About the Bioprocessing and Biomanufacturing Simulator

Developed by: Magnus Stefansson, MBA, Ph.D.
Rabb School of Continuing Studies, Division of Graduate Professional Studies, Brandeis University, 475 Old S St, Waltham, MA 02453

🔬 Overview

Welcome to the Bioprocessing and Biomanufacturing Simulator, an interactive platform designed to bring the core principles of bioprocess engineering to life. This tool provides a dynamic environment for students and educators to explore the complexities of bioreactor operations without the need for a physical lab.

The simulator is built to enhance understanding of how different operational modes and parameters impact the production of biotechnological products.

🎯 Purpose

This application was created exclusively for academic and training purposes. It serves as a virtual laboratory for students in graduate-level biotechnology programs, allowing them to:

Simulate and visualize complex bioprocessing dynamics in real-time.
Understand the trade-offs between different operational strategies.
Develop an intuition for process optimization and parameter sensitivity.
Analyze simulated data to draw meaningful conclusions about process performance.

✨ Core Features

The simulator is packed with features to facilitate a comprehensive learning experience:

Four Simulation Modes: Explore and compare Batch, Fed-Batch, Repeated Fed-Batch, and Bleed-Perfusion bioreactor operations.
Real-Time Parameter Control: Instantly see the impact of adjusting key parameters like growth rates, substrate concentrations, and yield coefficients.
Interactive Visualizations: Dynamic plots generated with Plotly allow for an in-depth look at how biomass, substrate, and product concentrations change over time.
Scenario Management: Load pre-configured scenarios for common bioprocessing tasks or build, save, and share your own custom scenarios.
Productivity Analysis: Automatically calculate key performance indicators (KPIs) such as volumetric productivity, total yield, and substrate conversion efficiency.
Assignment Mode: Instructors can lock parameters and provide guided questions to create structured learning assignments.
Data Export: Export simulation results to a CSV file for further analysis in other software.

🛠️ Technology

This application is built with a modern, open-source Python stack:

Streamlit: For creating the interactive web application interface.
Plotly: For generating rich, interactive data visualizations.
NumPy & Pandas: For numerical computations and data management.
SciPy: For the ordinary differential equation (ODE) solver that powers the kinetics engine.