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Related Experiment Video

Updated: Jun 17, 2026

Lentiviral Vector Platform for the Efficient Delivery of Epigenome-editing Tools into Human Induced Pluripotent Stem Cell-derived Disease Models
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Lentiviral Vector Platform for the Efficient Delivery of Epigenome-editing Tools into Human Induced Pluripotent Stem Cell-derived Disease Models

Published on: March 29, 2019

Mechanistic model for HEK293 viral vector processes and its application in a digital shadow framework.

Kim B Kuchemüller1, Jonas Austerjost2, Rafael Machleid2

  • 1Sartorius, Corporate Research, August-Spindler-Straße 11, 37079, Göttingen, Germany. kim.kuchemueller@Sartorius.com.

Bioprocess and Biosystems Engineering
|June 16, 2026
PubMed
Summary

A new mechanistic model accurately predicts viral vector production across different cell types. This model, integrated with real-time monitoring, forms a Digital Shadow for enhanced bioprocessing and decision support.

Keywords:
Cross‑platform modelingData-driven modelingInline monitoringPerfusion process

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Area of Science:

  • Biotechnology
  • Process Engineering
  • Cell Biology

Background:

  • Viral vector production is crucial for gene therapy and vaccines.
  • Accurate modeling of cell growth and viral production is essential for process optimization.
  • Current models often lack cross-platform applicability and real-time integration.

Purpose of the Study:

  • To develop a transferable mechanistic model for HEK293 cell growth and viral vector production.
  • To integrate the model with real-time monitoring data to create a Digital Shadow.
  • To demonstrate the model's utility for enhanced process understanding and decision support in bioprocessing.

Main Methods:

  • Developed a mechanistic model incorporating viable, dead, and lysed cell states.
  • Calibrated the model for adenoviral processes and adapted it for adeno-associated and lentiviral cultures.
  • Combined the mechanistic model with an orthogonal projections to latent structures (OASYS) model using dielectric spectroscopy data to create a Digital Shadow.
  • Validated the Digital Shadow with adenoviral perfusion datasets.

Main Results:

  • The mechanistic model accurately reproduced distinct infection and transfection dynamics across different viral vectors by adjusting specific parameters.
  • The Digital Shadow provided real-time viable cell density, continuously updating the mechanistic simulation.
  • High agreement was observed between simulated and measured viable cell concentrations in adenoviral perfusion cultures.
  • The model demonstrated cross-platform applicability and effective real-time prediction of culture growth and infection timing.

Conclusions:

  • Transferable mechanistic models provide a robust foundation for hybrid digital systems in bioprocessing.
  • The developed Digital Shadow exemplifies connecting mechanistic models with inline monitoring for improved process understanding.
  • This approach paves the way for closed-loop Digital Twin applications in viral vector manufacturing.