BioOpti was created to help optimizing biochemical workflows, from culture media formulation to enzymatic reaction simulations. Designed for researchers, students, and bioprocess enthusiasts, BioOpti makes it simple to model, predict, and refine experimental setups.
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📈 Culture Media Optimization:
Query the BACDive database to retrieve documented culture media for a given bacterial strain (by taxon name or culture collection number). Display each medium’s name, composition, reported growth performance, and associated metadata such as optimal temperature. -
🔬 Enzymatic Reaction Simulation:
Predict enzyme-catalyzed reaction rates under varying conditions (substrate concentration, pH, temperature, inhibitors). Easily simulate and optimize reactions based on Vmax, Km, inhibition type, and environmental factors.
- 🔄 Clone the Repository
First, choose a location on your computer where you want to store the project. For example, you can use your Documents folder.
Open your terminal and run:
cd ~/Documents # or any folder where you want to store the project
git clone https://github.com/Clarabrgnz/BioOpti.git
cd BioOptiThis will create a folder named BioOpti containing all the project files.
- 🐍 Create and Activate a Python Environment
Then, create a new environment (feel free to use a different environment name if you prefer):
conda create -n bioopti python=3.10
conda activate bioopti- 🧫 Install the Package
Make sure you are inside the BioOpti folder and run:
pip install .- 📓 Install JupyterLab
In order to use the Jupyter Notebook for demos or development:
pip install jupyterlabIf you're developing BioOpti or contributing to it, install in Editable Mode with Extra Dependencies
pip install -e ".[test,doc]"To run the test suite and check coverage:
pip install tox
toxThis example shows how to query the BACDive database for a known strain, and retrieve the list of documented culture media with associated growth performance.
from bioopti.media_optimizer import run
# Query BACDive for a bacterial strain
run("Pseudomonas aeruginosa")This example shows how to simulate the rate of an enzyme-catalyzed reaction using known kinetic parameters. Provide experimental conditions such as substrate concentration, Vmax, Km, pH, temperature, and optional inhibitor information, and the function simulate_reaction_rate will calculate the expected reaction rate (µmol/min) under these specific conditions.
from bioopti.reaction_simulator import simulate_reaction_rate
# Simulate an enzyme-catalyzed reaction
rate = simulate_reaction_rate(
substrate_conc=2.5, # [S] in mM
vmax=1.8, # Vmax in µmol/min
km=0.5, # Km in mM
pH=6.8, # current pH
temp=35.0, # current temperature °C
optimal_pH=7.0, # enzyme's optimal pH
optimal_temp=37.0, # enzyme's optimal temperature
pH_sigma=1.0, # pH tolerance
temp_sigma=5.0, # temperature tolerance
inhibitor_conc=0.1, # inhibitor concentration [I] in mM
ki=0.05 # inhibition constant Ki in mM
)
print(f"Simulated reaction rate: {rate:.2f} µmol/min")- Clara Bergonzi — Package Infrastructure, Reaction Simulation Module
- Pietre Bonaldi — Media Optimization Module, Tests and Coverage
- Marc Tang Vidale — Media Optimization Module, Notebook Development Contribution
- William Brian Pellassy — Reaction Simulation Module, Notebook Development Contribution
This project is licensed under the MIT License.
You are free to use, modify, and distribute this software with appropriate attribution.
See the LICENSE file for full details.
🚀 Happy optimizing with BioOpti 🚀