This project is a maintained fork of the code pstjohn/emll using PyTensor over Theano.
Works using emll can be found:
- Bayesian Inference of Metabolic Kinetics from Genome-Scale Multiomics Data
- Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling
To install:
pip install git+https://github.com/pnnl-predictive-phenomics/emll.gitor to install in developer mode:
git clone https://github.com/pnnl-predictive-phenomics/emll.git
cd emll
python -m pip install -e .Test install by running:
python -c "import emll"This code uses the intelpython distribution for some faster blas routines.
General code for solving for the steady-state metabolite and flux values as a function of elasticity parameters, enzyme expression, and external metabolite concentrations is found in emll/linlog_model.py. PyTensor code to perform the regularized linear regression (and integrate this operation into pymc3 models) is found in emll/pytensor_utils.py.
If you use emll in your work, please cite:
@article{St.John2019,
author = {{St. John}, Peter C. and Strutz, Jonathan and Broadbelt, Linda J. and Tyo, Keith E. J. and Bomble, Yannick J.},
doi = {10.1371/journal.pcbi.1007424},
editor = {Maranas, Costas D.},
issn = {1553-7358},
journal = {PLOS Computational Biology},
month = {nov},
number = {11},
pages = {e1007424},
title = {{Bayesian inference of metabolic kinetics from genome-scale multiomics data}},
url = {https://dx.plos.org/10.1371/journal.pcbi.1007424},
volume = {15},
year = {2019}
}