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Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical
1Department of Biotechnology, Krunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India.
Biotechnology and Bioengineering
|July 17, 2023
Summary
Hybrid modeling combines mechanistic and data-driven approaches for bioprocess optimization. Challenges in hybrid model development include data complexity and lack of shared resources, hindering wider adoption.
Area of Science:
- Bioprocess Engineering
- Computational Modeling
- Systems Biology
Background:
- Hybrid modeling, integrating mechanistic and data-driven methods, is gaining traction in bioprocessing.
- Applications include experimental design, parameter identification, and process optimization.
- The development workflow is complex, involving experimental design, data processing, parameter estimation, and validation.
Purpose of the Study:
- To revisit the development of hybrid models in bioprocess systems.
- To discuss the selection of data-driven components, parameter identification, and quality assurance.
- To review challenges and propose corrective actions for hybrid model development.
Main Methods:
- Review of existing literature and methodologies for hybrid model development in bioprocesses.
- Analysis of data-driven component selection and state mapping strategies.
- Examination of parameter identification techniques and model quality assessment protocols.
Main Results:
- Hybrid model development involves flexible integration of various modules, posing challenges in identifying optimal structures.
- Key considerations include data-driven component selection, parameter estimation, and robust model validation.
- Lack of data and code sharing in repositories impedes the exploration and expansion of these tools.
Conclusions:
- Hybrid modeling offers significant potential for bioprocess advancement but faces development hurdles.
- Addressing challenges in data integration, parameterization, and model validation is crucial.
- Promoting data and code sharing is essential for accelerating the adoption and innovation of hybrid modeling in bioprocessing.
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