novoStoic2.0: Integrated Pathway Design Tool with Thermodynamic Considerations and Enzyme Selection
- Wang L, Upadhyay V, Maranas CD (2021) dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design. PLOS Computational Biology 17(9): e1009448. https://doi.org/10.1371/journal.pcbi.1009448
- Upadhyay, V., Boorla, V. S., & Maranas, C. D. (2023). Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network. Metabolic engineering, 78, 171–182. https://doi.org/10.1016/j.ymben.2023.06.001
- Kumar, A., Wang, L., Ng, C.Y. et al. Pathway design using de novo steps through uncharted biochemical spaces. Nat Commun 9, 184 (2018). https://doi.org/10.1038/s41467-017-02362-x
- Chowdhury, A., Maranas, C. Designing overall stoichiometric conversions and intervening metabolic reactions. Sci Rep 5, 16009 (2015). https://doi.org/10.1038/srep16009
- Rdkit
- Tensorflow 2
- Streamlit
- Pandas
- Numpy
- Keras
- scikit-learn
- matplotlib
- Pulp
- CPLEX solver
- ChemAxon's Marvin >= 5.11
- Openbabel
Refer the file titled env.yaml for full list of depedencies
Remaining data can be taken from the scholarsphere psu link here
Due to constraints of file sizes on github, we have published all the data and codes on shcholar sphere psu.
- create a conda environment using:
conda create --prefix pathwaydesign - activate the created environment using:
conda activate pathwaydesign - install rdkit using:
pip install rdkit - install streamlit using:
pip install streamlit
run the following on terminal after activating the conda environment streamlit run Home.py