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Related Experiment Video

Updated: May 16, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

Modelling approaches for bio-manufacturing operations.

Sunil Chhatre¹

¹The Advanced Centre for Biochemical Engineering, University College London, Gower Street, London, WC1E 7JE, UK, sunil.chhatre@ucl.ac.uk.

Advances in Biochemical Engineering/Biotechnology

|November 28, 2012

Summary

Mathematical modeling and discrete event simulations accelerate bio-pharmaceutical process development. These in silico tools reduce costs and risks in drug commercialization by optimizing manufacturing strategies from early stages through scale-up.

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Last Updated: May 16, 2026

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Area of Science:

Biopharmaceutical process development
Computational modeling
In silico drug development

Background:

Drug commercialization requires faster, cost-effective methods.
Assessing manufacturing strategy risks is crucial.
Process simulations aid early-stage design and scale-up.

Purpose of the Study:

To summarize mathematical modeling and discrete event simulations for biopharmaceutical processes.
To explore financial and technical modeling for production, recovery, and purification.
To highlight the benefits of in silico approaches in drug development.

Main Methods:

Mathematical modeling of biopharmaceutical operations.
Discrete event simulations for process design and optimization.
In silico analysis of financial and technical aspects.

Main Results:

Simulations enable exploration of design spaces before extensive development.
In silico tools support optimization from early development to scale-up.
Accurate mathematical representation is key to realizing simulation benefits.

Conclusions:

Mathematical modeling and discrete event simulations are vital for efficient biopharmaceutical process design.
These computational tools offer significant financial savings and risk reduction.
In silico approaches streamline drug commercialization from development through plant operation.