup) python setup.py develop --user
      • Link to original solution
    2. NaN produced by the solver.
      • Problem: certain computing environments (e.g., compute clusters) may have compiled Numpy with GCC versions that don't match that used to compile Quad MINOS, and/or the Fortran 90 code in solvemepy. This may lead to unexpected behavior on just that compute environment (like NaN values).
      • Solution: re-compile Numpy with GCC version matching that used to compile Quad MINOS.
        1. load your virtual environment
        2. pip install git+https://github.com/numpy/numpy@v1.16.0
          • here, we use numpy 1.16.0 as an example
        3. perform the Installation steps 1-3 (compile quad minos, copy shared libraries, python setup.py develop)
      • Credit: thank you to Hui Zhong Lu at Calcul Quebéc for this solution!

Use qminos to solve ME models in python

For (reduced) ME models prior prototype 44

  1. Import qminospy to access the solver methods:
from qminospy.me2 import ME_NLP
me_nlp = ME_NLP(me)
# Solve directly using LCL (linearly constrained Lagrangian) NLP method
x,stat,hs = me_nlp.solvenlp()
# Access the solution that is saved in the original minime object
sol = me.solution
sol.f
sol.x_dict

24 Feb 2016: for ME models after prototype 44

  1. Import

py to access the solver methods:

from qminospy.me1 import ME_NLP1
# The object containing solveME methods--composite that uses a ME model object 
# Provide growth_key = 'mu' for minime models,
me_nlp = ME_NLP1(me, growth_key='mu')
# Use bisection for now (until the NLP formulation is worked out for the new prototype 44
muopt, hs, xopt, cache = me_nlp.bisectmu(precision=1e-6)    
# Access the solution that is saved in the original minime object
sol = me.solution
sol.f
sol.x_dict

If your ME model is based on ME 1.0 code (iOL1650, iJL1678):

  1. Same as above but use growth_key='growth_rate_in_per_hour'
from qminospy.me1 import ME_NLP1
# The object containing solveME methods--composite that uses a ME model object 
me_nlp = ME_NLP1(me, growth_key='growth_rate_in_per_hour')
# Bisection
muopt, hs, xopt, cache = me_nlp.bisectmu(precision=1e-3)    
# Access the solution that is saved in the original minime object
sol = me.solution
sol.f
sol.x_dict

Use qminos to solve LP problems in quad-precision:

See the notebook: examples/test_quadLP.ipynb