Constellab Bioprocess

The Constellab Bioprocess  is a Streamlit-based application designed for the analysis and visualization of fermentation and bioprocess data generated from experiments. Its primary goal is to simplify and streamline the processing of fermentation data by providing an intuitive, interactive interface for uploading, processing, and exploring experimental results.

This application supports the analysis of QC0 and fermentation plate reader data, enabling users to efficiently interpret bioprocess performance across multiple fermentors and batches. The dashboard automatically handles data ingestion and processing through loading task, transforming raw experimental files into structured, analysis-ready datasets.

Key Features

• File Upload Interface : Upload required input files, including CSV and ZIP formats, directly through the app.
• Automated Data Processing : Seamless processing of fermentation data using standardized workflows.
• Data Quality Assessment : Automatic identification of missing values, incomplete datasets, and potential data quality issues.
• Interactive Sample Selection : Select specific samples, fermentors, or conditions for focused analysis.
• Data Visualization : Explore data through interactive charts, including time series, box plots, histograms, and scatter plots.
• Statistical Analysis : Access descriptive statistics and summary metrics to support data interpretation.
• Batch and Condition Comparison : Compare multiple fermentation batches and fermentor conditions to assess process variability and performance.
• Critical parameter identification : Identification of critical bioprocess parameter using classical and advanced machine learning tools (e.g. Causality inference)

Related works

This work is related to the brick Design of Experiment. This brick implements advanced machine learning tools for causality inference.

🔗 Brick Design of Experiment :  https://constellab.community/bricks/gws_design_of_experiments/latest

🔗 Causality inference : https://constellab.community/bricks/gws_design_of_experiments/latest/doc/use-cases/causal-inference/d344106b-96fb-4526-8879-9e2007369aba

References

The data used in this application comes from this public dataset :

Tovilla-Coutino, Maria de Lourdes; Passot, Stéphanie; Trelea, Ioan-Cristian; Jérôme Delettre; Ropers, Marie-Hélène; Gohon, Yann; Fonseca, Fernanda, 2022, "Effect of fermentation pH, temperature, and harvest on cell growth and functional properties of frozen and freeze-dried Lactobacillus delbrueckii subsp. bulgaricus CFL1", https://doi.org/10.15454/FZHIE0, Recherche Data Gouv, V1