InSiliCHO: Modeling Cell Line Dynamics for Process Development
Modeling and simulation can be a powerful tool for bioprocess design and cell culture process development. We now present InSiliCHO, an open source tool for in silico simulation of Chinese hamster ovary (CHO) cell dynamics. InSiliCHO provides a robust foundation for advancements in process development cell culture, cell culture simulation, and more.
You can try InSiliCHO online in our app, or download the Python package with pip.
Why Simulate in Cell Culture Process Development?
Simulation tools like InSiliCHO empower researchers to:
- Understand and predict bioprocess behavior during scale-up
- Explore system dynamics qualitatively by testing control strategies quickly and cost-effectively
- Achieve deeper insights into process behaviors compared to traditional empirical approaches, such as response surfaces derived from cell line development experiment design
For example, InSiliCHO models demonstrate greater detail and accuracy in describing the relationship between initial glucose concentrations, process temperature, and cell density over 150 hours compared to standard Design of Experiments (DoE) techniques.
See here for a comparison of process dynamics as described by a typical response surface as obtained with DoE (top), versus a response surface from a process model (bottom).
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Core Foundations of InSiliCHO
InSiliCHO is based on the model described in Möller et al., 2020 [1], which captures critical parameters, such as:
- Cell density, glucose, glutamine, lactate, ammonia and monoclonal antibody (mAb) concentrations
- Sensitivity of growth and death rates to the various chemical species
- Metabolic shifts, such as lactate, depending on glucose concentration
- Ammonia’s inhibitory effects on antibody production
The dynamics of the model are based on known characteristics of CHO, and the default parameters used in the model were chosen to reconstruct experimental data with good fidelity, proving it as an invaluable tool for cell line development and process optimization.
[1] Möller, Johannes, Tanja Hernández Rodríguez, Jan Müller, Lukas Arndt, Kim B. Kuchemüller, Björn Frahm, Regine Eibl, Dieter Eibl, and Ralf Pörtner. “Model Uncertainty-Based Evaluation of Process Strategies during Scale-up of Biopharmaceutical Processes.” Computers & Chemical Engineering 134 (2020): 106693.
Try InSiliCHO in Your Experiments
You can explore InSiliCHO in our app by adjusting sliders for parameters and initial conditions, visualizing real-time process dynamics, and comparing experimental data. Or, download the Python module via pip to integrate it into your cell line process development workflow.
Want a challenge? Try optimizing initial conditions to achieve a final mAb concentration above 1000 mg/L.
Applications of InSiliCHO
- Design of Experiments (DoE): You can conduct in silico DoE campaigns to evaluate tradeoffs between cost and complexity in experimental designs. Then, optimize for cell line development experimental design to maximize efficiency and minimize trial-and-error in vitro.
- Model Fitting: InSiliCHO can be used to tailor model parameters to specific projects, enabling precise simulation of unique cell culture simulation setups and conditions.
- Active Learning: Using uncertainty estimates, you can identify high-impact experiments that improve model accuracy, streamlining the cell line development and process optimization workflows.
We’re continuously improving InSiliCHO with initiatives such as:
- Enhancing CHO cell models for scale-up scenarios
- Expanding simulation platforms to other cell lines
- Integrating advanced analytics into process development cell culture workflows
Contact Us to Get Started
If InSiliCHO aligns with your goals in cell culture process development, or you’re exploring innovative strategies for cell line development experiment design, we’d love to collaborate with you. Reach out to us at simulation@culturebiosciences.com to discuss how we can support your projects.
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