BioPathOpt is a Python package designed to streamline metabolic engineering workflows by integrating advanced modeling and optimization tools into a unified framework. It enables researchers to enrich stoichiometric metabolic models with additional biochemical data, seamlessly incorporate enzyme-constrained modeling approaches, and perform targeted optimization for the production of desired molecules.
Enzyme-constrained modelling extends traditional metabolic models by limiting reaction rates based on enzyme capacities. This adds biological realism, captures proteome allocation trade-offs, and improves prediction accuracy for metabolic engineering. There are two different modelling frameworks for this type of modelling GECKO written in MATLAB and ECMpy. The former is impractical if you do not have the right license and the later is slow and has not been updated for a while. To this end, we provide this package to help the metabolic engineering community run these types of models with more ease.
Below are plots that illustrate the improvements of this type of modelling framework to accurately simulate cell metabolism
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| Original iML1515 model predictions for various carbon sources | EC iML1515 model predictions for various carbon sources |
We see that there are clear improvements:
Run the following code to install by travelling to the github folder and running:
pip install -e .
Important
You must manually download the brenda JSON on their website manually
The package parses the following databases: MetaNetX and Brenda and generates a large cache to enrich models. The later needs to be downloaded here and compressed:
from biopathopt.utils import write_compressed_json
import json
write_compressed_json(
json.load(open('brenda_2024_1.json')),
'biopathopt/flatfiles/json_brenda.json.gz',
)
To generate the cache run the following:
import biopathopt
data = biopathopt.Data()
data.refresh_cache()
Here is a non-exhaustive list of the features of this package.
The Read model notebook shows you how to pass any cobrapy model and enrich the annotations automatically
Enzyme-constrained notebook shows you how to build an enzyme constrained model and using an E.Coli model demonstrates the improvements in this modelling method vs the classical model by comparing it to actual measured data. (taken from ECMpy)
The FSEOF notebook (Flux Scanning based on Enforced Objective Flux) algorithm identifies metabolic reactions whose flux increases when production of a target compound is enforced, helping to pinpoint potential overexpression targets for strain engineering.
This package would not be possible without the work of ECMpy and FVSEOF where we I copied and altered a lot of the code. This is built relying heavily on CobraPy.
- Mao, Zhitao, et al. "ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models." Synthetic and systems biotechnology 9.3 (2024): 494-502.
- Ebrahim, Ali, et al. "COBRApy: constraints-based reconstruction and analysis for python." BMC systems biology 7.1 (2013): 74.
- Chen, Yu, et al. "Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0." Nature protocols 19.3 (2024): 629-667.