novoStoic2.0

novoStoic2.0: Integrated Pathway Design Tool with Thermodynamic Considerations and Enzyme Selection

Related work

1. 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
2. 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
3. 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
4. Chowdhury, A., Maranas, C. Designing overall stoichiometric conversions and intervening metabolic reactions. Sci Rep 5, 16009 (2015). https://doi.org/10.1038/srep16009

Requirements:

1. Rdkit
2. Tensorflow 2
3. Streamlit
4. Pandas
5. Numpy
6. Keras
7. scikit-learn
8. matplotlib
9. Pulp
10. CPLEX solver
11. ChemAxon's Marvin >= 5.11
12. 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.

creating conda environment

• 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

Steps to run streamlit interface locally

run the following on terminal after activating the conda environment streamlit run Home.py