COMO: Constraint-based Optimization of Metabolic Objectives

Setting up COMO

Go to COMO's documentation page for full installation and operation instructions or use one of the Quick Start options

COMO Documentation Page

Quick Start Options

Docker Quick Start

This installation method does require docker

• Install Docker
• Pull our latest container
  • docker pull ghcr.io/helikarlab/como:latest
• Run the container
  • docker run -p 8888:8888 ghcr.io/helikarlab/como:latest

NOTE: The defualt installation method here does not allow for saving your work or utilizing the Gurobi solver. If you would like either (or both) of these features, please visit our documentation for more details

Conda Quick Start

This installation method does not require docker

• Install Conda
  • Preferably, install mamba instead. Mamba is much faster than Conda and offers the same features
• Clone this repository
  • git clone https://github.com/HelikarLab/COMO.git
• Change directories into the newly cloned repository
  • cd COMO
• Create a new conda environment
  • conda env create -f environment.yaml, OR
  • mamba env create -f environment.yaml
• Activate the new environment
  • conda activate como, OR
  • mamba activate como
• IMPORTANT: Install our modified version of zFPKM to allow for filtering insignificant local maxima during RNA-seq processing
  • R -e "devtools::install_github('babessell1/zFPKM')"
• Start the notebook server
  • cd main && jupyter notebook (for "retro" jupyter notebook look and feel), OR
  • cd main && jupyter lab (for the newer jupyter lab look and feel)

This will open a web browser with the Jupyter Notebook/Lab interface. From here, you can open the COMO.ipynb notebook to get started

NOTE: This installation method will allow for saving your work and utilizing the Gurobi solver. If you would still like more details about this installation method, please visit our documentation

Flow Charts

Please follow this link for flow charts

Resources

Resources for packages used in COMO and other useful links, please see here

Citation

If you use this work, please cite it with the following

Brandt Bessell, Josh Loecker, Zhongyuan Zhao, Sara Sadat Aghamiri, Sabyasachi Mohanty, Rada Amin, Tomáš Helikar, Bhanwar Lal Puniya, COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery, Briefings in Bioinformatics, Volume 24, Issue 6, November 2023, bbad387, https://doi.org/10.1093/bib/bbad387